Most businesses today acknowledge the transformative power of AI but get bogged down in the complexities of implementation.Recently on Behind the Growth, I had the pleasure of interviewing Inanc Cakiroglu, Director of AI and Analytics, to celebrate his impressive journey in the field. Inanc has over 25 years of experience in ICT and has witnessed firsthand the evolution of AI from a niche technology to a powerful force shaping industries worldwide.In this blog post, we’ll distill Inanc’s insights into a practical, three-step guide to help you identify and solve business problems using AI.Define Before You Design: Start with the Business Problem Inanc stresses the need for a clear and well-defined problem statement as the foundation for any successful AI project. He calls this approach “first principle thinking.”In his words, “It is basically getting a business problem, business case, dividing it into very smaller pieces into various smaller, grander components, and then starting with the very basic approach, solving these basic pieces and creating complex solutions.”He observed that companies are getting caught up in the excitement of AI and jumping into solutions without first identifying the core problem they’re trying to solve.“Usually, everybody thinks about which application area they should apply AI, which kind of AI technologies they should adopt, which kind of AI solutions or the data or analytic solutions they should be looking at,” Inanc notes. “But I think the technology shouldn’t be the starting point. The starting point should be a use case.”Think of it like building a house. Just like you wouldn’t start constructing walls without a blueprint, similarly, diving headfirst into AI solutions without a clear understanding of your core business problem is a recipe for wasted resources and underwhelming results.Instead, take the time to break down complex challenges into smaller, actionable components. For example, instead of broadly stating “We need to improve customer experience,” define a specific problem like “Reduce customer churn by 15%” or “Increase online sales conversions by 10%.”This laser focus, driven by a clearly defined use case, will guide you towards the most relevant and impactful AI solutions.Matching the Solution to the Challenge: Exploring AI Solutions Once you’ve defined your business problem, the next step is to explore potential AI solutions. Inanc emphasizes that technology should not be the starting point but rather a means to an end.To guide this process, he outlines a simple yet powerful framework for AI solution creation, focusing on what he calls “filter actions”:“All the solutions, all the technologies are coming up with filter actions. You either classify something, you filter out something, you predict something, or you match something. So you should be asking that, okay, you have a problem here, you have a challenge here. How would you like to solve this challenge? You would like to classify something, match something, then predict something, or you would like to understand something, getting insight from something and using this insight to provide a better experience.”Let’s break down each category and see how it translates to practical AI applications:1. Classification This involves categorizing data into predefined groups. For example, an AI-powered chatbot can classify incoming customer queries based on topic or urgency, routing them to the appropriate department or agent.2. Prediction This uses historical data to forecast future trends or outcomes. Businesses can leverage predictive AI to anticipate customer churn, identify potential fraud, or optimize inventory levels.3. Matching This focuses on finding similarities or connections between data points. AI-powered recommendation engines, for example, use matching to suggest products or services based on a customer’s past purchases or browsing history.4. Understanding/Insight Generation This involves extracting meaningful insights from complex data sets. Businesses can use AI to analyze customer sentiment, identify market trends, or gain a deeper understanding of their operations.By understanding which “filter action” aligns with your desired outcome, you can narrow down the vast array of AI solutions and choose the one that best fits your needs.The Fuel For Success: The Crucial Role of Data While creating your use cases and thinking about the technology, you should also think about your data process, data operation, and how you can come up with this data.Inanc is unequivocal about the importance of data in AI: “Without good established pipelines, you cannot achieve something.” Just as a car can’t run without fuel, AI algorithms can’t deliver meaningful insights or drive effective actions without the right data.There are three crucial aspects to data readiness that businesses need to address:1. Data Quality The accuracy, completeness, and consistency of your data directly impact the performance of your AI models. Garbage in, garbage out, as they say. Therefore, it’s essential to assess the quality of your existing data and identify any gaps or inconsistencies that need to be addressed.2. Data Pipelines Having established pipelines ensures that data flows seamlessly from various sources to your AI systems. This involves processes for data collection, cleaning, transformation, and integration. Robust data pipelines are essential for delivering timely and reliable insights.3. Business Process Transformation In some cases, existing business processes might not generate the data needed for your AI initiatives. In such instances, consider a business process transformation to modify your workflows and systems to capture the relevant data.Inanc cautions against viewing data preparation as a one-time task: “So in the meantime, while you are creating your use cases and thinking about technology, you should also think about your data process, your data operation, how you can come up with this data.”Effective AI implementation requires a continuous focus on data quality, pipeline management, and adapting business processes as needed.Takeaway To unlock AI’s full potential for your business, remember the three essential steps:Define your business problem with laser focus Identify the right AI solution to address that specific challenge Ensure your data is ready to fuel the process By taking this structured approach, you can move beyond the hype and harness the power of AI to drive tangible results.If you found these insights valuable, be sure to tune into the full episode of Behind the Growth for a conversation with Inanc Cakiroglu you don’t want to miss! You’ll gain even deeper insights into the evolving AI landscape, the potential of emerging technologies, and practical advice for leveraging AI to transform your business.Find it here:Simple & Clear Business Problems for Effective AI
Data, Decisions, and AI: A 3-Step Roadmap to Solve Business Problems
The global pharmaceutical industry is undergoing a profound transformation, fueled by the unprecedented power of artificial intelligence (AI).I recently had the pleasure of interviewing AI expert Nitesh Soni for an episode of Behind the Growth to discuss how this transformation is unfolding, from drug discovery to patient care.Nitesh is a recognized leader in data science and AI with experience across various sectors, including finance, pharmaceuticals, and academia. He’s known for his impactful business engagements and has notably contributed to projects that have reached global acclaim. He’s currently helping Sanofi, one of the world’s leading pharmaceutical companies, move forward by leveraging data and AI at scale.In this article, we’ll explore the key takeaways from my conversation with Nitesh, uncovering how AI is transforming global pharma and offering a glimpse into the future of this exciting field.Drug Discovery and Development: Accelerating the Future of Medicine Nitesh emphasizes that AI is playing a major role in accelerating drug discovery and drug development. He points out that “[Four or five] years back, these things were taking way longer because of limited AI computer or limited algorithms.”But advancements in AI technology are rapidly changing the game.AI’s Role in Target Identification and Drug Repurposing AI algorithms are now being used to expedite target identification — the process of pinpointing specific molecules that could be targeted by new drugs. By analyzing vast datasets of genetic information, chemical structures, and clinical trial data, AI can identify promising drug candidates far more efficiently than traditional methods.Furthermore, AI is proving highly effective in drug repurposing — finding new uses for existing medications. AI algorithms can sift through mountains of data to identify potential new applications for existing drugs.Nitesh notes that this approach is gaining traction in the industry as it can significantly shorten development timelines and reduce the costs associated with bringing new treatments to market.Personalized Medicine: Tailoring Treatments with AI Insights Nitesh also highlights the growing importance of personalized medicine, an approach where treatments are tailored to individual patients based on their unique genetic makeup, lifestyle, and other factors.AI is a fundamental enabler of personalized medicine, as it can analyze complex patient data, including genetic profiles and medical histories, to identify the most effective therapies for each individual.He says, “[We should focus on] creating a more personalized experience for our patients and healthcare providers. It’s more like our LinkedIn or Facebook where you start getting your personalized recommendations. That’s what we are doing in the commercial area — basically, how we can build more next best actions, more targeted information for our customers and patients.”Revolutionizing Manufacturing and Supply Chain Management Beyond drug discovery and development, AI is making a significant impact on pharmaceutical manufacturing and supply chain management. Nitesh is particularly excited about the potential for AI to streamline these critical processes, making them more efficient, reliable, and responsive to global demand.Optimizing Manufacturing Processes with AI Nitesh highlights AI’s role in automating and optimizing various aspects of pharmaceutical production.He states, “How we can standardize and streamline the manufacturing process by leveraging AI, how we can automate a lot of things in that space, how we can optimize for example, yield optimization is important because that impacts your supply chain and demand.”AI-powered systems can monitor production lines in real-time, detecting anomalies and potential issues before they escalate. Predictive maintenance, powered by AI algorithms, helps manufacturers anticipate equipment failures, reducing downtime and ensuring smooth operations.AI can also play a role in quality control, analyzing product characteristics to identify defects and maintain high standards.Intelligent Supply Chains: Meeting Global Demand AI’s impact extends beyond the factory floor, reaching into the complex networks that distribute pharmaceuticals globally.By leveraging AI’s ability to analyze vast amounts of data, pharmaceutical companies can create more intelligent supply chains. AI algorithms can track products in real-time, providing valuable insights into inventory levels, transportation routes, and potential delays.This real-time visibility enables proactive decision-making, allowing companies to adjust production schedules, optimize distribution routes, and ensure that life-saving medications reach patients when and where they need them.Transforming Commercial Strategies and Patient Engagement AI’s impact on the pharmaceutical industry also reaches into the realm of commercial strategies and patient engagement. Nitesh highlights how AI is transforming pharmaceutical companies’ interaction with healthcare providers and patients, leading to more personalized and effective communication.AI-Powered Marketing and Sales in Pharma AI is revolutionizing how pharmaceutical companies market and sell their products. Nitesh, who leads the commercial pillar at Sanofi, explains that AI is being used to empower sales and marketing teams.AI algorithms can analyze vast amounts of data on healthcare professionals, including their prescribing patterns, areas of expertise, and preferred communication channels. This allows companies to target their marketing efforts more effectively, delivering relevant information to the right physicians at the right time.AI-powered tools can also help sales representatives personalize their interactions with physicians, providing tailored information and recommendations.Enhancing Patient Engagement and Adherence AI is also playing a growing role in enhancing patient engagement and medication adherence.By leveraging AI-powered communication tools, pharmaceutical companies can provide patients with personalized information, support, and reminders. This aligns with the industry’s broader focus on creating a more personalized experience for patients, as Nitesh notes.AI-powered chatbots can answer patients’ questions about their medications, potential side effects, and treatment plans. These chatbots can provide 24/7 support, improving accessibility and convenience for patients.Additionally, AI can be used to develop personalized medication reminder systems, helping patients stay on track with their treatment regimens and improving health outcomes.The Future: Compound AI and Responsible Development As AI continues to evolve, its impact on the pharmaceutical industry is only going to grow more profound. Nitesh highlights two key trends shaping the future of AI in pharma: the rise of compound AI systems and the increasing importance of responsible AI development.The Rise of Compound AI Systems Nitesh believes that the future of AI in pharma lies in “the compound AI system, which is a combination of your traditional AI plus generative AI.”He suggests that combining traditional AI with generative AI will unlock new possibilities, allowing companies to tackle more complex challenges and develop even more innovative solutions.Generative AI, a relatively new field, focuses on creating new content, such as text, images, and even code. Integrating generative AI into traditional AI systems could lead to breakthroughs in areas such as drug design, where AI could generate novel drug candidates, and personalized medicine, where AI could create customized treatment plans tailored to individual patients.Ethical Considerations and Responsible AI in Pharma Always pay strong attention to data quality and data governance, because without that, you cannot build trustworthy AI.As AI becomes more integrated into pharmaceutical operations, Nitesh emphasizes the crucial importance of ethical considerations and responsible AI development.He states, “Putting better internal control to build safe and responsible AI. And the last thing I think we did not touch that strongly is always paying strong attention to data quality and data governance because without that, you cannot build your trustworthy AI.”Ensuring data privacy and security is paramount, especially when dealing with sensitive patient information. Mitigating bias in AI algorithms is crucial to prevent unfair or discriminatory outcomes in areas such as drug development and patient selection for clinical trials.Transparency in AI development is also essential, building trust in these powerful technologies and allowing for greater accountability.Takeaway The conversation with Nitesh Soni reveals that AI is a fundamental shift in how the pharmaceutical industry operates and serves patients worldwide. From accelerating drug discovery to personalizing treatment plans, AI is poised to revolutionize every aspect of global pharma.If you found these trends insightful, be sure to tune into the full episode of Behind the Growth for a conversation you do not want to miss! Nitesh shares even more valuable insights into the future of AI in pharma and offers practical advice for companies looking to leverage these transformative technologies.Find it here:The Impact of AI on Global Pharma
AI in Pharma: Transforming Drug Discovery, Development, and Patient Care
Building a successful career in any field can be challenging, but the rapid growth and constant innovation within FinTech can make it seem particularly daunting.There’s no shortage of advice for those looking to break into the industry, but not all of it is helpful. Misconceptions and “career myths” abound, often leading aspiring professionals down the wrong path.In a recent episode of Behind the Growth, I had the pleasure of interviewing Suhanniyah Carpenter, Sr. Director of Consumer Banking at Royal Bank of Canada, to celebrate her extensive journey and gain her valuable insights on navigating this complex landscape. As a seasoned leader in consumer banking with extensive experience in both technology and finance, Suhanniyah offers a wealth of knowledge on overcoming challenges, embracing innovation, and achieving lasting success in fintech.In this article, I draw from our conversation to debunk three common myths that often hold aspiring fintech professionals back. These learnings will challenge several common assumptions and help you gain a clearer understanding of what it truly takes to thrive in this dynamic field.Myth #1: Maternity Leave Will Derail Your Career Progression One of the most persistent myths, particularly for women in tech, is the belief that taking maternity leave will inevitably set back their careers. In a field like fintech, where innovation is constant, many fear that stepping away, even temporarily, will make them irrelevant or hinder their advancement opportunities.Suhanniyah directly addressed this concern during our conversation, challenging the assumption that maternity leave equals career stagnation. She shared, “Here’s myth buster number one. A lot of women come to me and say, ‘If I’m at the early stages of my career, is this really the right time to step back and start a family and go on maternity leave? Is this going to hinder my career progression?’ Just speaking from my own experience, it did not– Right?”Suhanniyah’s experience provides a powerful counter-example to this pervasive myth as she received a job offer while on maternity leave. However, she also emphasized the importance of maintaining a professional presence during this time. “As long as you keep your network intact, as long as you keep your LinkedIn profile intact, and you really focus on what you bring in your career is kind of visible to folks, that’s what really matters.”By sharing her personal experience and offering practical advice, Suhanniyah encourages women in fintech to confidently navigate the transition into parenthood without sacrificing their career ambitions.Myth #2: Career Progression Requires Constant Job-Hopping Another common misconception in fintech is the idea that climbing the career ladder requires constant job-hopping. Many believe that switching companies every few years is the only way to gain new experiences, increase their salary, and advance to higher positions.Suhanniyah challenges this assumption, demonstrating that you can grow internally within your organization. Her journey at RBC demonstrates the power of leveraging internal networks, building strong relationships with managers, and proactively expressing career goals.She says, “I think that’s something that is important to stress– If you’re passionate and you’d like to explore new opportunities, have that really good conversation with your manager to say, ‘I’d like to explore something different. I’d like to help in this space…’ Using your network and your manager as that partner in your career progression really helps open doors that you didn’t even know were available.”Her advice highlights the importance of viewing managers as partners in career development, openly discussing aspirations, and seeking their support in exploring new opportunities within the company. By fostering these relationships and actively pursuing growth, she shows that it’s possible to achieve significant career advancement without the constant need to switch employers.Myth #3: Remote Work Limits Career Advancement Potential The rise of remote work has brought about new anxieties, particularly the concern that working outside a traditional office setting can limit career growth. Some worry that being physically distanced from their colleagues and managers will lead to fewer opportunities, less visibility, and ultimately, a slower career trajectory.However, Suhanniyah’s experience challenges this notion. She revealed that a significant portion of her career progression at RBC actually happened while working remotely. She stated, “I’ve been with RBC for four years and I’ve had four different roles since I joined in 2020, and most of the changes in career progression happened when I was fully remote.”Her experience transitioning to a new role at RBC at the onset of the pandemic, coupled with her subsequent career progression within the company while working remotely, clearly demonstrates that physical presence isn’t a prerequisite for advancement.Instead, she emphasizes the importance of being proactive, engaged, and communicative, regardless of location. By focusing on delivering results, building strong relationships, and seeking out new challenges, remote workers can still demonstrate their value and unlock opportunities for growth.Three Strategies for Career Progression in Fintech Most of my managers up until very recently were male managers. And those interactions were really important to me. They helped me understand when i was kind of overthinking some things.Throughout our conversation, Suhanniyah emphasized several key factors that have contributed to her success and that she believes are essential for anyone looking to build a thriving career in fintech.First and foremost, she highlights the power of meaningful connections. “A lot of my career progression was really about meaningful connections,” she shared. “Having people that whether they’re within your immediate team or you’ve worked with them in the past — those connections are going to be able to carve out roles, pathways into career opportunities that you haven’t even been exposed to.” Building a strong network of mentors, colleagues, and industry contacts can open doors to new possibilities and provide invaluable guidance along the way.In addition to networking, Suhanniyah stresses the importance of focusing on skills and knowledge, particularly by “grounding yourself in the technology” and “what you bring to the table.” In a field like fintech, continuous learning is crucial. Professionals need to stay updated on the latest technologies, trends, and best practices to remain relevant and competitive.Finally, Suhanniyah underscores the importance of resilience, advocating for a “focus on the task at hand” even when faced with bias or challenges. This resilience, coupled with a commitment to continuous learning, helps professionals adapt to the changing demands of the fintech industry.Takeaway A successful fintech career is built on a foundation of meaningful connections, continuous learning, and unwavering resilience. By embracing these principles, aspiring professionals can navigate challenges, unlock new opportunities, and chart a course toward a fulfilling and impactful career in this dynamic industry.If you found these insights valuable, be sure to tune into the full episode of Behind the Growth for a conversation you won’t want to miss! Suhanniyah shares even more wisdom and practical advice for those looking to thrive in the world of fintech.Find it here:Career Growth and Innovation in Fintech
Building a Successful Fintech Career: 3 Myths Debunked
Consumers are increasingly seeking out companies that align with their values, and the financial sector is no exception. This growing demand for purpose-driven businesses is reshaping the very definition of success.On a recent episode of Behind the Growth, I had the pleasure of interviewing Mahima Poddar, Group Head of Equitable Bank, to celebrate her journey and explore how her leadership is driving positive change in Canadian banking. Mahima has played a pivotal role in transforming Equitable Bank into a shining example of purpose-led leadership.In this article, we’ll delve into key takeaways from our conversation, uncovering how Equitable Bank’s commitment to enriching people’s lives is shaping their strategy, culture, and customer experience. You’ll discover how a purpose-driven approach can create tangible benefits, not only for customers but for employees and communities as well.Defining Success: Beyond Profits Mahima eloquently captured Equitable Bank’s philosophy on success: “Our vision is the center of what we do, and that’s to drive change in Canadian banking to enrich people’s lives.”The key elements of this philosophy are:Driving Positive Change in the Industry: Equitable Bank is committed to challenging the status quo and pushing the boundaries of what’s possible in Canadian banking. They are not content with simply replicating existing models; they strive to create a better, more customer-centric experience. Enriching the Lives of Their Customers: This is the heart of their purpose. Equitable Bank believes in empowering their customers to achieve their financial goals and improve their overall well-being. They do this by offering fair and transparent products and services that prioritize customer value. Putting Customers at the Center of Their Operations: Every decision, from product development to customer service, is made with the customer in mind. This customer-centric approach ensures that Equitable Bank remains focused on meeting the evolving needs of the people they serve. This purpose-driven approach stands in stark contrast to the profit-centric model that often dominates traditional banking institutions.Mahima observed that at many big banks, “There’s so much pressure on hitting the analyst consensus … that there’s actually very little opportunity to innovate or push the envelope.” This relentless pursuit of short-term financial targets can leave little room for genuine customer-centricity and limit a bank’s capacity to innovate and adapt to changing needs.Building a Purpose-Driven Culture Creating a truly purpose-driven organization requires more than just words. It demands a culture that embodies those values in every aspect of the business. Mahima highlighted how Equitable Bank cultivates this culture through a unique approach to leadership and employee empowerment.She explained that at Equitable Bank, “Every team member acts like an owner of the bank.” This simple but powerful concept creates a profound shift in mindset. “It takes out all of the noise of ‘Am I doing the right thing for these different stakeholder groups?'” Mahima elaborated.When everyone operates with an owner mentality, decision-making becomes clearer and more aligned with the company’s core purpose. “Because again, ultimately, you’re 100 percent owner of this bank, and it’s likely the right decision across employees, across stakeholders, across regulators, because you care about the long-term sustainability of the organization,” Mahima emphasized.This “ownership mentality” is a key driver of Equitable Bank’s success. It encourages long-term thinking and sustainable practices. Employees are motivated to consider the impact of their decisions not just on the next quarter’s earnings, but on the lasting health and viability of the organization.Tangible Benefits of Purpose-Led Leadership We are really focused on delightful customer experience. and so we're putting Gen AI tools in front of our customer care agents so that they get prompted with an answer or a solution that might make sense for the customer.Equitable Bank’s commitment to purpose-led leadership translates into tangible benefits for their customers. One of the most compelling indicators of this success is their exceptional Net Promoter Score (NPS).Mahima highlighted their impressive customer satisfaction levels: “Our ultimate goal is for customer satisfaction … Our goal is not just to be best in Canada … but to have customer satisfaction that’s best in class globally.” This ambition to be a global leader in customer satisfaction demonstrates the depth of Equitable Bank’s commitment to exceeding expectations.This dedication to putting the customer first is evident in their product offerings. Mahima emphasized that “Everything that we’re doing is about giving more benefit to the customer.” Equitable Bank demonstrates this commitment through products designed to provide genuine value and empower customers.Examples include their no-fee banking options and high-interest savings accounts. These offerings directly address common customer pain points and prioritize their financial well-being. By developing solutions that are demonstrably better than what already exists, Equitable Bank has solidified its position as a leader in purpose-driven banking.Takeaway Mahima’s insights offer a compelling blueprint for leaders looking to build businesses that go beyond profits to create genuine value for their customers. By embracing a purpose-driven approach, fostering a culture of ownership, and prioritizing long-term impact, companies can create a more sustainable and fulfilling future, not just for themselves, but for the people they serve. Equitable Bank stands as a testament to the power of this philosophy in action.If you found these insights inspiring, be sure to tune into the full episode of “Behind the Growth” for a conversation you won’t want to miss! Hear directly from Mahima Poddar as she delves deeper into Equitable Bank’s journey, sharing her valuable perspectives on leadership, innovation, and the future of banking.Find it here:Purpose-Led Leadership and its Impact
From Vision to Value: The Impact of Purpose-Driven Leadership
In today’s fast-paced work environment, organizations are flooded with vast amounts of information. Whether it’s operating procedures, internal documents, or various knowledge hubs, finding the right information when needed is often challenging. Employees may spend valuable time searching through multiple sources or asking colleagues for guidance, especially during onboarding or when dealing with complex tasks.While Generative AI (GenAI) is incredibly effective for general queries, it struggles to provide accurate answers to questions rooted in a company’s specific operational knowledge. Without access to internal data and processes, GenAI lacks the context to deliver relevant information that addresses an organization’s unique needs.Additionally, traditional search methods lack the ability to contextualize or summarize information effectively, making it harder for users to find what they need quickly. Fine-tuning large language models to fit an organization’s specific needs is often time-consuming and resource-intensive, and in rapidly changing environments, it becomes inefficient.Moreover, experimenting with and refining prompting techniques further adds to this resource burden.How AI Is Transforming Knowledge Management AI can revolutionize how organizations manage and retrieve knowledge. By leveraging advanced AI techniques, companies can overcome the inefficiencies of traditional search and create a knowledge management system that is faster, more accurate, and highly customized to their operational needs. Here’s how AI addresses these challenges:Contextualized Search AI can process and analyze internal documents, providing search results that are relevant to the user’s specific query. This eliminates the need for manual sifting through large databases or document repositories.Fine-Tuned GenAI Models AI models can be fine-tuned to understand industry-specific data, enabling them to provide accurate and contextually relevant answers that go beyond general knowledge.Multi-Format Document Processing AI systems can handle various document formats, including Excel, Word, and PDFs, allowing them to search across multiple types of data and even provide insights at a granular level, such as table data in spreadsheets.Speech-to-Text and Text-to-Speech Integration AI tools with voice search capabilities allow users to interact with the system using natural speech, making it even more accessible and user-friendly.API Integration For more complex use cases, AI-powered systems can make API calls to gather or update data in real time, ensuring users have access to the most up-to-date information.Enter: The AI-powered Knowledge Management System Our AI-powered Knowledge Management System addresses the key challenges organizations face by using advanced techniques like the RAG (Retrieval-Augmented Generation) approach. Here’s how this solution works:Leveraging Internal Data Using the RAG approach, our system integrates internal company data, enabling it to augment GenAI responses with specific, relevant information. This provides more precise answers and eliminates the knowledge gap that generic AI systems often have.Vector Database for Contextual Search Our system runs internal documents through a vector database, which allows for fast and highly accurate retrieval of information based on context and relevance. This makes the process of finding the right information more efficient and less time-consuming.Support for Multiple Document Types Whether it’s a PDF, Word document, or Excel file, our system can process and analyze different formats, making it easy to retrieve and work with data from multiple sources. It can even analyze data at the table level for more detailed insights.Voice Search Capabilities With integrated speech-to-text and text-to-speech functionalities, users can interact with the system through voice commands, making it particularly useful in hands-free or on-the-go scenarios.Tailored to Your Industry Our solution can be fine-tuned with industry-specific data, ensuring that the output is aligned with your organization’s unique operational landscape. This customization makes the system highly versatile and adaptable.Benefits of Our Knowledge Management System Faster, More Accurate Information Retrieval AI significantly speeds up the process of finding relevant information by searching based on user intent and the context of the query, rather than just matching keywords. According to Gartner, companies that implement AI-driven knowledge management systems see a 25-30% improvement in information retrieval speed.Tailored Knowledge Solutions With AI fine-tuned for your industry, the system delivers highly relevant and accurate information. According to Forrester, AI solutions customized to an organization’s needs lead to 30-50% more accurate responses compared to general-purpose models.Multi-Format Document Processing Handling different document types with AI not only streamlines workflows but also improves accuracy in data extraction. A study by Capgemini shows that companies utilizing AI for multi-format document analysis saw a 40% reduction in manual effort.Voice Search for Convenience The integration of voice search capabilities allows for easier access to information. McKinsey reports that organizations incorporating voice-enabled AI saw a 15-20% boost in user engagement, as employees found it more intuitive to use.Takeaway AI-driven knowledge management is a game changer for organizations looking to improve efficiency in handling internal queries. By fine-tuning AI with internal data and implementing contextualized search techniques, companies can expect to reduce the time spent searching for information by up to 50 to 70%. This not only improves productivity but also enhances the overall employee experience.In a world where information overload can slow down operations, AI-powered systems provide a clear path forward. By automating the process of information retrieval, contextualizing search results, and offering voice-enabled access, businesses can create a more seamless, efficient, and user-friendly knowledge management process. AI is no longer just a tool for general queries—it’s becoming essential for managing the complexities of internal knowledge.To learn more about integrating AI into your organization and leveraging it to drive efficiency and results, don’t miss out on the previous blogs in our AI Accelerator series:Automating Code DocumentationAccelerating Code Transformation with AI
AI-driven Knowledge Management: Handling Queries with Higher Efficiency
Businesses need to innovate at pace. However, most find themselves stuck with outdated enterprise architecture (EA) practices that are slow, cumbersome, and resistant to change.I had a great opportunity to interview Hasan Jafri, VP of Engineering, Digital Business Solutions at TELUS and learn from his success with modernizing enterprise architecture.With an extensive background in leading digital transformations at multiple large enterprises, Hasan brought his expertise to TELUS, where he built a foundation for engineering excellence through innovative EA practices. He firmly believes that EA, when done right, is a critical driver of business growth and innovation.In this blog post, we’ll explore key strategies from Hasan’s playbook so you can rethink your approach to EA, unlock engineering excellence, and drive the agility your business needs.Rethinking Enterprise Architecture for the Digital Age From Static Blueprints to Agile Principles Traditional EA often relies on extensive documentation — static blueprints that can quickly become outdated. It’s a common complaint Hasan has heard throughout his career: “When I first took on an enterprise architecture type role, the biggest complaint was it’s too academic, it’s too high-level. They talk about concepts.”But modern EA requires a shift in mindset. Instead of emphasizing rigid documentation, the focus should be on creating adaptable solutions and embracing agility. Think of your EA as a product, not a document. Zero in on your customer journeys — both internal and external — and how technology can deliver value at every step.Embracing a Culture of Engineering Excellence Modernizing EA also means fostering a culture of engineering excellence. Even if you’re leveraging SaaS solutions, you need your engineers to be world-class coders who understand how to build upon those platforms.As Hasan puts it, “You still want to hold your SaaS providers and any large systems that you’ve purchased accountable for what they’re building and how they’re building it… so you want to build that muscle within your engineering team.”This means building a strong engineering team that can critically evaluate vendor offerings, ensure code quality, and create custom solutions that meet your specific business needs. Stream-aligned teams, trunk-based development models, and DevOps practices are essential for achieving this level of engineering excellence.Shifting Left: Building Quality Upfront Another key principle of modern EA is “shifting left.” This means incorporating security, reliability, and observability from the very beginning of the design phase.“So security design, reliability by design, privacy by design, observability by design,” says Hasan. “So what you do is you shift everything left and you build it up, design it up front. So it’s built in.”By building quality into your systems from the start, you can avoid costly rework, reduce technical debt, and minimize operational headaches down the road.Practical Steps to Modernize Your Enterprise Architecture Modernizing your EA requires concrete actions. Here are four practical steps you can take:1. Define Your Customer Journeys Start by understanding your customers. This applies to both external customers who use your products or services and internal customers who rely on your technology to do their jobs.“You want to start with customer journeys,” says Hasan. “So you need to find your customer journey, even if it is an internal customer, your salespeople, what’s their journey like in their sales process, right? What’s your operations people, what’s your field support people’s journey like …”Map out their key processes and identify their pain points. Then, think about how your EA can support those journeys and create a more seamless and efficient experience.2. Embrace EA as Code Traditionally, EA has been heavily reliant on documentation. But Hasan advocates for a more agile approach: EA as code.“How do you code EA? You build software templates. You build SDKs and code snippets,” he explains. “Architecture shouldn’t be building designs. Now, design documents are important because they tell you how to build something. But you want to bring that down to the code level.”By using software templates, SDKs, and code snippets, you can make your architecture more accessible and actionable for engineers. This helps bridge the gap between EA and engineering, fostering better collaboration and faster implementation.3. Automate Governance with AI Governance is an essential part of EA, but it often involves tedious and time-consuming processes like architectural review boards (ARBs). Hasan’s solution? Automate governance with AI. “We leverage AI to build our blueprints. So we’ve automated the blueprint build process,” he says. “We have the system architects go in and pull requests and review the code.”This approach makes governance more efficient and less intrusive, freeing up your team to focus on building and delivering value.4. Implement a FinOps Model Finally, don’t forget about cost optimization. In a cloud-centric world, managing your technology spending is crucial. “And then, lastly, in a digital paradigm in cloud, you need to build a FinOps capability,” advises Hasan.FinOps (financial operations) helps you track, analyze, and optimize your cloud costs, ensuring that your technology investments are aligned with your business goals and delivering a positive return.Building a Foundation for Continuous Improvement Everybody's working remotely and so that required investment in collaboration tools and communication toolsOnce you’ve rethought your approach to EA and taken practical steps to modernize your practices, you start building a foundation for continuous improvement.Invest in Tools That Drive Agility Modernizing your EA requires investing in the right tools. Hasan highlights the critical need for robust tools to support remote work, enhance collaboration and communication, and ensure data privacy and security. These tools are not just “nice-to-haves” but essential components of an agile and innovative EA practice.By providing your team with the tools they need to work efficiently and securely, you’re enabling them to embrace modern EA principles and deliver value faster.As we learned from Hasan’s experience, if you build great tools, people will use them — and that’s a powerful driver of adoption and success.Fostering Collaboration Between EA and Engineering Teams Silos between EA and engineering teams can hinder progress.Hasan advocates for a more collaborative approach, where architects are embedded within engineering teams, participating in code reviews and contributing to the development process. This ensures that EA principles are integrated into the actual building of systems.Open communication, shared goals, and a culture that values collaboration are essential for successful EA modernization.When EA and engineering teams work together seamlessly, you can achieve greater agility, faster time-to-market, and better business outcomes.Takeaway As companies across industries seek to unlock agility and accelerate innovation, modernizing enterprise architecture is no longer a choice, but a necessity.By rethinking traditional approaches and embracing the strategies outlined in this post, you can build a foundation for engineering excellence and position your business for success in the digital age.If you found these insights valuable, you wouldn’t want to miss his thoughtful perspectives on the future of technology and digital transformation on the full episode of Behind the Growth. Tune in to dive deeper into his expertise and insights.Find it hereEnterprise Architecture and Engineering Excellence
Engineering Excellence: 4 Steps to Modernize Enterprise Architecture
The business world is buzzing about AI, and for good reason. One of the most transformative applications of this technology is conversational AI — a powerful tool that can reshape how businesses interact with customers, streamline operations, and gain valuable insights.But beyond the hype, there’s a real need to understand how conversational AI actually works, where it can be applied effectively, and what it takes to implement it successfully.To demystify conversational AI, I sat down with Sabih Ahmed, Director of Conversational AI at Scotiabank, on the Behind the Growth podcast. Sabih is an accomplished product leader with over 15 years of experience in AI-driven enterprise transformations, making him a leading voice in his field.In this blog, we’ll explore Sabih’s expert insights, providing a guide to conversational AI to help you uncover the practical applications of this technology, learn how to navigate the implementation process, and discover what the future holds for conversational AI in the business world.What Is Conversational AI? Conversational AI is a type of artificial intelligence that enables machines to have human-like conversations. It goes beyond simple, pre-programmed responses, allowing systems to understand, interpret, and respond to natural language in a way that feels intuitive and engaging for the user.As Sabih puts it: “It’s really a subset or the subfield of artificial intelligence where we teach machines to talk to humans in a human-like way.”This is a significant departure from traditional chatbots, which typically rely on rule-based systems. Instead of following a rigid set of pre-defined paths, conversational AI leverages machine learning and natural language processing to adapt to different conversation flows and handle a wider range of user inputs.Real-world examples of conversational AI are becoming increasingly common.Think of the chatbots that help you book appointments online, the voice assistants like Siri and Alexa that answer your questions, or the AI-powered systems that guide you through troubleshooting steps when you encounter a technical issue. These are all powered by conversational AI, making interactions with technology more natural and efficient.Why Is Conversational AI Important for Your Business? As a tech leader, you’re always looking for ways to work smarter, not harder. Conversational AI offers a practical solution to enhance customer experiences, optimize operations, and drive bottom-line results.Improved Customer Experience Conversational AI enables you to provide instant, around-the-clock support. Customers can get answers to questions or resolve issues anytime, without waiting for a human agent. AI-powered systems can also personalize interactions, tailoring responses to individual customer preferences.Increased Efficiency Conversational AI can be used to automate repetitive, high-volume tasks, freeing up human agents to focus on more complex, strategic work. Sabih explains, “We usually tend to look at things where it’s a repetitive task, it is high in volume, and it is low in complexity.”Cost Savings Sabih emphasizes that one of the primary benefits of conversational AI is its ability to reduce operational costs. By automating tasks that would otherwise require human agents, businesses can optimize resource allocation and free up budget for other strategic initiatives.Data-Driven Insights Every conversation with a customer is an opportunity to gather valuable information. Conversational AI systems capture and analyze this data, providing insights into customer behavior, preferences, and pain points.“When it comes to conversational AI, that data is everything,” says Sabih, emphasizing the power of data-driven decision-making. These insights can be used to improve offerings, tailor marketing campaigns, and drive better business outcomes.How to Implement Conversational AI Successfully implementing conversational AI requires a strategic approach. It’s not just about choosing the right technology. It is also important to understand your business needs, secure buy-in from key stakeholders, and commit to ongoing optimization.Here’s a practical roadmap, based on Sabih’s insights, to guide you through the process:Step 1: Identify the Right Use Cases Begin by pinpointing specific areas where conversational AI can provide the most value for your business.Sabih emphasizes the “golden rule” for selecting use cases: focus on tasks that are “low complexity, high volume, and repetitive.” These are often the easiest to automate with conversational AI and yield the quickest wins.Think about automating frequently asked customer service inquiries, lead generation processes, appointment scheduling, or even internal workflows that involve repetitive actions.Step 2: Build a Compelling Business Case Before diving into implementation, it’s crucial to secure buy-in from key decision-makers.Sabih stresses the importance of a strong business case, stating: “Even if you have a business case, if you don’t have the right sponsors and the right executive buy-in … this will only be a pipe dream in PowerPoint decks and pieces of paper which wouldn’t see the light of day.”To build a compelling business case, quantify the potential return on investment (ROI) of conversational AI.Outline the expected cost savings, increased efficiency, and revenue growth that the technology can deliver. Clearly articulate how conversational AI aligns with your broader business goals and address any potential risks, outlining mitigation strategies to alleviate concerns.Sabih suggests showcasing successful implementations at similar companies, stating: “If you’re building it for a FinTech or if you’re building it for a large FI, there are always partners, within Canada, down south, or across the kind of Atlantic where you will find partners who have done this, who have built this and who have success stories. I think that reference validation also really helps.”Step 3: Choose the Right Partner One of the most crucial decisions you’ll face is whether to build your conversational AI solution in-house or partner with a vendor. Weigh your team’s technical expertise, available budget, and desired timeline.Sabih acknowledges that many companies find this decision challenging: “I think one big question that comes even before the implementation journey begins is do you build internally or do you partner? And I think that’s a big question that, more often than not, companies struggle with.”If you choose to partner, carefully evaluate potential vendors based on their experience in your industry, technical capabilities, and their approach to collaboration. Look for partners who have a proven track record of successful implementations and can provide ongoing support and guidance.Step 4: Design Engaging and Effective Conversations This is where conversational design comes into play. It’s the art and science of crafting seamless and engaging interactions between users and your conversational AI system.Sabih emphasizes the critical importance of this step, stating: “You could potentially have the best technology behind your conversational AI. But if the quality of the conversation that AI is having with you is not top notch, you’re going to fail either at adoption or at conversion or at resolutions, something or the other is going to break downstream.”To create a positive user experience, invest time in designing conversation flows that feel natural, intuitive, and human-like. Think about the tone of voice you want your AI to convey, the types of questions it should anticipate, and how it should handle errors or unexpected inputs.The goal is to create a conversational experience that feels as natural and helpful as interacting with a knowledgeable human agent.Step 5: The Crucial Role of Testing Thorough and comprehensive testing is paramount to ensure a seamless user experience, identify potential issues, and avoid costly mistakes.Sabih underscores the importance of testing, drawing from real-world examples of chatbot failures: “Air Canada’s chatbot ran into some issues.Then I think WestJet’s chatbot also had an issue. So all that to say, you can build a good chatbot or build a good conversational AI experience, whether it’s in chat or voice. Testing is foundational.”Your testing process should include usability testing to assess the user experience, ensuring that interactions are intuitive, clear, and easy to navigate. Additionally, conduct A/B testing to optimize conversation flows, comparing different variations to determine which perform best in achieving your desired outcomes.Sabih emphasizes, “It is very important that the testing ecosystem is set up from day one, which can avoid a lot of unpleasantries.”Step 6: Monitor, Analyze, and Optimize The work doesn’t end once your conversational AI solution is launched. It’s crucial to continuously monitor its performance, analyze user interactions, and make ongoing improvements.Sabih stresses the importance of a continuous learning cycle: “In conversational AI, the last thing you want to do is build it and forget it. There is nothing like that in conversational AI. You build it for once, but you are then incrementally adding on it throughout the time that it’s out there in the market. There is no stopping that.”Track key metrics like user engagement, conversation completion rates, and customer satisfaction to identify areas where your AI is performing well and areas that need improvement. Use this data to refine conversation flows, expand the AI’s knowledge base, and ensure that it continues to provide a valuable and engaging experience for your users.The Future of Conversational AI AI should be looked at as an augmentation of human capabilities and an augmentation of business outcomes rather than potentially a replacement.Sabih sees a future where conversational AI moves beyond simple chatbots and voice assistants to become a truly transformative force.One of the most exciting developments, according to Sabih, is the rise of “virtual agents.” These AI-powered systems will be capable of replicating human roles and responsibilities to a far greater extent than current technologies allow.He envisions “a true 100% replication of human beings’ roles and responsibilities,” driven by advancements in “machine-based brain power, as well as machine-based language capability.”To prepare for this future, Sabih believes that businesses need to embrace an AI-first culture. This means moving beyond the perception of AI as a job-stealing threat and instead viewing it as a powerful tool to augment human capabilities.He argues that AI should be “looked at as an augmentation of human capabilities and augmentation of our business outcomes rather than potentially a replacement.” This shift in mindset will be crucial for businesses to fully leverage the potential of conversational AI and other AI-driven technologies.Takeaway Conversational AI is no longer a futuristic concept — it’s a powerful tool that businesses can leverage today to enhance customer experiences, streamline operations, and drive tangible results.As Sabih wisely advises, the key is to approach this technology with a customer-centric mindset, focusing on delivering genuine value through engaging, intuitive interactions.By embracing an AI-first culture and strategically navigating the implementation process, businesses can position themselves at the forefront of this exciting technological evolution.Want to dive deeper into Sabih’s expert insights on conversational AI? Be sure to listen to the full episode of Behind the Growth for a conversation you don’t want to miss!Find it here:Mastering Conversational AI for Business
Conversational AI: 6 Steps To Smarter Customer Interactions
As technology evolves, many businesses find themselves constrained by legacy code that is difficult to maintain, understand, or upgrade. These systems often accumulate technical debt, which arises from years of patches, fixes, and workarounds.Additionally, the use of outdated frameworks and libraries introduces security risks and integration challenges, while the lack of clear documentation further complicates maintenance efforts.The diminishing pool of skilled developers for legacy languages such as COBOL adds to the complexity of modernizing these systems.As businesses increasingly adopt cloud-based infrastructures, the incompatibility of legacy code with modern deployment practices becomes a major hurdle to achieving seamless modernization.How AI Accelerates Code Transformation AI can play a crucial role in overcoming the many challenges associated with code transformation. It automates time-consuming tasks, allowing for the modernization of legacy code with less human intervention. AI-driven tools can:Analyze and understand existing code AI can efficiently digest folder structures and hierarchies, making sense of even the most complex and outdated codebases.Convert outdated frameworks and languages AI can automatically convert legacy code into modern, cloud-ready architectures while maintaining functional integrity.Fill documentation gaps Using natural language processing, AI can generate technical documentation for the transformed code, ensuring that future developers have the information they need.Optimize security AI tools analyze legacy code for potential security vulnerabilities and recommend mitigations, ensuring the new code adheres to modern security standards.Integrate automated testing AI can create unit test scenarios for the newly transformed code, ensuring a smooth transition and functionality in the new environment.AI Accelerator to Overcome the Challenges of Code Transformation We have developed a comprehensive AI-powered code transformation solution that addresses the complexities of legacy systems. Here’s how our approach solves the most pressing challenges:Step #1: Code Analysis and Security Audits Our solution starts by analyzing the legacy code to understand its structure, dependencies, and existing vulnerabilities. This ensures that the modernization process is secure from the outset.Step #2: Automated Code Conversion Once the analysis is complete, the AI automatically converts the legacy code to the target language and framework.For example, a Java Spring Boot application can be converted into a modern Node.js framework, including the transformation of all entities, repositories, and configuration files. The AI also suggests optimal cloud environments and architectural improvements, such as transitioning from a monolithic to a microservices architecture.Step #3: Human-in-the-Loop While AI handles most of the code transformation process, human oversight ensures accuracy and alignment with business goals. This human-in-the-loop system leverages both the speed of AI and the expertise of developers to deliver high-quality results.Benefits of Using an AI Accelerator Faster Code Transformation With AI, you can dramatically speed up the code transformation process—by as much as 40-60%. Imagine the time saved when AI does the heavy lifting, allowing your team to focus on more strategic tasks.According to Capgemini, this boost in efficiency lets businesses modernize their systems and adapt to new technologies much faster.Reduced Human Intervention AI isn’t just faster—it also cuts down on the manual effort needed for code migration. In fact, AI can reduce human intervention by 40-60%, according to McKinsey.This means your developers can spend less time on repetitive tasks and more time being creative and solving complex problems.Improved Security Security is always a concern with legacy systems, but AI makes it easier to catch vulnerabilities. AI-powered tools can identify security issues with 95% accuracy, as highlighted by Microsoft. So not only are you transforming your code faster, but you’re also making it more secure.Seamless Integration with Cloud Technologies As more businesses move to the cloud, it’s crucial to make sure your systems are ready for this shift. AI helps smooth this process by ensuring your code is cloud-native from the get-go. In fact, Gartner predicts that by 2025, 50% of businesses will rely on AI to manage their cloud environments.Business Impact Our AI-powered code transformation solution delivers significant business impact by reducing project timelines and costs.For example, one of our telecom clients saw a 30% reduction in delivery time and a 50% boost in efficiency as AI seamlessly converted their complex legacy code into modern architectures, complete with automated test cases.Final Takeaway AI-driven code transformation addresses the common challenges of technical debt, outdated frameworks, and documentation gaps, helping businesses modernize faster, more securely, and with greater scalability.As more companies adopt cloud technologies, AI will be essential for transforming legacy systems without disrupting operations.The modernization journey doesn’t have to be a bottleneck—with the right AI tools, it can be efficient, secure, and highly effective.To learn how AI can enhance the coding process, read our this blog from our AI Accelerator series on automating code documentation.
AI Accelerator Series: Accelerating Code Transformation with AI
The healthcare industry is on the verge of a monumental shift towards greater interoperability and personalization.Consider the following: the average US hospital generates a staggering 50 petabytes of data every single year. Yet, a vast majority of this data remains untapped, leaving a wealth of potential insights buried within complex systems.As medical breakthroughs converge with technological innovations, we’re on the cusp of a new wave in healthcare, one where data-driven insights, powered by AI, will reshape patient care as we know it.To navigate this exciting but complex landscape, I had the pleasure of interviewing Mike Hamilton, President of GE HealthCare Canada, for the Behind the Growth podcast. Mike has dedicated his entire career to healthcare and medical technology, bringing a wealth of experience and a forward-looking vision to the conversation.In this blog post, we’ll explore Mike’s insights on the key trends shaping the next wave of healthcare, from the transformative power of AI to the urgent need for data interoperability. We’ll also discuss technologies and strategies that are poised to revolutionize patient care and redefine the future of the healthcare industry.AI: From Buzzword to the Backbone of Healthcare While artificial intelligence (AI) might still sound futuristic to some, it’s already playing a crucial role in shaping the healthcare landscape.As Mike emphasizes throughout our conversation, AI is not about replacing human expertise; it’s about enhancing it. “AI represents the extension — not the extinction, but the extension — of professional capability in healthcare,” he explains.Augmenting, Not Replacing, Human Expertise Imagine a world where routine tasks are handled seamlessly by intelligent systems, freeing up healthcare professionals to focus on what they do best: providing personalized, compassionate care. This is the promise of AI in healthcare.One example of how AI is augmenting healthcare professionals is GE HealthCare’s TrueFidelity technology. Embedded within CT scanners, TrueFidelity uses AI to deliver highly detailed, real-time images of the heart, leading to faster and more accurate diagnoses.“By harnessing these advanced AI algorithms,” Mike explains, “TrueFidelity improves image clarity, enabling much more accurate diagnoses of cardiac conditions.”Similar AI-driven solutions are transforming radiology workflows, optimizing patient positioning during scans, and enhancing image quality across a range of modalities. This means faster diagnoses, more informed treatment decisions, and ultimately, better outcomes for patients.Unleashing the Power of Data for Personalized Care AI models will not only support real-time clinical decision making, but in all probability will possess the capacity to predict patient health outcomes.AI’s impact extends beyond tools like TrueFidelity. Mike envisions a future where AI creates a “cohesive mosaic” of patient data, integrating multimodal data from various sources — medical imaging, doctor’s notes, lab results, genomic data, and more — to create a holistic understanding of each person’s health. “AI foundation models,” he says, “will possess the capability to begin to predict patient health outcomes years in advance.”AI opens up incredible possibilities for personalized medicine, allowing healthcare providers to identify individuals at risk and intervene proactively to prevent disease progression. It’s a future where healthcare becomes less about reacting to illness and more about predicting and preventing it altogether.This predictive power, combined with AI’s ability to augment human skills, paves the way for a future of truly personalized and preventative healthcare. However, navigating this future requires a thoughtful approach to data privacy and the ethical considerations surrounding AI.Breaking Down Silos in Digital Health: Interoperability as a Cornerstone We’ve established the transformative power of AI in healthcare, but its full potential can only be realized if we address a critical challenge: data interoperability.As Mike eloquently puts it, “The average US hospital produces 50 petabytes of data per year […] And about 97 percent of that data today remains unused.”The Urgent Need for Seamless Data Flow This vast sea of untapped data represents a missed opportunity. Healthcare systems are often fragmented, with information siloed within departments, electronic health records, and disparate technologies.This lack of interoperability hinders our ability to leverage AI’s full potential for insights, prediction, and personalized care.A patient’s data should be readily accessible to specialists, hospitals, and researchers. This level of data accessibility would empower AI algorithms to identify patterns, improve diagnostic accuracy, and ultimately personalize treatments based on a holistic understanding of each patient.Collaboration and Open Platforms as Key Enablers Achieving this level of interoperability requires a fundamental shift in how we approach data in healthcare.Mike stresses the importance of collaboration: “Scaling AI deployment and driving technological innovation necessitates collaboration, not only with trusted technology vendors but also with other healthcare institutions and really tapping into a diverse network of partners.”The key lies in embracing open platforms and data-sharing initiatives that break down traditional silos and foster a spirit of cooperation. Cloud-based platforms, coupled with robust data security measures, can play a vital role in enabling secure and efficient data exchange between stakeholders.Empowering Patients: The Rise of Consumer-Centric Care As we navigate this new wave of healthcare innovation, it’s crucial to remember that technology should ultimately serve to enhance the patient experience. The rise of consumer-centric care puts patients at the forefront, empowering them to take a more active role in managing their health.From Passive Recipients to Active Participants Mike acknowledges this shift, observing that healthcare professionals need to “work backward from the actual need of [their] customer… the patient.”This means understanding and addressing the evolving expectations of patients who are increasingly engaged in their own health journeys.Technologies like telehealth, remote monitoring devices, and personalized health apps are key enablers of this transformation. These tools provide patients with greater access to information, facilitate seamless communication with healthcare providers, and allow individuals to track their health data and make informed decisions about their care.Building Trust and Addressing Digital Equity We need to look forward and figure out how we educate and train staff on AI. Not just the ones coming up through the education system, but those who are out practicing today.However, this transition to a more empowered patient model also presents challenges. Ensuring equitable access to digital health solutions is paramount, as disparities in technology access can exacerbate existing health inequities.“Educating and training staff,” as Mike emphasizes, is critical to bridging the digital divide. Healthcare organizations must actively work to address digital literacy gaps, providing patients with the skills and support needed to navigate and benefit from these tools.Additionally, robust data privacy measures, transparent communication, and user-friendly interfaces are essential to building patient trust and fostering widespread adoption of digital healthcare solutions.Takeaway The next wave of healthcare is already upon us, driven by a confluence of powerful forces — from AI’s transformative potential to the rise of patient empowerment.Embracing these changes requires a willingness to adapt, collaborate, and always keep the patient at the center of our efforts.Mike Hamilton’s expertise and vision offer a compelling roadmap for navigating the future of healthcare — one where technology empowers both patients and professionals to achieve better outcomes, together.If you found this article insightful, be sure to tune into the full episode of Behind the Growth for a conversation you do not want to miss!Find it here:Shaping the Next Wave of Healthcare
Shaping the Future of Healthcare with AI and Innovation
Businesses today are under constant pressure to adapt, innovate, and deliver exceptional customer experiences. But navigating the complexities of cloud transformation is a challenge many face.To help you accelerate your cloud journey, I had the pleasure of interviewing Mattew Collins, Chief Commercial Operations Officer at Calix. Matt has played a pivotal role in Calix’s remarkable transformation from a hardware-centric company to a cloud solutions powerhouse.With over 30 years of experience in the tech industry, including leadership roles at IBM and Dun & Bradstreet, Matt brings a wealth of knowledge and practical insights to the table.In this article, we’ll unpack Matt’s key takeaways on how to successfully navigate a cloud transformation, drawing on his firsthand experience at Calix. We’ll explore the importance of a bold vision, agile methodologies, a collaborative culture, and, above all, a relentless focus on the customer experience.Defining a Bold Vision The most important growth driver is when your customers are so excited that they're going to go out and promote you.Matt’s first impression at Calix was, “Wow, we have a lot to get done!” They were not making a profit at the time and needed to transform rapidly. The key? A crystal-clear vision.“We had a very clear vision that said everything we do is driven towards the goal of democratizing data and helping our customers use the techniques of a Google or an Amazon or Facebook,” Matt explained, “but to drive a better, more operationally effective broadband service provider.”For Calix, this meant empowering even smaller broadband providers with the tools and insights to leverage data like industry giants and reimagine how their business works to provide value-add and deliver a superior experience. Thanks to this clarity in vision, Calix was able to transform from a hardware company to a cloud solutions company.That’s the power of a bold vision for cloud transformation.Defining your bold vision begins with asking some fundamental questions:Why are you taking on this challenge? How can you bring your customers along for the journey? What is your purpose? What drives you? A truly inspiring vision should push you beyond your comfort zone, excite your team about the possibilities, and convert customers into fans.Fostering a Culture of Collaboration and Transparency A bold vision is essential, but it’s only the starting point. To bring that vision to life, you need a team that’s aligned, empowered, and working together seamlessly. Matt shared that a culture of collaboration and transparency was crucial to Calix’s successful transformation.“Calix is an incredibly collaborative organization, almost to the point of discomfort,” Matt laughed. “The point is there isn’t an important decision that we make that isn’t made collaboratively.”He described a system where every new product idea, deal with a customer, and even marketing campaign is reviewed by the entire leadership team and relevant team members. This level of transparency and open dialogue might seem daunting, but it’s essential for moving fast and ensuring everyone is on the same page.Think about how you can foster a similar environment within your own organization:Break down silos: Use communication tools that encourage cross-functional collaboration and knowledge sharing. Lead by example: As a leader, be transparent about your decision-making process and encourage open discussion and debate. Create a safe space for feedback: Ensure your team feels comfortable sharing ideas, challenges, and even dissenting opinions. Matt also highlighted the importance of what he called “dynamic tension,” or the ability to have honest debates and challenge ideas constructively. This might feel uncomfortable at first, but it’s essential for pushing your team to think critically and come up with the best possible solutions.A culture of collaboration and transparency isn’t built overnight. It takes time, effort, and a commitment from leadership to create an environment where everyone feels valued, heard, and empowered to contribute.Embracing Agile Methodologies In the cloud technology field, traditional waterfall development methods can feel like trying to navigate a speedboat with a giant ship’s rudder. You need to be nimble, adaptable, and responsive to change. That’s where agile methodologies come in.Matt described how Calix made a decisive shift from a lengthy, waterfall approach to a more iterative and collaborative agile innovation model.“We completely flipped our innovation cycle to what we call our agile innovation method, which is very much a scrum model,” Matt explained. “You know, develop an MVP, put it in market, get early adopters, and then build the product out.”This shift to agile enabled Calix to accelerate their development cycles, get feedback from customers more quickly, and continuously improve their offerings.Some key principles of agile methodologies that you can apply to your own cloud transformation include:Iterative development: Don’t be afraid to fail. Failure is part of the process and a learning opportunity. Continuous improvement: Embrace a mindset of continuous learning and improvement, using feedback from customers and your team to refine your solutions. Collaboration and communication: Foster close collaboration within the company to ensure everyone is aligned and working towards a common goal. Transitioning to agile might require a significant cultural shift within your organization. Be patient, persistent, and provide your team with the training and support they need to succeed. The rewards of increased speed, flexibility, and customer focus will be well worth the effort.Focusing on the Customer Experience Imagine a world where your customers rave about your services, a world where they see you as a partner who understands their needs and helps them succeed. That’s the power of putting customer experience at the heart of your cloud transformation.Matt strongly emphasized this point, saying, “Don’t just be the dumb pipe.” He shared Calix’s commitment to helping broadband providers move beyond simply delivering connectivity and become true experience providers.“We really focused on, ‘Hey, you have to have persona-based offerings and you have to have value-based offerings,'” Matt explained. “The reason is, if you look at what happened during the pandemic, people actually started to define themselves with how they’re using broadband.”Your customers aren’t just buying a product or service. They’re buying an experience.Matt suggested some key actions you can take to prioritize customer experience in your cloud transformation:Create detailed profiles of your ideal customers, understanding their needs, motivations, and pain points. Understand the end customer. Calix not only focused on broadband companies but also their subscribers. As Matt puts it, “If you really want to understand what’s happening in your market, you have to understand what’s happening with the end customer, in this case the subscriber, as well as your customer, and why is it happening.” Use technologies like AI to improve customer experience. Matt discusses how chatbots can reduce customer waiting time and lead to faster resolution, which enhances customer satisfaction. By focusing on delivering a seamless, valuable, and even delightful customer experience, you can turn your cloud transformation into a powerful engine for growth, loyalty, and brand advocacy.Takeaway Transforming your business for the cloud era is a journey that requires a bold vision, a collaborative spirit, a willingness to embrace agile methodologies, and an unwavering focus on the customer experience. But with the right strategy, you can unlock incredible opportunities for growth, innovation, and customer delight.If you found these insights valuable, be sure to tune into the full episode of Behind the Growth for a conversation with Matt Collins you don’t want to miss! You’ll gain even deeper insights into Calix’s transformation journey and learn how their experiences can help you navigate your own cloud evolution.Find it hereThe Transformational Journey of a Cloud Services Leader
Mastering Cloud Transformation: Insights from a Cloud Services Leader
Navigating complex healthcare systems can often feel like an exercise in frustration, with bureaucratic hurdles obscuring the path to quality patient care.But imagine a world where accessing medical services is as simple as clicking a button, where personalized care plans are crafted solely with the patient in mind, and digital innovation is harnessed fully to streamline and optimize processes for your well-being.This transformation is underway, reshaping the traditional healthcare landscape into one that is interconnected, intuitive, and highly responsive to individual needs.What we’re looking at is a paradigm shift that extends beyond mere patient satisfaction, but a reimagining of how we approach and manage our health. Driven by technology, the future of healthcare promises inclusivity, efficiency, and personalized healthcare solutions.To dissect the true impact of digital innovation on healthcare, I had the pleasure of interviewing Aaron Cheng, VP of Digital Health at Shoppers Drug Mart, who is a seasoned executive celebrated for spearheading dynamic technical teams and fostering groundbreaking innovation in the digital landscape.Aaron’s expertise converges on consumer fintech, digital marketplaces, and enterprise digital solutions, and together we explored his profound insights on integrating digital health into traditional pharmacy models, the importance of a patient-focused ecosystem, and his vision for the AI-driven future of healthcare.The Digital Transformation of Pharmacy CareThe landscape of healthcare is undergoing a monumental shift, thanks to the advent of digital technology. Gone are the days when your only option was a visit to the doctor’s office for any minor ailment.Today, digital health tools are revolutionizing the way we access pharmacy care, making it more convenient, personalized, and efficient than ever before.Accessibility of Medical Services Digital platforms, like health apps, enable you to consult with healthcare professionals without leaving your home. This not only saves time but also makes healthcare accessible to those in remote or underserved locations.Personalized Care Plans Imagine having a healthcare plan tailored just for you, considering your medical history, lifestyle, and health goals. Digital health tools analyze your data to provide personalized advice and recommendations, enhancing the quality of care you receive.Efficiency and Time-Saving The integration of AI and machine learning in pharmacy care automates routine tasks such as prescription refills and appointment scheduling. This not only reduces wait times but also allows pharmacists to focus more on patient care.Expanded Scope of Services Pharmacies are no longer just places to pick up medication; they’re becoming wellness hubs. With the help of digital tools, pharmacists can now offer services like minor ailment treatment, wellness consultations, and chronic disease management.This digital transformation is not just changing the operational aspects of pharmacies but also redefining the role they play in the healthcare system.By leveraging technology, pharmacies are expanding their scope and capacity to offer more comprehensive care, directly impacting patient outcomes in a positive way.The Role of AI in Streamlining Healthcare Delivery Artificial intelligence is no longer just a buzzword in tech circles; it’s a game-changer in the healthcare industry, poised to revolutionize how we receive and manage our healthcare. Here’s how AI is making strides in streamlining healthcare delivery, ensuring that you get the care you need with efficiency and precision that was previously unimaginable.Automating Administrative Tasks One of AI’s most significant impacts is its ability to automate the countless administrative tasks that bog down healthcare professionals. From scheduling appointments to managing patient records, AI can handle these processes quickly and accurately, freeing up healthcare providers to spend more time where it matters most—with their patients.Enhancing Diagnostic Accuracy AI algorithms are incredibly adept at analyzing vast amounts of data, including medical images. These algorithms can detect anomalies that the human eye might overlook, leading to earlier and more accurate diagnoses. Early detection is often key to effective treatment, making this application of AI incredibly valuable.Personalized Treatment Plans By analyzing data from a variety of sources, including electronic health records and genetic information, AI can help healthcare providers develop more personalized treatment plans. This bespoke approach ensures that treatments are tailored to your unique health profile, potentially increasing their effectiveness.Predictive Analytics AI’s ability to predict potential health issues before they become serious problems is perhaps one of its most exciting applications. By analyzing trends in your health data, AI can alert you and your healthcare provider to risks so you can take proactive steps to avoid them.In essence, AI is transforming healthcare delivery from a one-size-fits-all model to a more personalized, efficient, and predictive approach. This shift not only promises better health outcomes but also a more satisfying healthcare experience for you.The Challenges of Digital Health Adoption The road to integrating digital health solutions into our healthcare system is paved with potential but also marked by challenges. Recognizing and overcoming these hurdles is essential to fully realize the benefits of digital health for everyone involved. Here are some key challenges and strategies to address them:Resistance to Change One of the most significant barriers is the natural human resistance to change. Both patients and healthcare providers can be wary of new technologies, especially when it involves sensitive health information. Educational campaigns and demonstrations of tangible benefits can help overcome skepticism, showcasing how digital health tools enhance, rather than complicate, the healthcare experience.Privacy and Security Concerns With digital health solutions, concerns about data privacy and security are paramount. Patients need assurance that their health information is protected. Implementing robust cybersecurity measures and transparent privacy policies is crucial. Educating users on how their data is used and safeguarded can also build trust.Interoperability For digital health to be truly effective, different systems and tools need to be able to communicate with each other seamlessly. This interoperability is often hindered by the diverse range of software platforms in use. Developing and adopting universal standards and protocols can help ensure that digital health tools work together smoothly, providing a cohesive user experience.Accessibility Digital health must be accessible to everyone, regardless of age, income, or tech-savviness. Addressing this involves creating user-friendly interfaces that are intuitive for all users, including those who are not digitally literate. Additionally, ensuring affordable access to the necessary technology, such as smartphones or internet service, is crucial for widespread digital health adoption.Regulatory Hurdles Navigating the complex regulatory landscape can be a significant challenge for digital health innovators. Regulations are necessary to ensure patient safety and data protection, but they can also slow down the development and adoption of new technologies. A collaborative approach between innovators and regulators can help find a balance, fostering innovation while maintaining high standards of safety and privacy.By addressing these challenges head-on with strategic solutions and stakeholder collaboration, the path to digital health adoption can be smoothed, paving the way for a healthcare system that is more responsive, efficient, and personalized.Future Prospects: Integrating Digital Solutions for Enhanced Patient Care The integration of digital solutions into healthcare is not just an ongoing trend; it’s the future of patient care. As we look ahead, several exciting prospects stand to further enhance the quality, accessibility, and personalization of healthcare services. Here’s a glimpse into what the future might hold:Seamless Integration Between Digital and Traditional Care In the near future, we can expect a healthcare ecosystem where digital and traditional care methods are seamlessly integrated. This will allow for a more cohesive healthcare journey, where patients can move effortlessly between online consultations, in-person visits, and digital monitoring, all within a unified framework.Expansion of Remote Monitoring and Telehealth The use of wearable devices and home monitoring equipment is set to expand, enabling continuous care and health management from the comfort of one’s home. This not only increases convenience for patients but also allows for real-time data collection that can enhance preventive care and early intervention.AI-Powered Personal Health Assistants Imagine having a personal health assistant powered by AI, always on hand to provide health advice, remind you of medication schedules, and even alert you to potential health issues before they become serious. These assistants could become a central part of everyone’s health management, offering tailored advice based on your health data.Digital Platforms for Holistic Health Management Future digital health platforms will likely offer holistic health management tools, integrating physical, mental, and emotional health tracking and support. These platforms will provide personalized recommendations for diet, exercise, stress management, and more, all based on your health data and personal preferences.Enhanced Patient Empowerment and Education With the advancement of digital health solutions, patients will have greater access to their own health information and a better understanding of their health conditions. This empowerment will enable more informed decision-making and foster a more collaborative patient-provider relationship.As digital solutions evolve and mature, their potential to transform the healthcare landscape is limitless. Embracing these changes will not only improve outcomes for patients but also enhance the overall healthcare experience for providers and patients alike.Takeaway The promise of digital health is not in the technology itself, but in its capacity to empower patients and healthcare providers alike. While the journey toward integrating digital solutions into healthcare is an ongoing process, it is filled with both challenges and opportunities.By harnessing digital tools and embracing innovation, we can create a healthcare system that fosters wellness, prevention, holistic care, and is truly responsive to the needs of every individual, ensuring better health outcomes for all.If you found this article helpful and want to gain more insights into the future of healthcare, be sure to tune into the full episode of Behind the Growth for a conversation you do not want to miss!Find it here:Bridging Digital Innovation and Pharmacy Care
Embracing Digital Health: The Impact of Technology on Pharmacy Care
One of the most persistent challenges in software development is the lack of comprehensive and up-to-date code documentation.Whether it’s business-level or technical documentation, keeping it aligned with the evolving codebase is often neglected. This leads to major issues down the road—understanding code logic becomes harder, updates go undocumented, and large codebases become increasingly complex.Additionally, modern code often comes without adequate explanations, making it difficult to understand the business context behind it. This is further compounded when tools meant for documentation lack integration with existing development workflows.Moreover, as codebases grow larger, generating documentation manually becomes resource-intensive and error-prone, putting further strain on already overburdened development teams.The Role of AI in Code Documentation AI has a transformative role in automating the documentation process for codebases, making it both scalable and efficient. It not only reduces the human effort required but also ensures that the documentation is accurate, up-to-date, and aligned with the actual code. Here’s how AI steps in to solve these challenges:Generate Technical and Business Documentation AI-powered tools can automatically generate detailed technical documentation, including class diagrams, sequence diagrams, and flowcharts, while also providing business-level insights into what the code accomplishes.Provide Context and Understanding By leveraging natural language processing, AI tools interpret and explain intricate code logic, even for large and complex systems, ensuring that stakeholders have a clear understanding of the codebase.Integrate Seamlessly with Tools AI-driven documentation systems can integrate smoothly with popular development platforms like GitHub, allowing them to automatically pull code changes and keep documentation updated without manual intervention.Improve Scalability AI excels in analyzing vast codebases without being constrained by human capacity, making it ideal for large-scale documentation tasks that would otherwise be too labor-intensive.Solving the Code Documentation Challenge Our solution to the code documentation problem incorporates AI to automate and enhance the process of generating high-quality documentation. Here’s how it works:1. Automated Documentation Creation Our AI-powered system can generate comprehensive documentation for codebases, from technical details to business logic. It provides flow diagrams, sequence diagrams, and class diagrams to give a visual representation of the code’s structure and functionality.2. Multi-Language Support The solution supports multiple programming languages and frameworks, allowing it to generate documentation for diverse codebases, whether it’s a Java Spring Boot application or something else entirely. The AI interprets the code and provides clear, plain-language explanations of its components.3. Multiple Interaction Points The system engages in multiple interactions with large language models (LLMs) through various threads. This fine-tuning ensures that the generated documentation is highly accurate and tailored to the specific needs of the project.4. External Tool Integration Our AI tool integrates with external systems to automatically create architectural diagrams and documentation without manual input. It not only pulls data from your codebase but also keeps everything updated as the code evolves.5. Local Deployment for Security For clients with high confidentiality requirements, our solution allows for local deployment, ensuring that all data remains within your enterprise environment. This option is crucial for companies handling sensitive or proprietary code.Benefits of Our AI Solution Up-to-Date Documentation with Minimal Effort One of the biggest perks of using AI is how much time it saves. Automating the documentation process can cut down 60-80% of the time developers would normally spend updating docs manually. That’s a huge win for teams who’d rather focus on writing great code than worrying about keeping documentation up to date.Improved Code Understanding Let’s face it—understanding complex code can feel like untangling a giant knot. AI makes it easier by breaking things down into clear explanations and diagrams. According to a Forrester study, teams that used AI for code analysis saw a 30% boost in development speed, because everyone had a better grasp of the code. No more guessing games!Efficient API Documentation API documentation can be a real time sink. But with AI, you can cut the time spent on it by 50%. Tools like this handle heavy lifting, automatically generating everything from endpoint details to request parameters. This means faster development cycles and less manual work for your team.Automated Diagrams and Visualizations Ever spent hours making flowcharts or class diagrams? AI can handle that for you. Automated tools that generate these visuals can save teams 20-40% of the time normally spent on designing and reviewing architecture. Plus, they make it easy for everyone to stay on the same page with clear visual representations of the code.Final Takeaway: The Impact of Automating Code Documentation with AI The impact of automating code documentation with AI is transformative for businesses.By reducing the human effort required for generating and maintaining documentation, companies can achieve 60-80% efficiency gains. This not only frees developers to focus on innovation but also ensures that documentation stays accurate, up-to-date, and aligned with the evolving codebase.Automated tools generate real-time technical documentation, API specs, and architectural diagrams, making it easier for teams to collaborate, onboard new developers, and avoid costly misunderstandings. The ability to produce detailed, clear documentation without the usual manual burden enhances communication across teams and improves project outcomes.In short, AI-powered documentation allows businesses to overcome one of the most persistent challenges in software development: maintaining quality documentation.By automating this process, teams can move faster, work more efficiently, and focus on building great software rather than getting bogged down by tedious, error-prone tasks. With AI, businesses can confidently tackle even the largest, most complex codebases, ensuring clarity and continuity in their projects.
AI Accelerator Series: Automating Code Documentation
As businesses evolve, embracing generative AI (GenAI) no longer remains a luxury, but a necessity, making it crucial for companies to identify, envision, and embrace the value of GenAI across the organization.But the challenge arises when it comes to balancing ethical considerations with the development and implementation of AI technologies.In a recent episode of Behind the Growth, I had the opportunity to engage with Andrea De Mauro, a renowned Data & Analytics Leader, Author, and Executive Advisor.Celebrating his vast career and a rich background in Data Analytics – that spans over 18 years and includes building data science and analytics organizations at Vodafone and Procter & Gamble – together we explored the intricacies of GenAI and its human-centric challenges.Andrea lets us in on his extensive experience of championing ethical data use and innovation while prioritizing the human element, and in this article, I share these invaluable insights that will help you successfully navigate the intersection of technology, business, and ethics.The Human-centric Challenges of GenAI With the rapid advancement of Gen AI, there’s a lot of focus on the technical side of things. But the emphasis on technological advancements often overshadows critical human challenges.While it may seem counterintuitive, the process of data transformation is primarily a human challenge related to organization and culture more than technology and data.AI may help maximize business outcomes, but organizational support is needed to successfully navigate the transition.Recognizing this, organizations must address three key human-centric issues beyond the technical realm.1. Managing Talent One pivotal aspect is the strategic management of talent, involving clear role definitions, responsibilities, career paths, and skill development for data professionals. Organizations often grapple with retention and recruitment issues when they lack well-defined career progression paths.2. Organization The second crucial element involves organizational dynamics, requiring a careful placement of data analytics talents either within centralized units or integrated with business teams. Striking the right balance ensures a conducive environment for continuous dialogue and collaborative value creation between data professionals and business units.3. Culture A third and integral aspect is cultivating a data-friendly culture. This involves fostering openness within the organization, where data professionals, including analysts, actively contribute to decision-making processes. Achieving this cultural shift is essential for leveraging data effectively.The Ethical Use of AI In addition to human challenges, we must also recognize the need for ethical use of AI. Ethical use of AI demands a clear understanding and codification of its capabilities.The first step involves meticulously codifying AI functions, allowing organizations to assess their impact on various stakeholders, customers, and employees. This process, although time-consuming, provides a crucial foundation for evaluating the ethical implications of AI applications.By developing explicit codes of ethics for AI, coupled with comprehensive training, we can ensure a shared understanding across the organization. This approach, exemplified in managing AI-driven tools like chatbots, equips employees to not only use the technology effectively but also be aware of potential risks.Building fluency in AI, involving a common language and understanding of key concepts, is pivotal for ethical considerations, particularly in recognizing and managing biases that machines can have when making decisions and inferring. Investing in ethical AI capabilities and promoting fluency ensures responsible and informed usage of AI technologies in organizations.The Crucial Role of People ManagersAs AI is evolving, so is the definition of being a manager. The role of people managers becomes pivotal — particularly in navigating concerns about job displacement.One major shift is evident in decision-making processes, where managers increasingly rely on algorithmic recommendations rather than their instincts. This skill needs to be learned, and it’s up to managers to encourage their teams to be mindful of AI’s limitations.Amidst these changes, managers of data professionals should understand that data professionals often feel undervalued and burdened by routine tasks, despite aspiring to leverage their skills as transformative agents.So, managers should alleviate their frustrations by involving data professionals in meaningful conversations, clarifying roles, and emphasizing their role in the ongoing cultural transformation toward data-centric approaches.By nurturing a sense of inclusion and actively involving data professionals in business discussions, managers can harness their potential as transformation agents in the company’s data culture.Key to Success: Making the Collaboration Between Humans and AI WorkTo successfully integrate AI in their processes, companies need to foster organizational fluency in AI technologies, making sure that everyone — no matter their job — know what AI and Gen AI are about.A shared understanding of AI helps ease worries and sets the stage for humans and machines working together. When mundane tasks are automated, individuals can channel their efforts into more meaningful and creative endeavours. This collaboration between humans and AI is all about leveraging each other’s strengths for enhanced efficiency and effectiveness.Success in this collaborative future hinges on establishing a culture of collaboration within organizations. This involves not only understanding AI’s capabilities and ethical considerations but also actively fostering a mindset that encourages experimentation and collaboration between humans and machines.By cultivating such a culture, we can navigate the evolving AI landscape, where humans and machines coexist, amplify each other’s capabilities, and drive innovation forward.As this collaboration becomes more established, it offers a positive outlook for the future. However, we need to approach this change carefully, making sure it is inclusive. This is why the fluency program is so crucial — it reaches everyone in the company and fosters understanding and involvement across the board.Practical Tips for Leaders and Organizations If you are venturing into the complexities of Gen AI while prioritizing the human element, having a strategy in place can pave the way for a smoother journey. Here are a few tips to seamlessly integrate Gen AI, focusing on inclusivity, skill development, and strategic vision for the entire workforce:Selecting Impactful Use Cases First and foremost, envisioning the role of Gen AI within the organization is crucial. This involves selecting impactful use cases that not only demonstrate value to leaders but also to the entire workforce. Gen AI should be perceived as a transformative force for everyone in the organization, debunking the notion that it’s solely for technical experts.Upskilling Technical Professionals To facilitate the integration of Gen AI, upskilling technical professionals is imperative. Fluency programs should be implemented, focusing on developing the skills required for working with generative AI. This not only prepares technical professionals but also ensures that the entire workforce is equipped for the integration of new skills and tools.Inclusive Development Path Creating an inclusive development path is another vital step. This involves offering education on the basics of AI and Gen AI to all employees. With tools like Microsoft’s Copilot becoming increasingly prevalent, an inclusive approach ensures that the workforce is ready to engage with these tools consciously — with awareness and ethical considerations.Managerial Involvement Lastly, leaders are encouraged to stay personally involved from day one instead of delegating the responsibility of Gen AI to technical experts. Seeing their leaders embrace Gen AI will enthuse employees to learn more about it and foster a culture of curiosity and continuous learning.Takeaway To enhance collaboration between AI and humans, it’s clear that organizations need to put their efforts towards building AI fluency that ensures responsible usage and recognition of biases in decision-making.Overcoming human-centric challenges in talent management, organizational dynamics, and fostering a data-friendly culture requires strategic talent management with clear role definitions and skill development. Moreover, people managers will play a pivotal role in navigating and encouraging algorithmic decision-making, and involving data professionals in meaningful conversations.The collaboration between humans and AI is crucial and not just a matter of the future: it is a current need that enables businesses to achieve more creativity and efficiency, and it is up to organizations to foster a culture of experimentation and collaboration to make GenAI work for everyone.If you found this conversation insightful and want to learn more, be sure to tune into the full episode of Behind the Growth!Find it here:The Human Element in the Age of GenAI
AI Ethics and Challenges: Embracing Humanity in the GenAI Era
In an era where customer experience is top priority, it is critical for us to understand the digital evolution reshaping travel experiences. Several key trends are redefining this landscape, including the rise of online travel agencies (OTAs), a growing focus on mobile usage, and the need for businesses to establish a solid online presence.I had the privilege of interviewing a leader at the intersection of technology and travel, VP of Product & Customer Platforms at FlightHub, Ramzi Rahbani, to discuss digital transformation within the travel industry.With a diverse background spanning banking to retail, Ramzi brings over 13 years of leadership experience in driving digital innovation. His dedication to a product-first approach and expertise in fostering high-performance teams underscore his commitment to enhancing travel experiences and creating a more connected world.During our conversation on the Behind the Growth podcast, Ramzi shared insights into the dynamic nature of the travel industry, future trends, and the pivotal role of data and customer feedback in shaping services to meet the evolving needs of travellers – and I’m happy to share his celebrated and eye-opening perspective in this article.Four Trends in the Post-COVID Era of TravelThe aftermath of COVID-19 has left an indelible mark on the travel sector, redefining its landscape and charting new trajectories. But the travel industry is now navigating a path of recovery and adaptation, and examining the post-pandemic landscape reveals four prominent trends that are reshaping the travel sector.1. Strong Resurgence in Travel Firstly, there is a strong resurgence in travel, marked by a forecasted 6 percent growth in 2024 compared to the pre-pandemic peak of 2019. This indicates a remarkable recovery and hints at a robust future for the industry.2. Changing Consuming Behaviours Secondly, changing consumer behaviours post-pandemic are influencing the way people engage with travel. The shift towards online bookings has become pronounced, with major players aiming for 100 percent online transactions. American Airlines, for example, is currently at an impressive 80 percent online booking rate, underscoring the importance of digital channels.3. Growth of Online Travel Agencies Thirdly, the growth of Online Travel Agencies (OTAs) has become a significant trend. With a projected Compound Annual Growth Rate (CAGR) of 6 percent leading up to 2028, OTAs are establishing themselves as key players in the industry. This trend is reflected in the success and growth of platforms like FlightHub, highlighting the evolving dynamics of travel bookings.4. Shift Towards Mobile-First Approach Lastly, the travel sector is experiencing a shift towards a mobile-first approach. Mobile bookings now comprise a substantial portion of the total volume, reaching around 50 percent. This shift emphasizes the necessity for travel platforms to prioritize mobile accessibility and engagement, recognizing it as a fundamental channel rather than a mere luxury.Building a Robust Online Presence in the Travel Sector Establishing a robust online presence has become imperative for gaining a competitive advantage. This entails catering to diverse customer preferences across various channels — mobile, web, and walk-in.Understanding the nuances of each channel is crucial. For example, mobile app users tend to be four to five years younger on average than website users. And while walk-in or call-in options persist, acknowledging a diverse age range in the customer base — and each demographic’s needs — is vital for sustained success in the travel industry.The prevalence of an omni-channel approach becomes evident in travel planning habits, with 94 percent of leisure travellers seamlessly switching between devices during the trip planning process — starting on mobile for searches and possibly shifting to a website for the actual booking.Acknowledging this behaviour, we need to seamlessly integrate their services across platforms, ensuring a cohesive and uninterrupted customer experience.Key Considerations for Crafting a Digital Strategy The digital channels you choose must align with a comprehensive business strategy.There are a couple of important elements that need to be integrated so businesses can effectively leverage digital channels to drive customer engagement and achieve sustainable growth in the digital era:Defining the Strategic Role of Mobile Apps Fundamentally understanding the role of mobile apps in the overall business strategy is crucial as this is what sets the foundation for subsequent strategic decisions. This begins by asking a critical question: What purpose does the mobile app serve? This will help us understand whether these channels serve as central drivers, enablers, or peripheral avenues.Aligning Digital Channels with Business Objectives The app becomes a channel for enhancing customer experience by leveraging the native capabilities of the device. The goal is not merely app development but a conscious effort to align the digital channels with broader business objectives.Setting Short-Term KPIs Businesses should set short-term key performance indicators aligned with broader business goals. This involves a meticulous examination of the business strategy, identifying the intended outcomes, and establishing targets. KPIs will serve as benchmarks for measuring the effectiveness of digital channels.Building a Customer-centric App While the app needs to be tailored to meet specific business needs, more importantly, it has to be customer-centric. This involves translating strategic decisions into practical steps, ensuring that the app is not just a standalone feature but an integral part of the customer engagement strategy.The Impact of Digital Evolution on Travel vs. Banking Apps Digitalization is taking over our lives, and every industry is feeling the pressure to provide customers with fast and easy digital access. For example, banking apps are central for financial transactions, making their perceived utility quite high to users, who tend to stick around due to their essential nature. Even if they are flawed, they’re necessary for day-to-day payments, which makes the cost of switching quite high for the customer.Travel apps, on the other hand, are used less frequently and have an extremely low cost of switching, so they need to make the most of moments when users find them useful during travel transactions.This means that travel apps face a unique challenge because of the sporadic nature of travel. They need to stay relevant, provide value, and meet the varying needs of users in the digital era.To tackle this, travel apps can go beyond just selling tickets. By being part of the entire travel journey — from planning to arrival and beyond — we can keep users engaged and build lasting connections.Owning the Complete Customer Journey Strategies to dominate the entire customer journey involve a comprehensive understanding of customer needs and transitioning from being a mere flight partner to a comprehensive travel partner.This evolution involves observing and addressing customers’ unmet needs throughout their journey, ultimately providing value beyond the basic flight booking. Expanding service offerings becomes key in this approach. Statistics show the rise in last-minute bookings, indicating a shift in consumer behaviour.By catering to these evolving needs, we can become indispensable travel companions, offering services like hotel bookings, Wi-Fi access, activities, and car rentals seamlessly.Effective Customer Engagement Strategies Tailoring engagement strategies based on the average lead time from booking to departure and return is essential. For shorter lead times, a focused approach on immediate post-booking engagement is crucial, while longer lead times allow for a more gradual and tailored engagement process.This is what brings customers back to the app. To maintain customer interaction between transactions, we must leverage various channels effectively. Utilizing push notifications, gamification, nudges, app messaging, and emails strategically helps in building awareness, enticing customers to explore offers, and bringing them back for continuous learning and engagement.Takeaway The travel sector’s digital transformation in the aftermath of the pandemic has shown resilience and adaptability.However, to continue thriving, we have to develop strategies to connect with customers and provide great experiences. Success today boils down to having a strong online presence, grasping how consumers behave, and adopting a holistic approach to engaging customers.Looking ahead, the industry’s future in the digital era hinges on how well we manage these trends.If you want to learn more about these trends and gain deeper insights into the transformations happening within the travel industry, be sure to tune into the full episode of Behind the Growth!Find it here:Transforming the Travel Experience
How Digital Innovation is Transforming the Travel Experience
Good Design = Good Business. (Thomas Watson Jr., Former IBM CEO)Design is not just about look and feel. It’s about how — and how well — it works. It’s about creating a competitive advantage for your brand.For the first time, we brought together a panel of design experts for a live session to answer all your questions about UX Research, Self-Serve Journeys, UI/UX, Prototyping, Design Systems, and more. Tune in to this insightful open conversation and learn from what our panel shared.
Ask A Designer: Live AMA Session with a Panel of Design Experts
Following our exploration of the Developer Scoring Mechanism in the previous blog, we now turn our attention to the final piece of the puzzle: forming the optimal team to execute the project. Traditional team formation processes are often ad-hoc and influenced by personal biases, leading to suboptimal outcomes. This blog explores the third AI accelerator: Team Formations, which aims to bring precision and efficiency to team composition.The Challenge for AI Team formation is a critical aspect of project planning. It involves selecting the right mix of skills, balancing resource availability, and ensuring cost-effectiveness. However, this process is often fraught with challenges. Traditional methods rely on personal judgment and experience, which can lead to biases and inefficiencies. Moreover, the dynamic nature of project requirements and resource availability adds further complexity.AI-driven Team Formations address these challenges by leveraging data from previous accelerators, HR systems, and costing tools. By analyzing this data and applying Machine Learning (ML) algorithms, the system suggests the optimal team configuration for a project.How it Works This accelerator uses data from the WBS and Developer Scoring Mechanism to suggest the best team composition for a project. It considers factors like skill sets, availability, and cost to propose the most effective team. The process involves several steps:Resource Availability: The system integrates with HR systems to check resource availability. This ensures that only available resources are considered for the project. Cost Analysis: Using data on resource costs, the system suggests budget-friendly teams. This involves analyzing hourly rates, project timelines, and overall budget constraints. Team Suggestion: The AI suggests the optimal team configuration based on project requirements. This involves balancing skills, experience, and availability to form the most effective team. Feature Breakdown: Under The Hood Data Integration: The system pulls data from HR, cost management, and performance scoring systems. This comprehensive data collection ensures that all relevant factors are considered. Algorithmic Analysis: ML algorithms evaluate potential team configurations based on various criteria, including skill sets, availability, and cost. Team Suggestions: The system proposes teams that balance skills, cost, and availability. This ensures that the project is executed by the most suitable team. Project Planning: The accelerator generates project plans and Gantt charts for the chosen team. This provides a clear roadmap for project execution, ensuring alignment and coordination among team members.Accelerator Benefits Deploying this accelerator ensures that project teams are not only well-balanced in terms of skills but also cost-effective. Industry statistics indicate that optimal team formations can lead to a 15-25% increase in project efficiency. Some of the key benefits include:Reduced Project Costs: By selecting budget-friendly teams, the accelerator helps in reducing overall project costs. This involves optimizing resource allocation and minimizing wastage. Improved Project Timelines: Well-balanced teams are more efficient, leading to improved project timelines. This ensures that projects are delivered on time, meeting client expectations. Enhanced Project Success Rates: AI-driven team formations lead to better alignment of skills and project requirements, resulting in higher project success rates. Business Impact The business impact of this accelerator is significant. By forming the best possible teams, companies can deliver projects more efficiently and effectively, leading to increased client satisfaction and better market competitiveness. According to a study by Harvard Business Review, companies that use AI for team formation report a 20% increase in project success rates and a 15% reduction in project costs.As an example, we helped a multinational corporation implementthe Team Formation Accelerator and reported a 30% improvement in project delivery times and a 25% increase in project success rates. These improvements were attributed to better team alignment, optimized resource allocation, and enhanced project planning.Ending Note In conclusion, the Team Formation Accelerator powered by AI represents a significant advancement in project management. By leveraging data from previous accelerators and applying sophisticated ML algorithms, it suggests the optimal team configuration for a project. This leads to reduced project costs, improved project timelines, and enhanced project success rates. As the industry continues to embrace AI, tools like this accelerator will become indispensable for project managers seeking to optimize team formation and drive project success.Looking Ahead This concludes our blog series on AI accelerators for project management. We hope this series has provided valuable insights into how AI can revolutionize the planning and execution of software development projects. Stay tuned for more in-depth explorations of emerging technologies and their applications in project management.
AI-based Accelerators Series: Forming Optimal Teams with AI
The impact of Artificial Intelligence is rapidly expanding, reshaping industries and redefining how we approach creativity. For designers, this evolution presents both exciting opportunities and complex challenges.As AI-driven tools become integral to the design process, designers face new challenges in their work.From design tools to ethical concerns, our design team was posed the following questions:Question 1What’s the first thing that comes to mind when talking about AI? Name: Véronique JanosyTitle: Lead Product DesignerAnswer: Frankly, I’m suffering from AI fatigue. It seems everyone is talking about where they can use AI, without really addressing whether we should be using it. I liken it to getting a new hammer and looking around desperately for nails to hammer but ignoring the fact that we might not actually need to hammer anything in at all.A hammer is innocent enough, though, and with AI we do have to contend with ethical questions that arise around its use, which relatively few people seem to be addressing.My fear is that once the novelty wears off (and I believe that much of its popularity right now is due to its novelty), we will have to grapple with the fallout of not having been cautious enough in the first place.Name: Adriano RenziTitle: UX ResearcherAnswer: AI and Machine Learning have been well adopted in recommendation systems since Amazon started to use hybrid recommendation system, reaching great achievements on the e-commerce industry, becoming the sparkle for experimentation with music categorization and patterns association (Pandora), leading to a complex new era of music (entertainment in general) recommendation.I would say that the state of the art in recommendation systems today has evolved to behavioral prediction systems, which certainly can be used (and has been used) to influence society on political decisions and makes us uneasy about the future.On the generative side, the natural programmed machine learning through patterns, have been appropriating graphic and textual material from real people to generate new products, which unfortunately is affecting the market and rising a new educational paradigm in a bad way – in the name of saving expenses.In many areas of expertise, it has shown to be a tool for the mediocre professional to improve their results.Name: Ram KumarTitle: UX ResearcherAnswer: Prompts.Love or hate it, AI is not a fad, and it is here to stay in some form. It is better to befriend it to empower us than to detest it as a force of evil.That said, prompts are how we communicate with AI tools. It would be good to grasp AI prompting techniques and try them out with tools to see what works and what does not.It is also an excellent way to learn how we and the AI models reason and respond. Think, ‘Her!’ I find these tips on effective prompting quite useful.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer: Since ChatGPT went mainstream at the beginning of 2023, we have witnessed how rapidly “AI” has become a household term.When Artificial Intelligence is mentioned, I immediately think, “This is the new reality,” and I feel the urgency to embrace it to fully harness its benefits.Like any tool at our disposal, AI can be used for both good and bad purposes. I am also reminded of Hollywood movies like “Her” and “Ex-Machina,” which show that our fascination with AI is not new but a topic we have been contemplating for decades.Name: Osama NadeemTitle: Senior Product DesignerAnswer: The first thing that comes to mind and how I’ve used AI, is that of an Assistant.I’ve found AI to be helpful in solving even some design scenarios and edge cases, and it’s very interesting to see that AI is being ingested everywhere – from apps to operating systems and hardware systems.Question 2Have you explored or experimented with any AI tools? Name: Véronique JanosyTitle: Lead Product DesignerAnswer: I have only played for a few minutes with the GenAI built into Adobe Illustrator and Photoshop, to satisfy my own curiosity.In my professional work, however, the client I work for is very restrictive about the tools we use, so I have not used it there.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer: Yes, I’ve experimented with several AI tools. Midjourney and DALL-E have been particularly fascinating for generating unique visual content and concept art, which can be a huge asset during the initial stages of design ideation or to complement a blog post. I’ve also explored Microsoft’s Copilot and Anthropic’s Claude for coding assistance.Furthermore, I’ve created a few GPTs for OpenAI’s ChatGPT store, tailored for specific design and productivity needs.I’m also looking forward to the release of Figma AI, anticipating its potential to further streamline and enhance the design process for UX, UI and Product designers.Name: Ram KumarTitle: UX ResearcherAnswer: Yes, I have been using Grammarly extensively to ensure my writing is on point and have experimented with Grammarly AI tools to change my tone and improve the impact of my writing.I got a ChatGPT subscription a couple of months ago and signed up for an AI boot camp to learn about building products using a combination of No-code tools and ChatGPT. It has been fascinating so far.However, it can be a bit of a rabbit hole, and it would be helpful to time-box your efforts if you plan to dip your toes in trying AI tools.Name: Adriano RenziTitle: UX researcherAnswer: Yes, I experimented with Mdjourney, Dall-E, ChatGPT and Grammarly on different occasions, mostly in an exploratory learning sense. But I haven’t pursued them on a regular basis usage.Name: Osama NadeemTitle: Senior Product DesignerAnswer: I’ve explored many tools – Midjourney, DALL-E, ChatGPT and other AI based plugins for Figma. I’m really looking forward to Figma AI though, I believe it would be very interesting to play around with AI meant for UX/UI design, in a tool that we love.It would eliminate all the third party plugins that are marketed as AI but they’re hardly there.Question 3If you have worked with or experimented with any AI tools, what have you found useful? Name: Véronique JanosyTitle: Lead Product DesignerAnswer: Honestly, I haven’t found anything to be useful. In my personal exploration with GenAI, I’ve found the results to be so poor that I would never use it for anything. Both in Illustrator for icon creation, and in Photoshop for generative fill (to complete a cropped image, for instance), results were strange, unnatural, and would have taken massive amounts of tweaking to achieve anything resembling what I’d need.With that much work, I would much rather browse the internet for inspiration and then use traditional Illustrator and Photoshop methods to achieve the results.The only use I can find for AI in my work might be for doing repetitive tasks, calculations, batch renaming etc., but scripts exist, so I would question why we would want to use AI instead.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer: AI tools have really surprised me with what they can do. Midjourney and DALL-E, for example, can create incredible visuals from just a text prompt that can be used for quick ideation or as inspiration. ChatGPT has been great for bouncing ideas around and getting new perspectives, almost like having a brainstorming buddy available 24/7.I have also learned to appreciate how AI can help in the early stages of research and can help me come up with solutions or ideas that I might not have thought of on my own.As of late, using Adobe’s Firefly within Photoshop and Illustrator has proven quite helpful when modifying images that just need small but specific visual changes.Name: Ram KumarTitle: UX ResearcherAnswer: AI tools can be a good starting point to brainstorm ideas or learn about topics or tools one wants to become familiar with. AI responses can be wordy, so it is helpful to break down the questions into parts or even ask the AI tools to list the response it provides in steps and ask for explanations about each step.I found the step-by-step instructions provided by ChatGPT helpful in learning how to use no-code tools for a basic application or a landing page.Name: Adriano RenziTitle: UX researcherAnswer: The only AI tool I found useful for my needs was Grammarly to check errors on my papers, but with a very attentive review of making sure the meanings would not be distorted (which happened a few times).Nevertheless, I do see non-visual professionals using generative AI to create design and visual posts, instead of hiring a visual artist, corroborating my answer for #1: “it has shown to be a tool for the mediocre professional to improve their results”Name: Osama NadeemTitle: Senior Product DesignerAnswer: If I think about tools, it’s just ChatGPT which I’ve found usable. Other tools right now, especially the ones for images and illustrations are mostly useless for the purpose of UX/UI design.For art however, Midjourney and DALL-E are pretty fascinating.Question 4Has AI impacted your work? Name: Véronique JanosyTitle: Lead Product DesignerAnswer: Only insofar as I cannot escape talk of it, or announcements from apps including AI in their latest updates. AI will affect the industry in the near future, however, as designers will be required to learn certain AI wrangling skills or risk being passed up for employment opportunities.I also fear that as companies start using AI for a variety of tasks, designers’ roles will change to include “fixing” aberrations or mistakes made by AI.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer: AI has definitely made my work faster and more efficient. Tasks that used to take days or weeks can now be done in a fraction of the time. This means I can take on more projects and push creative boundaries further than before.Tech advancements are also keeping everyone in the industry on their toes, constantly learning and adapting to new tools and methods. I have had to pick up new skills, like prompt engineering and using AI-driven insights for user research, which has been both challenging and rewarding.AI isn’t about replacing jobs; it is about enhancing what we can do. People who know how to use AI effectively are the ones who will really stand out in the job market.Overall, AI has been a huge growth opportunity for me, pushing me to innovate and stay ahead in a fast-paced field.Name: Ram KumarTitle: UX ResearcherAnswer: Not yet, at least not directly. However, this is likely to change in the coming months.There are valid concerns about the type of data AI tools can store and utilize.As a researcher, it’s crucial to consider these tools for tasks like extracting a quote from a specific participant in a usability test. It’s essential that we engage in discussions and implement safeguards to ensure the data and the individuals involved are treated with the utmost respect and privacy.Name: Adriano RenziTitle: UX ResearcherAnswer: As a researcher, no. The pattern mapping done by AI is very simple and high level. Similar results from a PO running a survey and presenting percentage results – it can be very misleading. But I think it can be useful to find specific parts of a transcript if needed, and obviously save a lot of time.As an artist, yes. Customers are looking more and more for the human-made real thing and interested in personal views of the world.Nevertheless, illustrators that work with realism painting are getting impacted negatively by AI, as industry sees AI to save moneyName: Osama NadeemTitle: Senior Product DesignerAnswer: AI has an impact but not a lot right now. I frequently visit ChatGPT for general assistance, brainstorm ideas or improve my writing/content.I’ve never used AI to create illustrations or icons because that seems like a lot of work, especially when you must tailor those assets – which cannot be accurately achieved by changing your prompt.
Ask A Designer Round 7: AI & Design
Following the discussion on the Work Breakdown Structure (WBS) accelerator in our previous blog, which you can read here, we now turn our attention to the next crucial step in project planning: understanding and scoring the capabilities of the development team. Traditional performance management systems often rely heavily on subjective judgment and lack real-time, data-driven insights. This can lead to misalignment of resources, inefficiencies, and missed opportunities for optimization. This blog delves into the second AI accelerator: the Developer Scoring Mechanism, which aims to bring precision and objectivity to developer performance evaluation.The Need for AI Effective project execution hinges on the capabilities and performance of the development team. However, accurately assessing and scoring developer performance is a complex task. Traditional methods involve periodic performance reviews, often biased by personal judgment and limited by infrequent evaluation periods. These methods fail to provide a continuous, data-driven view of developer performance, making it challenging to make informed decisions about team composition and resource allocation.The AI-driven Developer Scoring Mechanism addresses these challenges by leveraging data from tools like Jira, GitHub, and SonarQube. By analyzing this data and applying sophisticated Machine Learning (ML) algorithms, the system provides an objective, real-time performance score for each developer.How it Works This accelerator utilizes data from project management and code repositories, combined with ML models, to generate accurate performance scores. The process involves several steps:Data Collection: Gather data from tools such as Jira, GitHub, and SonarQube. This data includes metrics like ticket resolution times, code quality, number of commits, and peer reviews. Data Processing: Use Large Language Models (LLMs) to fill in any missing data and standardize the information. Scoring Algorithm: Apply ML models to analyze the data and generate a performance score for each developer. This score reflects various performance metrics and provides a comprehensive view of the developer’s capabilities. Feature Breakdown: Under The Hood Data Integration: The system collects and integrates data from multiple sources, including Jira, GitHub, and SonarQube. This comprehensive data collection ensures that all relevant performance metrics are captured. Data Normalization: LLMs and ML models standardize the data, filling in gaps where necessary. This step ensures that the data is clean, complete, and ready for analysis. Performance Scoring: The AI generates a score based on various performance metrics, such as code quality, bug resolution time, and task completion rate. This score provides an objective measure of a developer’s performance. Dashboard: Scores are displayed on a user-friendly dashboard, providing insights into individual and team performance. This dashboard allows project managers to quickly identify high-performing developers and make informed decisions about team composition.Accelerator Benefits Implementing this accelerator allows project managers to make informed decisions about team composition and identify high-performing developers. Industry statistics indicate that accurate performance assessment can improve team productivity by 20-30%. Some of the key benefits include:Objective Performance Evaluation: The system provides an objective measure of developer performance, reducing the potential for bias. This leads to fairer and more accurate assessments. Real-Time Insights: The AI-driven approach provides real-time insights into developer capabilities, allowing for timely interventions and adjustments. Enhanced Team Formation: By understanding the strengths and weaknesses of individual developers, project managers can form more balanced and effective teams. This leads to improved project outcomes and higher team morale. Business Impact The business impact of this accelerator is profound. By providing accurate and objective performance scores, it enables better resource management and project execution. According to a study by McKinsey, companies that use data-driven performance management practices are 23% more likely to outperform their competitors in terms of project success. Additionally, a survey by Deloitte found that organizations using AI for performance management reported a 45% increase in employee satisfaction.In our experience, the implementation of the Developer Scoring Mechanism at a large telco client led to a 25% improvement in project delivery times and a 30% reduction in project costs. These improvements were attributed to better team formation, optimized resource allocation, and enhanced project monitoring.Ending Note In conclusion, the Developer Scoring Mechanism powered by AI represents a significant advancement in performance management. By leveraging data from various sources and applying sophisticated ML algorithms, it provides an objective, real-time view of developer performance. This leads to better team formation, improved project outcomes, and enhanced resource management. As the industry continues to embrace AI, tools like this accelerator will become essential for project managers seeking to optimize performance and drive project success.Up Next In the next blog, we will explore the third AI accelerator: Team Formations. This tool leverages AI to suggest the optimal team configuration for a project, considering factors such as skill sets, availability, and cost. By doing so, it ensures that project teams are well-balanced and efficient, leading to improved project outcomes.
AI-Based Accelerators Series: AI for Precision Developer Scoring
Introduction The Canadian banking sector is at a crossroads. On one hand, it boasts a global reputation for stability and security. On the other, it faces pressure to embrace rapid digital innovation and keep pace with evolving customer expectations.To learn more about the shift, I interviewed Nelson De Jesus, SVP and CIO of Personal and Business Banking & Direct Financial at CIBC. With over two decades in the financial sector, Nelson brings a unique perspective on how Canadian banks can balance their traditional strengths with the quickly changing needs of a digital-first world.Here’s Nelson’s perspective on the four key areas trending in the Canadian banking sector:Trend 1: The Rise of Experiential Banking The more in tune we get with client preferences, the more we can interface with our clients... those relationships don't have to be human. They can be digital. I think the better the products will be able to offer, the better we will tailor our digital experience to client expectations.Customers are no longer satisfied with simply completing transactions at their bank. They expect their financial institutions to understand their individual needs, anticipate their financial goals, and offer personalized solutions that seamlessly integrate with their lives. This is driving the shift toward what Nelson calls “experiential banking,” a trend that reimagines the entire banking journey.“It’s a process,” Nelson explained. “And you know, I think like everything else, you begin with ‘let’s go 100% digital.’ That’s not the right answer.” He believes that the key to experiential banking lies in striking the right balance between digital convenience and human connection. This means offering a hybrid approach that combines the efficiency of digital tools with the personalized guidance of human advisors, allowing customers to choose the level of interaction that best suits their needs.For example, CIBC’s mobile app now goes far beyond basic account management. It provides personalized financial advice based on individual spending patterns and goals, offers insightful nudges to help customers save more effectively, and even allows you to deposit cheques.This approach reflects a deeper understanding of how different customers interact with their finances, acknowledging that a one-size-fits-all digital solution simply won’t cut it.Nelson emphasized the need to understand customer preferences at a granular level, moving beyond broad demographics to create truly personalized experiences. “If you can offer a better experience, people will take it,” he said. Experiential banking is about providing that better experience — one that not only meets customers’ functional needs but also anticipates their desires, reduces friction, and fosters a sense of trust and engagement.Trend 2: Open Banking’s Slow but Steady Progress While Open Banking has been making headlines globally, its implementation in Canada has been a more gradual process. Nelson acknowledges the potential of Open Banking to create a more competitive and innovative financial landscape, but he also expresses a degree of caution.“I’m very positive [about Open Banking], but I’m also very skeptical as to the timeframe,” he admitted. The regulatory landscape in Canada is still evolving, and Nelson pointed to the relatively low adoption rates in other jurisdictions as a potential challenge. “In the UK, client buy-in is sub-20%, meaning 80% of people have not registered to take part in it,” he shared.Despite the uncertainties, Nelson believes that the industry is moving in the right direction. “To the extent that [Open Banking regulation] obligates us to run a better bank, I think it’s good. I think it’s good for everybody,” Nelson stated.Open Banking holds the promise of empowering customers by giving them more control over their financial data and enabling them to access a wider range of financial products and services. However, its success in Canada will depend on a coordinated effort between regulators, financial institutions, and technology providers to build a secure, transparent, and user-friendly ecosystem.Trend 3: Generative AI’s Potential and Challenges Generative AI is one of the most talked-about technologies, and its potential to revolutionize industries — including banking — is undeniable. Nelson is particularly excited about the possibilities of generative AI, seeing it as a far more transformative force than blockchain or cryptocurrencies.“With AI, there’s a general familiarity with modeling. So, people understand … what it does. It looks for big patterns of data … and you’re basically testing that hypothesis,” he explained. Generative AI takes this a step further by using those data patterns to create new content, predictions, and insights. Nelson believes that generative AI can fundamentally change how banks interact with their customers.However, he acknowledges the ethical considerations and potential risks, especially when it comes to “hallucinations” — instances where AI generates outputs that are nonsensical or even harmful. “How do we now prevent those fatalistic scenarios from playing out like once you get to the point where, you know, it is that powerful? Have you crossed the line that makes it too powerful?”Despite these challenges, Nelson is optimistic about the future of generative AI in banking. He believes that with careful planning, robust governance structures, and a focus on responsible implementation, this technology can enhance customer experiences, improve decision-making, and create new value across the financial ecosystem.Trend 4: The Evolution of Mega Platforms The rise of mega platforms like Google, Amazon, and Microsoft is reshaping industries across the board, and banking is no exception. Nelson sees these partnerships as both a significant opportunity and a potential challenge for traditional financial institutions.He highlights the ability of mega platforms to innovate rapidly and adapt to changing market demands. He pointed to the evolution of Microsoft Teams as a prime example, noting how the company quickly pivoted from the aging Skype platform to create a solution that met the surging demand for video conferencing during the pandemic.Nelson also emphasized the strategic importance of partnerships, particularly in the field of AI. “Now they’ve signed up with OpenAI; I think it’s been a very fruitful partnership for them,” he observed. By collaborating with specialized AI companies like OpenAI, mega platforms can accelerate their development of new products and services, gaining access to cutting-edge technologies and expertise.However, these partnerships also present challenges. Nelson questioned whether mega platforms can consistently deliver “commercial-grade” solutions that meet the stringent requirements of the financial industry. “If I’m going to buy this service, then I want to be sure that they’re not going to turn it off tomorrow,” he cautioned.The evolution of mega platforms will undoubtedly continue to shape the banking landscape. Financial institutions will need to carefully evaluate the benefits and risks of these collaborations, ensuring that they maintain control over critical data and processes while leveraging the innovation and scale that mega platforms can provide.Takeaway The future of Canadian banking belongs to those who can strike the right balance between embracing innovation and preserving the trust that customers have come to expect. It’s a delicate act, but one that holds immense potential to create a more dynamic, customer-centric, and ultimately valuable financial ecosystem.If you found these trends insightful, be sure to tune into the full episode of Behind the Growth for a conversation you don’t want to miss! You’ll hear even more from Nelson De Jesus as he shares his deep expertise and perspectives on the future of Canadian banking.Find it here:Leadership and Innovation in the Banking Sector
Mega Platforms, Gen-AI, Open and Experiential Banking in Canada
Introduction Okay, let’s be honest. How many times have you fumbled with those tiny SIM cards, trying to pry them out of your phone with a paperclip? Or worse, completely lost one right when you needed it most? We’ve all been there, frustrated by those fiddly physical SIMs in a world that demands constant connectivity.But what if I told you there’s a better way? A way to switch carriers, activate new devices, and even travel internationally without ever having to touch a physical SIM card again?That’s where eSIMs come in. And trust me, once you understand how they work, you’ll wonder how we ever lived without them.I recently had the pleasure of interviewing Larry Baziw, a seasoned telecom expert, to celebrate his incredible journey in the industry. Larry’s been at the forefront of mobile technology for decades, and he walked me through the exciting world of eSIMs, explaining how they’re poised to revolutionize the way we connect.In this blog, I’ll share everything I learned from Larry, breaking down the complexities of eSIMs in simple terms so you can understand how they work and why they matter.What Is an eSIM? Now that you know eSIMs offer a more convenient way to connect, let’s dive deeper into what they actually are. As Larry explained it, an eSIM, or Embedded Subscriber Identity Module, is essentially “a tiny chip embedded directly into your device, replacing the traditional, removable SIM card.” It’s a small, rewritable chip that stores your network information digitally, eliminating the need for those cumbersome physical SIM cards.The magic of eSIMs lies in their software-based nature. Larry pointed out the key distinctions between eSIMs and their physical counterparts:eSIM vs. Physical SIM: The Software Advantage Software-Based: eSIMs operate entirely in the digital realm, storing your carrier information as software profiles. Imagine downloading a carrier “app” directly onto your phone — that’s essentially what an eSIM does. Remote Provisioning: No more trekking to the carrier store for a new SIM card. With eSIMs, you can download and activate a carrier profile directly from your device settings — no physical swapping required. How eSIMs Work: Multiple Profiles and Over-the-Air Magic To understand how this all works in practice, let’s break down two key features of eSIM technology:Multiple Profiles: eSIMs can store multiple carrier profiles simultaneously, giving you the flexibility to switch between them seamlessly. Larry compared this to “switching between apps on your phone”. Imagine having profiles for your work line, personal line, and even international carriers, all ready to go at a moment’s notice. Over-the-Air (OTA) Activation: Activating a new carrier profile is as simple as selecting it from your device settings and letting it download over the air. No special equipment or technical knowledge required — it’s all handled seamlessly through software updates. Benefits of eSIMs? What it comes down to is one fundamental security issue: something you know (your carrier subscription) and something you have (the physical SIM card). As long as we keep that going, we can keep customers' identities secure.Okay, so eSIMs sound pretty cool, right? But how do they actually make your life easier and more connected? Larry had a lot to say about the benefits of eSIMs.Convenience: Say Goodbye to SIM Card Struggles Seamless Switching: One of the most obvious advantages of eSIMs is the ease with which you can switch carriers. No more fiddling with SIM card trays or waiting for a new card to arrive in the mail. Larry shared, “And you can start to understand how easy it was to just sit at home and activate your new subscription service and not have to wait for something physical to be mailed to you or to have to drive somewhere to pick up a physical card.” Effortless Device Setup: With eSIMs, setting up a new phone or tablet becomes a breeze. You can activate your service and start using your device almost instantly. Remote Management: Imagine being able to manage your entire mobile subscription from the comfort of your couch. With eSIMs, you can. Change plans, update your information, and even troubleshoot issues remotely, without ever having to step foot in a carrier store. Flexibility: Your Connectivity, Your Way Multiple Lines, One Device: Juggling multiple phones for work and personal use? eSIMs let you consolidate. Store both profiles on a single device and switch between them seamlessly, depending on which number you need to use. Global Connectivity: Traveling internationally has never been easier. Instead of scrambling to find a local SIM card at the airport (or paying exorbitant roaming charges), you can activate an international data plan directly on your eSIM, often at a fraction of the cost. Larry even shared his own experience with this, saying, “Yeah, traveling, getting an eSIM for — I wanted a roaming package for data, and I’ve never met the operator. Obviously, it was all digital. I bought it online. It was an eSIM experience. There’s just so much to the whole experience that it absolutely makes sense. Why would we not have it?” Device Design & Security: A Win-Win Sleeker Devices: eSIMs free up valuable space inside your devices, allowing manufacturers to create thinner, lighter, and more stylish phones, tablets, and wearables. Enhanced Security: Believe it or not, eSIMs can actually be more secure than traditional SIM cards. Because they’re embedded and not physically removable, they’re much harder to steal or tamper with. eSIMs in Action: Beyond Smartphones You might be thinking, “Okay, eSIMs sound great for my phone, but what else can they do?” Well, get ready to be amazed, because Larry discussed a whole world of eSIM applications that extend far beyond our smartphones.The IoT Revolution: Connecting Everything eSIMs are a driving force behind the rapidly expanding Internet of Things (IoT), enabling seamless connectivity for a vast array of devices across industries.Industrial Automation: Imagine factories where machines can communicate with each other, self-diagnose problems, and even order replacement parts automatically. That’s the power of eSIMs in industrial automation. They allow for remote monitoring and control of equipment, optimizing efficiency and reducing downtime. Connected Cars: Your next car might be equipped with an eSIM, providing access to navigation, entertainment, emergency services, and real-time diagnostics. eSIMs are making cars smarter, safer, and more connected than ever before. Smart Cities: eSIMs are playing a critical role in building smarter, more efficient cities. They’re connecting infrastructure like traffic lights, parking meters, and public transportation systems, allowing for better management, resource optimization, and improved citizen services. Wearables and Beyond: A World of Possibilities eSIMs aren’t just transforming industries; they’re also changing the way we interact with our personal devices.Seamless Integration: Larry pointed out that eSIMs were actually adopted in machine-to-machine (M2M) and IoT devices long before they became common in smartphones. He said, “When I look back from the Canadian perspective, eSIMs were introduced into sort of machine-to-machine and IoT services long before they came into consumer hands.” This early adoption in wearables has led to a seamless integration of eSIM technology in devices like smartwatches and fitness trackers, giving them standalone connectivity. Emerging Applications: The potential applications for eSIMs are seemingly endless. Think about it: Larry even posed this question: “Could you imagine a day when you buy a TV for your home and it comes with a SIM slot? What does that mean?” He believes that eSIMs could revolutionize everything from payment systems and digital IDs to home appliances and entertainment systems. The Future Is Connected The future of eSIMs is truly exciting. Here’s why:A Growing Ecosystem: Embracing the eSIM Revolution The adoption of eSIM technology is accelerating at an incredible pace. More and more device manufacturers, from smartphone giants to wearable innovators, are integrating eSIMs into their latest products. And network operators around the world are rapidly expanding their eSIM support, recognizing the convenience and flexibility it offers their customers.Universal Profiles: The Dream of Seamless Connectivity Universal eSIM profiles allow for truly seamless connectivity across devices and networks. Imagine a world where you can activate any device on any compatible network with a single eSIM profile. No more carrier restrictions, no more regional limitations — just instant, global connectivity at your fingertips. While we’re not quite there yet, the industry is moving in that direction, and the possibilities are endless.Takeaway The future of connectivity is here, and it’s embedded. eSIMs are transforming the way we connect, making our lives easier, more flexible, and more connected than ever before.Want to dive deeper into the world of eSIMs and hear more from telecom expert Larry Baziw? Be sure to check out our full conversation on the Behind the Growth podcast. You won’t want to miss Larry’s insights on the future of this game-changing technology!Find it here:The Rise of eSIMs in the Tech Ecosystem
Exploring eSIMs and Its Multi-Industry Use Cases
The integration of AI, especially NLP, in Back-End Development is a growing trend because of its advanced solutions compared to traditional methods. Learning and implementing NLP models can significantly improve the efficiency and accuracy of text processing tasks.This webinar on Natural Language Processing (NLP) for Back-end Development will help you stay current with cutting-edge practices and learn how to implement NLP in Back-End Systems.Expertly tailored for BE Developers, Tech Leads, Solution Architects, Data Scientists, and AI Enthusiasts, this webinar is a must-watch if you want to gainUse Cases: Specific use cases where NLP can be applied in Back-End development to replace traditional methods. Best Practices: How to integrate NLP models into existing Back-End systems, including best practices and common pitfalls. Tools & Libraries: An overview of key tools and libraries for NLP integration in Back-End development. Real Examples: Case studies and examples of how NLP has been successfully used in backend development to solve real-world problems. Future Trends: Insights into future trends in AI and backend development, and how NLP is expected to evolve and impact the field. Don’t let outdated methods hold you back. Watch now to learn more!
Natural Language Processing (NLP) for Back-End Development
The planning phase of the software development lifecycle (SDLC) is often riddled with challenges and uncertainties. It’s a phase where meticulous attention to detail and precise estimations are crucial. Yet, it’s also the phase most susceptible to human error and misjudgment. In many cases, these errors lead to project delays, cost overruns, and resource misallocation. For instance, consider a CRM application project initially estimated to take one year but ultimately stretched to three years due to unforeseen complexities and a lack of proper documentation and team alignment.These common issues in project planning have driven the need for more advanced, reliable solutions. Enter AI-based accelerators, specifically designed to streamline and enhance the planning process. This blog is the first of a three-part series and will focus on the first of these accelerators: creating Work Breakdown Structures (WBS) for business requirements using AI. The upcoming parts of this blog series will focus on Developer Scoring Mechanism and Team Formations.The Role of AI Artificial Intelligence (AI) has the potential to revolutionize the way we approach WBS creation. Traditionally, WBS creation involves breaking down a project into smaller, manageable tasks, estimating the effort required for each task, and ensuring that these tasks align with the overall project goals. This process is often manual, time-consuming, and heavily reliant on the experience and judgment of project managers.By leveraging AI, particularly Large Language Models (LLMs) and Machine Learning (ML), we can automate and enhance this process. AI can process unstructured data from Business Requirement Documents (BRD), analyze historical data, and generate detailed WBS with precise effort estimations. This not only reduces the time and effort required but also improves the accuracy and reliability of the estimations.How it Works Input BRD: The process begins with the user providing the BRD. This document outlines the project requirements in detail. WBS Task Creation: The AI system, powered by LLMs, processes the BRD and breaks it down into smaller, specific tasks. This involves understanding the requirements and translating them into actionable items in the form of Epics and Stories. Effort Estimation: Each task is then assigned an effort estimation based on ML models trained on historical project data. This step involves vectorizing the tasks and matching them with similar tasks from past projects to determine the likely effort required. Feature Breakdown: Under the Hood Natural Language Processing: The AI system reads the BRD and identifies key components. Using Natural Language Processing (NLP), it extracts relevant information and structures it into a format suitable for further processing. Leveraging LLMs: The system breaks down the requirements into specific, manageable tasks. This step ensures that all aspects of the project are covered, and nothing is overlooked. ML Models: These models come into play to estimate the time and resources needed for each task. These models use historical data to provide accurate and realistic estimations. Integration: The system integrates with project management tools like Jira, allowing seamless transition from planning to execution. This integration ensures that tasks are not only well-defined but also easily manageable within existing project management frameworks.Accelerator Benefits Deploying this accelerator in a project management setting can bring about significant improvements in efficiency and accuracy. According to industry statistics, project delays and budget overruns are primarily due to inaccurate initial estimates. By using AI to generate WBS and effort estimations, businesses can:Reduce Planning Time: The time required to create a WBS and estimate efforts can be reduced by up to 50%. This acceleration in the planning phase allows projects to commence more quickly. Increase Estimation Accuracy: AI-driven estimations are more accurate and reliable, reducing the likelihood of project delays by up to 30%. This accuracy helps in setting realistic timelines and budgets. Enhance Resource Allocation: With detailed and accurate WBS, resource allocation becomes more efficient. This ensures that the right resources are assigned to the right tasks, improving overall project execution. Business Impact The business impact of deploying this accelerator is substantial. It not only saves time and reduces costs but also ensures that project teams are better prepared and more accurately aligned with project goals. This leads to higher project success rates and increased client satisfaction.For example, a study by the Project Management Institute (PMI) found that poor project planning is a leading cause of project failure, affecting 39% of projects. By integrating AI-driven WBS creation, companies can mitigate this risk, leading to more successful project outcomes. Additionally, Gartner predicts that by 2025, AI will play a role in the management of at least 50% of all projects, highlighting the growing importance and relevance of AI in project management.Summing It Up In conclusion, the WBS accelerator powered by AI represents a significant advancement in project management. By automating and enhancing the WBS creation and effort estimation process, it addresses many of the common challenges and uncertainties faced during the planning phase. This leads to more accurate planning, better resource allocation, and ultimately, more successful project outcomes. As the industry continues to embrace AI, tools like this accelerator will become indispensable for project managers seeking to improve efficiency and accuracy in their projects.Up Next In the next blog, we will dive into the second AI accelerator: the Developer Scoring Mechanism. We will explore how this tool leverages AI to provide precise, real-time performance evaluations of developers, helping project managers to make informed decisions about team composition and resource allocation.
AI-Based Accelerators Series: AI for Effortless Work Breakdown Structures (WBS)
Introduction Is your business feeling the heat when it comes to sustainability? You’re not alone. From evolving consumer expectations to stricter regulations, companies are under pressure to adopt more sustainable practices. But the path forward isn’t always clear.That’s where the power of AI comes in.I had the pleasure of interviewing Dr. Sedef Akinli Kocak, Director of Professional Development at the Vector Institute, to explore this topic. Dr. Kocak is a leading voice in the world of AI and its potential to address some of the world’s most pressing challenges — what she refers to as “wicked problems.”In this article, we’ll delve into Dr. Kocak’s insights on how AI is transforming sustainability, from optimizing energy use in data centers to mitigating climate risks using cutting-edge technologies. Get ready to discover how AI can help your business navigate the complexities of sustainability and pave the way for a brighter, more sustainable future.AI: A Catalyst for Sustainable Transformation Artificial intelligence, at its core, is about leveraging data to solve problems. AI systems excel at analyzing vast datasets, identifying patterns we might miss, and using those insights to make intelligent predictions. But how does this translate to real-world sustainability action?Think of it this way: almost every aspect of a business, from manufacturing processes to supply chains, relies on energy and resources. AI can help us understand these systems in ways we haven’t been able to before, optimizing them for efficiency and minimizing their environmental impact.Here are just a few ways AI is already making a difference:Energy Efficiency: Imagine a future where buildings automatically adjust their energy usage based on occupancy and weather patterns. Or where traffic flows smoothly, reducing congestion and emissions. AI is making these scenarios a reality. Resource Management: From smart grids that balance energy supply and demand to AI-powered recycling systems that sort materials with incredible accuracy, AI is helping us conserve precious resources and transition towards a circular economy. Climate Modeling & Prediction: AI is enabling scientists to build more accurate climate models, predict extreme weather events with greater precision, and develop strategies to mitigate risks and build resilience. Case Study: AI-Powered Data Center Optimization In today’s digital world, data is king. But this comes at a cost. Data centers, those massive hubs that power our online activity, consume enormous amounts of energy, making them a significant contributor to global carbon emissions.Dr. Kocak highlighted one solution during our conversation — a collaborative project between The Vector Institute and TELUS, a leading Canadian telecommunications company. Their goal? To leverage AI to reduce energy consumption in TELUS’s data centers. The project was inspired by DeepMind and Google System Energy Reduction using reinforcement learning.This type of AI, known as model-based reinforcement learning, essentially allows a system to learn and adapt in real time. It’s like teaching a computer to play a game, where it receives rewards for good decisions (energy savings) and penalties for bad ones (energy waste). Over time, the system becomes incredibly adept at optimizing energy usage based on factors like temperature, humidity, and even server load.And the results speak for themselves. “TELUS piloted this project,” says Kocak, “and the pilot results show that [there was] around 12% in reduced annual energy consumption in a small data center.”Imagine the impact of scaling this solution across more data centers and even other industries grappling with energy efficiency challenges. That’s the power of AI in action.Case Study: Satellite Imagery for Climate Risk Mitigation Climate change is no longer a distant threat; its impacts are being felt globally with increasing intensity. From severe storms to prolonged droughts, extreme weather events pose significant risks to communities, infrastructure, and businesses.AI is emerging as a powerful tool in our efforts to understand and mitigate these risks. One promising application involves the analysis of high-resolution satellite imagery.Dr. Kocak explains, “We work with companies using computer vision technique on satellite high-resolution images to co-develop a solution towards identifying and mitigating climate change risk.”Imagine AI systems capable of scanning vast geographical areas, identifying subtle changes in land cover, water levels, or vegetation health. This data can then be used to:Identify Vulnerable Areas: Pinpoint regions most susceptible to flooding, wildfires, or other climate-related hazards. Predict and Mitigate Risks: Develop early warning systems that alert communities and businesses to impending threats, allowing for timely preparation and response. Support Climate Resilience: Inform long-term infrastructure planning and resource allocation decisions, creating more resilient communities and businesses in the face of a changing climate. Navigating the Challenges of AI for Sustainability AI should benefit humans and the planet. AI systems must reflect the privacy and security interest of the individual.While the potential of AI to drive positive change in the realm of sustainability is undeniable, we must also acknowledge the potential pitfalls. Dr. Kocak emphasizes the importance of a thoughtful, ethical approach to AI development and implementation.One key area of concern is ensuring that AI systems are built on principles of trust and safety. As AI becomes more integrated into decision-making processes that impact our environment and society, transparency and accountability are paramount.“We at Vector, we recently released our trust and safety principles to help our ecosystem,” shares Kocak. These principles highlight the need for AI systems that:Benefit Humanity and the Planet: AI should be a force for good, addressing challenges in a way that aligns with our values and goals for a sustainable future. Reflect Democratic Values: AI development and deployment should be guided by principles of fairness, equity, and inclusivity, avoiding biases that could perpetuate existing inequalities. Prioritize Privacy and Security: As AI systems often rely on vast amounts of data, robust measures must be in place to protect individual privacy and prevent misuse of sensitive information. Navigating these complex issues requires a collaborative effort. Technologists, policymakers, ethicists, and domain experts must work together to ensure that AI is developed and deployed responsibly, maximizing its potential for good while mitigating potential harms.Takeaway The business case for sustainability is clear, and AI is rapidly emerging as a powerful tool in this critical journey. From optimizing energy use to mitigating climate risks, AI empowers companies to make smarter decisions for a healthier planet. By partnering with AI experts and embracing innovative solutions, businesses can unlock a more sustainable future, today.If you found this information insightful, tune into the full episode of Behind the Growth for a conversation you do not want to miss!Find it here:The Transformative Power of AI in Sustainability
How AI is Driving a More Sustainable Future for Businesses
[Toronto, Ontario] – mobileLIVE proudly announces its successful achievement of two significant certifications: ISO 27001 for Information Security Management and ISO 9001 for Quality Management Systems. These prestigious certifications reflect the company’s commitment to quality management and information security, reinforcing its dedication to providing exceptional products and services to its customers.ISO 27001 is an internationally recognized standard for information security management, signifying mobileLIVE’s commitment to protecting sensitive company and client information through rigorous policies and controls. This certification assures clients of mobileLIVE’s capability to manage information securely, thereby enhancing trust and confidence.Simultaneously, ISO 9001 certification underscores mobileLIVE’s dedication to efficiency and organizational excellence in delivering products and services. It validates our adherence to stringent quality standards, ensuring consistent customer satisfaction and continual improvement across all operations.“This dual certification is a testament to our unwavering dedication to excellence and security,” said Mohammad Siddiqui, Vice President Operations, PMO & Delivery. “It highlights our team’s hard work and commitment to maintaining the highest standards in everything we do.”mobileLIVE remains committed to innovation and customer satisfaction, leveraging these certifications to further enhance service delivery and operational efficiency.
mobileLIVE Achieves ISO 27001 and ISO 9001 Certifications, Demonstrating Commitment to Excellence
Introduction The financial services industry is on the cusp of a dramatic transformation, driven by the rapid advancements and growing influence of Artificial Intelligence (AI). Understanding and harnessing the power of AI is no longer a luxury but a necessity for banks seeking to remain competitive, deliver exceptional customer experiences, and thrive in an increasingly digital world.To shed light on what this future holds, I sat down with Yannick Lallement, Chief AI Officer at Scotiabank, one of North America’s largest financial institutions. With a PhD in Artificial Intelligence and a career spanning academia and the private sector, Lallement brings a unique perspective to the intersection of AI and banking.In this guide, we’ll draw upon Lallement’s insights to explore the potential impact of AI on the future of banking. We’ll examine key trends, explore emerging use cases, and provide practical considerations for financial leaders seeking to navigate this exciting and transformative period.The Evolving Landscape of AI in BankingWhile AI has already made inroads in automating back-office tasks and improving operational efficiency in banking, its future impact will extend far beyond these early applications.One of the most significant developments driving this evolution is the rise of Large Language Models (LLMs). These AI models, trained on massive datasets of text and code, possess an impressive ability to comprehend and generate human-like language. This capability makes LLMs particularly well-suited for tasks that were once considered the exclusive domain of humans, such as customer service, document analysis, and even strategic decision-making.As Yannick Lallement highlights, “What the LLMs can do is evolving pretty much, literally, every day.” This rapid evolution means that banks must continually adapt and embrace new possibilities to stay ahead of the curve.Lallement points out that the key to leveraging the power of LLMs lies in having “good source data.” As AI becomes more sophisticated and integrated into core banking functions, the ability to collect, manage, and analyze data becomes a strategic imperative. Banks that can effectively utilize their data will be best positioned to unlock the full potential of LLMs and gain a competitive advantage in the future of finance.Transforming the Customer Experience The future of banking is undeniably customer-centric. Banks that prioritize delivering exceptional, personalized customer experiences will be the ones that attract and retain customers in a competitive market. And AI, especially LLMs, has the potential to revolutionize how banks interact with and serve their customers.Imagine a banking experience where every interaction feels personalized, relevant, and tailored to your specific needs. AI is making this vision a reality. By analyzing vast amounts of customer data—transaction history, financial goals, risk tolerance, and even communication preferences—AI can help banks understand their customers on a deeper level than ever before. This knowledge empowers banks to move beyond generic financial products and services, offering hyper-personalized solutions that resonate with individual needs and aspirations.The rise of AI-powered chatbots and virtual assistants is already transforming customer service in banking. These intelligent systems can handle routine inquiries, answer questions, provide financial guidance, and even resolve issues 24/7. As these technologies become more sophisticated, they will become increasingly adept at understanding complex requests, offering personalized recommendations, and providing a seamless, intuitive customer experience across all channels.AI also allows banks to move beyond reactive customer service to a more proactive and predictive approach. By analyzing customer data, AI can anticipate needs, identify potential risks, and recommend relevant products or services before the customer even realizes they need them. This proactive approach not only enhances customer satisfaction but also strengthens relationships and builds trust.Revolutionizing Bank Operations The LLM is not going to replace a person. The question is always: how can you use an LLM to augment an existing employee?Beyond enhancing the customer experience, AI is poised to revolutionize how banks operate behind the scenes. From streamlining processes to uncovering hidden insights and empowering employees, AI offers the potential for greater efficiency, reduced costs, and improved decision-making across all areas of a bank’s operations.One of the most immediate impacts of AI in banking is the continued automation of repetitive, rule-based tasks. Processes such as loan applications, fraud detection, KYC (Know Your Customer) checks, compliance reporting, and risk assessment can be significantly streamlined with AI-powered solutions. This automation not only frees up valuable employee time for more strategic activities but also reduces errors, speeds up processing times, and ultimately lowers operational costs.The sheer volume of data generated in banking presents both a challenge and an opportunity. AI excels at analyzing these vast datasets, uncovering hidden patterns and trends that would be impossible for humans to detect. These data-driven insights can inform a wide range of decisions, from developing new products and services to optimizing marketing strategies and managing risk more effectively.AI isn’t about replacing human judgment; it’s about augmenting it. By providing bank employees with AI-powered tools that offer data-driven insights, predictive analytics, and real-time risk assessments, AI can empower them to make faster, more informed decisions.This means loan officers can assess creditworthiness more accurately, financial advisors can offer personalized investment recommendations, and risk managers can identify and mitigate potential threats more proactively.Navigating the Future of AI in BankingThe transformative potential of AI in banking is undeniable, but harnessing that potential requires more than simply implementing the latest technologies. Banks must adopt a strategic, forward-thinking approach that embraces innovation, invests in talent, and addresses the ethical considerations surrounding AI deployment.First and foremost, banks must cultivate a culture that embraces innovation and continuous learning. This means fostering an environment where employees are encouraged to explore new technologies, experiment with AI solutions, and view change as an opportunity for growth. Leadership must champion AI initiatives, providing the necessary resources and support to drive adoption across the organization.As AI becomes increasingly integrated into banking, the skills needed to thrive in this new landscape will evolve. Banks must invest in their workforce, providing training and upskilling opportunities to equip employees with the knowledge and expertise to work effectively alongside AI. Attracting top talent with backgrounds in AI, data science, and related fields will also be crucial to driving innovation and staying ahead of the curve.The increasing reliance on AI in banking raises important ethical considerations. Banks must ensure that AI systems are developed and deployed responsibly, addressing concerns surrounding bias in algorithms, protecting the privacy and security of customer data, and operating with transparency in how AI is being used to make decisions that impact people’s financial lives.Takeaway The future of banking belongs to those who embrace AI not as a replacement for human ingenuity, but as a powerful tool to enhance it. By fostering a culture of innovation, investing in their workforce, and prioritizing ethical AI development, banks can navigate this transformative era and create a future where technology empowers both institutions and the customers they serve.If you found these trends insightful, be sure to tune into the full episode of Behind the Growth for a conversation you do not want to miss!Find it here:Complexities of AI Implementation in Banking
The Potential Impact of AI on the Future of Banking
Keeping up with innovation and the fast pace of technological advancements is challenging. It requires Product Managers to continuously update their knowledge and skills and stay current with emerging technologies, tools, and trends.This webinar on Next Gen Product Management (NGPM) shares frameworks for skill advancement, the impact of ProductOps on lifecycle management, and the transformative role of AI in enhancing traditional Product Management roles.You will gain: NGPM Skill Maturity Matrix A detailed framework for advancing product management skills, including AI integration, ethical product design, and strategic foresight.Revolutionizing ProductOps A revolutionary approach to product lifecycle management by enhancing cross-functional collaboration and accelerating decision-making.Artificial Intelligence for PMs AI First approach to transform traditional PM tasks, such as, user story automation, dynamic road mapping, and data-driven user testing.Watch now to gain insights into skill advancement, lifecycle management, and AI integration in product management.
Next-Gen Product Management: Innovation, Skills, and Artificial Intelligence
Introduction Is your business ready to weather the storm of the climate crisis? The financial risks, the operational disruptions, the reputational hits — they’re no longer hypothetical. They’re the cost of doing business in a world where climate change is the new normal.I sat down with Vince Gasparro, a veteran of both government and finance, who’s dedicated his career to climate action, most recently as Managing Director and Head of Sustainable Finance at Roynat Capital, Scotiabank. Vince doesn’t sugarcoat the challenges ahead, but he firmly believes we have the tools to turn the tide.In this blog, you’ll get a glimpse into his no-nonsense take on why addressing climate change is no longer optional for businesses and how collaboration is key to creating lasting solutions. Get ready for a dose of clear-eyed optimism and practical insights to help your business navigate the complexities of a changing world.Vince Gasparro on the Urgency of Climate Action To grasp the urgency Vince brings to the table, you have to hear about his trip to British Columbia in 2021. Imagine driving through what should be pristine landscapes, only to encounter the worst air quality on the planet.That’s what Vince and his wife faced. Wildfires, fueled by a warming climate, had choked the region with smoke, turning a leisurely trip into a stark reminder of climate change’s immediate impact. “That’s just one anecdotal sort of point,” Vince remarks, “but when climate change hits, it hits hard.”A Burning Planet, A Bruised Bottom LineAnd it’s hitting businesses hard, too. We’re not talking about distant, abstract threats. From extreme weather events disrupting supply chains to the rising cost of resources and energy, the consequences of a destabilized climate are already being felt on balance sheets worldwide.What’s more, the way we power our businesses is in question. Vince points out that carbon emissions and methane, largely tied to our dependence on fossil fuels, are the primary culprits behind a warming planet.But he’s quick to emphasize that this isn’t about pointing fingers. “This isn’t an attack on those who work in the oil and gas industry,” he clarifies, acknowledging the need for empathy and understanding as we navigate this complex transition.The Power of Collective Action So, what’s the solution? It’s not a quick fix, and it’s not something any one entity can tackle alone. “No one group can do it on its own,” Vince emphasizes. He’s a staunch advocate for collaboration — a coordinated effort between governments enacting smart policies, businesses investing in innovative solutions, and individuals demanding change.He points to the inspiring example of Mark Carney, who rallied 450 of the world’s largest financial institutions, controlling $130 trillion in assets, to commit to net-zero emissions — a powerful testament to the impact of collective action.Vince’s message is clear: The time to act is now. Delaying action will only make the inevitable transition more disruptive and costly.Stakeholders in the Solution Vince is adamant that tackling the climate crisis isn’t a spectator sport. It’s a team effort, and everyone has a role to play.The Policy Pendulum: A Cautionary Tale Governments, with their ability to shape policy and incentivize action, are critical players. However, Vince uses the example of recent policy shifts in Ontario to illustrate the dangers of inconsistency, highlighting how short-sighted decisions can stall progress and even lead to increased reliance on fossil fuels.The lesson? We need consistent, long-term planning from policymakers to provide businesses with the certainty they need to invest in a greener future.Business Leadership: Beyond Compliance A lot of the problems we face move more quickly now than ever before. And one government can't do it on its own.But government action alone isn’t enough. Vince sees businesses as engines of innovation, capable of driving change at scale. It’s not just about complying with regulations; it’s about recognizing the competitive advantage of being a sustainability leader.Those who embrace sustainable practices, invest in green technologies, and embed environmental considerations into their operations will be better positioned to thrive in a carbon-constrained world.The Voice of the Consumer And then there’s the consumer. Never underestimate the power of their choices. Individual action and consumer demand are forces that can shift markets, Vince reminds us. When they choose eco-friendly products, support businesses with strong sustainability track records, and demand transparency, they send a clear message to the market: Sustainability matters.The key takeaway? Solving the climate crisis is a shared responsibility. Each stakeholder group — governments, businesses, and individuals — must play their part to drive meaningful and lasting change.Solutions and OpportunitiesWhile the scale of the climate crisis can feel daunting, Vince is a firm believer that we already have many of the solutions needed to build a more sustainable future. The key, he says, is embracing innovation and collaboration on multiple fronts.Investing in Renewable Energy: The Only Path Forward First and foremost, we need to accelerate the transition away from fossil fuels and towards cleaner sources of energy. “Ask them [investment bankers] how easy it is to raise equity or capital for a solar, wind…project—it’s become much easier,” Vince observes. This shift towards renewables, he argues, is already well underway, driven by a combination of market forces, technological advancements, and growing investor demand for sustainable options.Climate Tech: Innovation for a Sustainable Future Beyond renewable energy, Vince sees tremendous potential in the burgeoning field of climate tech. “Climate tech will play a significant role,” he states, highlighting innovations like carbon capture and storage, battery storage, and ever-increasing efficiencies in solar panel technology. These advancements, he believes, will be essential in decarbonizing industries that are difficult to electrify directly, such as heavy industry and transportation.Operational Efficiency: Doing More with Less But innovation isn’t just about high-tech solutions, Vince reminds us. Sometimes, the most impactful changes are surprisingly practical. He points to the often-overlooked area of operational efficiency. “Start with the low-hanging fruit,” he advises businesses. Building retrofits, for example, can lead to significant cost savings and emissions reductions. By optimizing energy use, minimizing waste, and embracing circular economy principles, businesses can shrink their environmental footprint while boosting their bottom line.For Vince, the path forward is clear. By embracing a combination of renewable energy, innovative technologies, and smart operational practices, businesses can position themselves not just to weather the climate crisis, but also to thrive in a more sustainable future.Takeaway The climate crisis demands action, not complacency. By embracing renewable energy, climate tech, and operational efficiency, businesses can drive meaningful change while securing their place in a sustainable future. The time for collaborative action is now – are you ready to lead the charge?If you enjoyed this blog, check out the full episode on the Behind the Growth podcast!Find it here:Taking Collective Action to Combat Climate Change
A Collective Action to Combat Climate Change
mobileLIVE is proud to celebrate another milestone this year–being recognized as one of the 2024 Best Workplaces in Technology. mobileLIVE received this honor after a thorough and independent analysis conducted by Great Place to Work®.The list is based on direct feedback from employees of the hundreds of organizations that were surveyed by Great Place to Work®. To be eligible for this list, organizations must be Great Place to Work-Certified™ and have exceptionally high scores from employees on the Trust Index survey.The recognition is a true honor, for the Best Workplaces in Technology list includes approximately 40 top companies in tech, including giants like NVIDIA and Cisco. Great Place to Work® has a set of themes and metrics that not only reveal whether employees feel their workplace is great, but also predict retention, agility, and overall business success. Using their proprietary Trust Index™ survey, they look at the core of what they know creates great workplaces — key behaviors that drive trust in management, connection with colleagues, and loyalty to the company.mobileLIVE is proud to have scored high on the Trust Index, roving that our approach is in the right direction: empowering employees, valuing diversity, and nurturing innovation throughout our workforce. At mobileLIVE, our people are the real drivers behind success, and this is a win for each of our valued employee.About Great Place to Work® Great Place to Work® is the global authority on high-trust, high-performance workplace cultures. A global research and consulting firm, Great Place to Work® provides the benchmarks and expertise needed to create, sustain, and recognize outstanding workplace cultures. In Canada, Great Place to Work® produces both industry and demographic-specific Best Workplace™ lists and represents the voices of 500,000 employees across industries. This is part of the world’s largest annual workplace study, recognizing the world’s Best Workplaces in a series of national lists including those published by The Globe and Mail (Canada) and Fortune Magazine (USA). Visit us at www.greatplacetowork.ca
mobileLIVE Recognized on the 2024 Best Workplaces in Technology List!
Introduction Have you ever stopped to count how many women are in the room? It’s not something most people do regularly, but for Lisa Sutherland, Senior Director of Emerging Markets at Rogers Communications, it’s a question her daughters have innocently asked her throughout her career: Why do you count the women in the room?Because even in 2024, women are still underrepresented in the tech industry, particularly in leadership positions.While progress is being made, deeply ingrained myths continue to discourage talented women from pursuing and advancing in tech careers. To help clear the path for the next generation, I had the pleasure of interviewing Lisa for the “Behind the Growth” podcast to get her insights on debunking these harmful misconceptions.With over three decades of experience witnessing the evolution of the tech landscape firsthand, Lisa offers invaluable wisdom on how we can create a more inclusive and equitable industry. Let’s dive into four persistent myths that she’s encountered and why they simply don’t hold up.Myth #1 – Tech is a “Boys’ Club”Picture a typical tech startup: a dimly lit room buzzing with the sound of fingers flying across keyboards. Empty energy drink cans litter the tables alongside scattered takeout containers. And huddled over glowing screens are… well, you know the image. It’s likely a group of young men, sporting hoodies and headphones, intensely focused on their code.This pervasive stereotype of tech being a “boys’ club” is one of the biggest hurdles women face. But according to Lisa, it’s a narrative rooted more in Hollywood than in historical reality. “Leading out of the Second World War, and even into the 1950s, computer programmers were mostly women,” she points out.The image of meticulous code written out on paper and fed into massive machines might not be as flashy as today’s sleek tech offices, but it underscores a crucial point: women have always been pioneers in tech, even if their contributions have been overshadowed.This historical erasure is precisely what makes movies like “Hidden Figures” so important, as Lisa highlights. The film sheds light on the brilliant African American women whose mathematical prowess was instrumental to NASA’s success, even as they faced segregation and discrimination. These stories challenge the narrow lens through which we often view tech achievements.Lisa further underscores this point by recalling her own experience attending tech events: “If you imagine a hackathon […] what’s that picture that you see?” It’s often the same stereotypical image—a homogeneous group lacking diversity. By acknowledging these ingrained biases, we can start to break them down and create a more welcoming and inclusive tech culture.Myth #2 – Women Aren’t “Naturally” Suited for Tech The notion that women somehow lack the inherent aptitude for tech is a damaging and persistent myth. It often rears its head in subtle ways, such as assumptions that women are less logical, less comfortable with numbers, or less drawn to technical fields. But as Lisa points out, these stereotypes are a relatively recent phenomenon.“Think about computer programming when you had to draw [it] on a piece of paper and insert it into a machine,” she suggests. “Those were computer programmers, and it was vastly women.” This historical context is crucial.In the early days of computing, programming was often viewed as detail-oriented and even secretarial — tasks often relegated to women. As technology evolved and became more lucrative and prestigious, the perception shifted, and it became increasingly masculinized.This evolution reveals a fundamental truth: success in tech isn’t about innate ability determined by gender; it’s about skills, dedication, and, importantly, opportunity.The fields of software development, data science, AI, and countless others require diverse talents, including problem-solving, creativity, and communication – none of which are exclusive to any gender.By letting go of the limiting belief that women aren’t “cut out” for tech, we open doors for countless individuals to discover their potential and contribute to a more innovative future.Myth #3 – Women Need to “Work Harder” to be Noticed Be present, lean in, and take on those challenges because you can make a difference with your beautiful talent, your creativity, your innovation.For many women in tech, especially those from older generations, the prevailing advice often boiled down to: put your head down, work hard, and wait to be recognized. It’s a well-intentioned sentiment, but as Lisa aptly points out, “Men were advocating for themselves and saying they could do the job, and women were waiting to be noticed.”This disparity in self-promotion is a key factor contributing to the gender gap in leadership roles and compensation. While hard work is undoubtedly essential, it’s often not enough in a competitive industry where men are often socialized to be more assertive in promoting their accomplishments and vying for opportunities.Lisa has observed a heartening shift in recent years, particularly among younger generations of women entering the tech workforce. “They are really learning to advocate for themselves, understand what they’re good at, and lean into those strengths,” she notes.This proactive approach is essential. Women in tech must feel empowered to own their accomplishments, articulate their value, and negotiate for the recognition and opportunities they deserve. This includes seeking out mentors and allies who can amplify their voices and champion their growth.Myth #4 – The Opportunities for Women in Tech Are LimitedIt’s a stark reality: women remain significantly underrepresented in tech leadership roles, and the gender pay gap persists. The numbers can feel discouraging, leading many to believe that the opportunities for women in tech are inherently limited.Lisa acknowledges this challenge, recalling a time when “there seemed to be this perception there was only enough room for one or two women around the table.” But she also emphasizes that this perception is changing, and companies are recognizing the value of building more diverse and inclusive teams.Her example of the Apple Health app initially overlooking women’s health needs is a powerful illustration. This oversight not only alienated a significant portion of potential users but also demonstrated the crucial need for diverse perspectives in product development. When women have a seat at the table, the products and solutions created are better, more innovative, and more relevant to a wider range of people.Lisa’s optimism shines through in her compelling analogy: “It’s a little bit like love,” she says. “There’s always enough love to go around for everybody if you make enough room in your heart.” By embracing this philosophy of abundance and actively “making room” for women in tech, companies can unlock a wealth of untapped talent, drive innovation, and create a more equitable and fulfilling industry for everyone.Takeaway It’s time to rewrite the narrative about women in tech. By debunking these persistent myths and fostering a more inclusive environment, we can unlock a wealth of untapped talent and create a future where everyone has the opportunity to contribute to a thriving tech industry.Want to hear more from Lisa on this important topic? Tune into the full episode of Behind the Growth for a conversation you don’t want to miss!Find it here:Advocating for Women in Tech
Debunking Four Myths About Women in Tech
Introduction Starting a business can feel like an insurmountable challenge, fraught with uncertainty and endless obstacles. How do you turn your entrepreneurial dreams into reality?I had the pleasure of interviewing Jahan Ali, a serial entrepreneur and the CEO of mobileLIVE, to celebrate his remarkable journey and gain insights from his experiences.Jahan is not just the founder of mobileLIVE but also the visionary behind HachiAI and XapCard. His journey from a new immigrant to a successful entrepreneur offers a wealth of knowledge and inspiration.In this blog, you’ll discover Jahan’s practical advice on overcoming early challenges, building a business from the ground up, embracing innovation, and the core values essential for entrepreneurial success. His insights will provide you with the tools and motivation to navigate your own entrepreneurial path.The Beginnings of an Entrepreneur We did not know anyone in Canada at that time, we did not have any funds, all the funds that we had, we paid our international tuition fees and, and then we are just here in a new country.Jahan’s entrepreneurial journey began with a leap of faith when he moved to Canada in 1998. As international students, he and his wife faced significant financial challenges in a new and unfamiliar country.They managed their finances meticulously, living in a one-bedroom basement apartment and sharing Happy Meals. Jahan secured a teaching assistantship, which paid $700 a month, while his wife worked at Yellow Pages.Despite the intense financial strain, Jahan’s determination and resilience set the foundation for his future success.The Power of Persistence and Support The support from his wife was crucial. Together, they navigated these tough times, proving that community and familial support are invaluable in the early stages of entrepreneurship.Jahan’s early experiences show that success often requires starting from the bottom, embracing a positive mindset, and persistently working towards your goals, no matter how daunting the circumstances.Building a Business from the Ground UpThe idea for mobileLIVE was sparked by Jahan’s realization of a gap in the market for high-quality, onshore digital transformation and IT consulting services.This insight came while he was working at Motorola, where he noticed inefficiencies and communication issues with offshore solutions. Jahan’s ability to identify a market need and his willingness to take risks were crucial in turning his vision into reality.Securing the First Client One of the key strategies Jahan employed to get mobileLIVE off the ground was offering free pilot projects to potential clients. This approach is a powerful lesson in building trust and showcasing value.By removing the initial financial risk for his clients, Jahan demonstrated the high standards and effectiveness of mobileLIVE’s solutions. This strategy not only secured his first clients but also established a foundation of trust and long-term relationships, highlighting the importance of innovation and bold moves in business.Key Growth Milestones mobileLIVE’s strategic thinking and willingness to innovate contributed to their growth. Winning a Request for Proposal (RFP) as the alternate vendor for device testing was a pivotal milestone moment. This achievement, which led to the creation of the first-ever 3G lab in Canada, underscores the importance of seizing opportunities and investing in unique capabilities to stand out in the market.Jahan’s experience with mobileLIVE teaches the importance of identifying market gaps, taking calculated risks, and continuously striving for excellence. By focusing on quality, building trust through innovative approaches, and capitalizing on growth opportunities, entrepreneurs can navigate the challenges of building a successful business from the ground up.Embracing Innovation and Adaptability Things are changing at lighting speed. So have a learn-it-all attitude. The moment you have a know-it all-attitude, the decline will start. So always keep learning.For Jahan, innovation and adaptability are essential to success. He believes that continuous learning and the ability to adapt to changing market conditions are crucial for any entrepreneur. His ventures, such as HachiAI and XapCard, are prime examples of how embracing innovation can lead to significant market impact.The Role of Innovation in Success Jahan views innovation as a driving force behind business success. He emphasizes the necessity of staying ahead of industry trends and continuously seeking new ways to solve problems.For instance, HachiAI was born out of a need to improve user acceptance testing across various platforms. By developing a product that could pre-code human gestures and automate processes, Jahan created a solution that filled a significant gap in the market.From HachiAI & XapCard: Pioneering New Solutions Jahan stresses the importance of encouraging innovation within the company.He shares how the ideas for HachiAI and XapCard emerged from mobileLIVE’s internal incubation program, demonstrating how fostering a culture of innovation within a company can lead to groundbreaking products.Jahan’s approach teaches the importance of being adaptable and open to new ideas. By continuously learning and embracing change, businesses can stay competitive and responsive to market needs. Innovation is not a one-time effort but a continuous journey that requires persistence and a proactive mindset.The Recipe for Entrepreneurial SuccessJahan’s journey offers a blueprint for entrepreneurial success, grounded in core values and practical wisdom. His experiences highlight the importance of hard work, persistence, emotional intelligence, and maintaining a positive attitude.Core Values for Success Jahan attributes his success to a set of core values that have guided him throughout his journey. First and foremost is hard work. Jahan believes that there is no substitute for dedication and effort.Persistence is another critical value. The entrepreneurial journey is filled with ups and downs, but Jahan’s story shows that those who remain persistent and refuse to give up are more likely to achieve their goals. This persistence is closely linked with maintaining a positive attitude, even in the face of adversity.Emotional intelligence, or EQ, is also essential. Jahan emphasizes the importance of understanding and managing one’s emotions, as well as empathizing with others. High EQ helps in building strong relationships, both within a team and with clients, which is crucial for long-term success.Final Words of Wisdom Jahan’s advice is clear: surround yourself with positive, supportive people, stay focused on your goals, and continuously strive to learn and adapt. He believes that luck favors those who are prepared and persistent. Success is not about being the smartest person in the room but about being the most resilient and adaptable.Jahan also stresses the importance of giving back. Whether it’s supporting your team, helping other entrepreneurs, or contributing to your community, being a giver creates a positive cycle of growth and success. After all, entrepreneurial success is not just about personal achievement but also about uplifting others and creating a lasting impact.Takeaway Jahan’s journey highlights the power of persistence, innovation, and community support in achieving entrepreneurial success. His insights provide a roadmap for aspiring entrepreneurs to navigate their own challenges and create lasting impact.If you found Jahan’s story inspiring, be sure to tune into the full episode of Behind the Growth for more valuable insights and advice!Find it here:Successfully Navigating the Entrepreneurial Journey
The Entrepreneurial Journey: Insights for Aspiring Business Owners
Toronto, ON–June 19 , 2024— mobileLIVE, a Toronto-based technology services and consulting company, is excited to announce the successful delivery of a highly anticipated masterclass on “7 AI-Xcelerators” at the Collision Conference. The event attracted overwhelming interest, with three times the number of registrants than the venue could accommodate, and only 100 attendees managed to secure their spots.The masterclass showcased seven practical and proven AI-Xcelerators that addressing Project Planning, Code Modernization, and Operational Automation:Code Transformation: This tool comprehends and analyzes legacy code, transforming it into modern languages and architectures, enhancing efficiency and compatibility.Code to Documentation: This accelerator generates code from various documents, including business logic, flow diagrams, sequence diagrams, and class diagrams, streamlining the documentation process.Knowledge Management: By processing input documents or data against existing repository documents, this tool performs analysis, comparison, and suggestion tasks, improving document management and utilization.Effort Estimations: Utilizing AI and machine learning models, this accelerator analyzes business requirements and generates a Work Breakdown Structure (WBS) to provide accurate time and cost estimates.Team Scoring: This tool collects metrics on code quality, task completion times, and peer reviews to generate a comprehensive performance score for each developer, fostering a data-driven approach to team management.Team Formations: Analyzing project requirements, developer scores, and historical project data, this accelerator suggests optimal team configurations, ensuring project success through strategic team alignment.Intelligent Digital Workers: Pre-trained to follow your processes and operate on various applications without requiring integration, these digital workers function around the clock without errors, significantly boosting productivity.The masterclass received rave reviews from attendees, underscoring the industry’s growing interest in AI-driven solutions.For more information about our AI-Xcelerators and to stay updated on upcoming events, subscribe to mobileLIVEs content.
mobileLIVE Hosts a Masterclass on “7 AI-Xcelerators” at the Collision Conference, 2024
Translating business requirements into technical deliverables, accurately estimating effort, and allocating the right talent is complicated. This often leads to cost estimation errors, talent misallocation, and project failure.Here’s how our AI-Powered approach has addressed these challenges:Effort Estimation We leverage AI to transform a Business Requirements Document (BRD) into a detailed Work Breakdown Structure (WBS) with time and cost estimates, reducing the time and human error involved in project planning.Team Scoring We use data from tools like Jira, GitHub, and SonarQube to calculate a comprehensive score for developers based on their performance, experience, and code quality, facilitating informed team selection and performance monitoring.Team Formation Our approach recommends optimal project teams by analysing historical performance data and matching it with project requirements, ensuring the best fit in terms of skill, cost, and delivery time.Watch this webinar for a demonstration highlighting AI-powered approach to Effort Estimation, Team Scoring, and Formation.
AI-Powered Project Planning, Effort Estimation, and Team Formation
The secret to a company’s success often lies in how a business treats its two most important assets: its customers and its employees.This approach is vital because fostering a positive employee environment translates into exceptional customer service, thereby creating a cycle that drives sustainable growth and profitability.In this episode of ‘Behind the Growth’, I had the honour of interviewing Hussain Qureshi, the President of mobileLIVE, to celebrate his journey and explore his perspectives on the topic.With a career spanning several decades in the tech and telecommunications industries, Hussain has a wealth of knowledge on what it takes to drive a company to success. From his early days as an electronics engineer to leading mobileLIVE as its President, his insights provide a valuable roadmap for any business aiming to prioritize both customer and employee satisfaction.In this blog, you’ll learn Hussain’s strategies for driving business growth while fostering a thriving workplace culture and a satisfied customer base.Identifying and Addressing Customer Needs At the core of any successful business lies a profound understanding of customer needs. Hussain emphasizes that mobileLIVE’s sustained growth is largely attributed to its ability to identify and effectively address these needs. This approach has kept it competitive and fostered strong, enduring relationships with its clients.Engaging Directly to Fine-Tune Solutions At mobileLIVE, the engagement process is designed to ensure that customer interactions are meaningful and productive.Hussain highlights importance of using detailed feedback mechanisms, which help capture essential insights into customer needs and preferences. This process involves regular surveys, customer interviews, and the use of customer service analytics to gauge satisfaction and pinpoint areas for improvement.Moreover, he employs a proactive approach by involving customers early in the development process. This allows for iterative feedback, refining products and services in response to direct customer input before final release.This hands-on engagement strategy ensures that solutions are not only effectively addressing the identified issues but are also evolving with the customer’s changing needs, thereby enhancing overall satisfaction and loyalty.Tailoring Solutions for Enhanced Customer Satisfaction Such a customer-centric approach ensures that every solution provided is tailored to meet specific needs, thus enhancing satisfaction and loyalty. This method of operation doesn’t just solve immediate issues but builds a foundation for long-term success and customer retention by consistently delivering value that customers truly need and appreciate. This proactive stance on customer experience is the cornerstone of a successful business, driving growth through customer satisfaction and loyalty.Fostering Employee Satisfaction and Engagement At mobileLIVE, the emphasis on employee satisfaction is as strong as the focus on customer needs. Hussain believes that a motivated and satisfied workforce is essential to the company’s success. By creating an environment where employees feel valued and part of the company’s mission, you can ensure that your team is productive and deeply engaged with their work.Strategies for Enhancing Employee Engagement mobileLIVE employs several strategies to keep its employees engaged and committed. This includes a robust internal communication strategy that ensures all team members are informed and their voices heard. Regular town halls, feedback sessions, and open forums are part of the company’s culture, fostering a sense of inclusion and transparency.Moreover, Hussain highlights the importance of investing in employees’ professional growth. Through continuous training and development opportunities, employees at all levels should be encouraged to acquire new skills and advance their careers within the company. This focus on personal and professional growth helps maintain high engagement and job satisfaction levels, contributing directly to employee retention and overall company success.Cultivating a Culture of Recognition Recognizing and rewarding employees’ efforts and achievements is another pillar of mobileLIVE’s strategy. Regular celebratory events, performance bonuses, and public acknowledgments contribute to a positive work environment. Such practices boost morale and reinforce the company’s values of hard work and excellence.By prioritizing employee satisfaction and engagement, you can create a motivated workforce that is well-prepared to meet the challenges of the market while driving the company forward.Integrating Employee Goals with Customer Outcomes At mobileLIVE, the alignment of employee goals with customer outcomes is a strategic imperative that drives mutual success. This integration ensures that as employees achieve their professional goals, they concurrently advance the company’s mission to enhance customer satisfaction. It’s a synergistic approach where employee success and customer success are seen as interconnected objectives that fuel the company’s overall growth.Feedback Loops and Communication Key to this integration is the robust feedback loops and open lines of communication between different levels of the organization. By fostering a culture where feedback is both given and received across all hierarchies, mobileLIVE ensures that employee initiatives are in harmony with customer needs. This ongoing dialogue helps in fine-tuning processes and services in real-time, enhancing the ability to meet customer expectations more efficiently and effectively.Empowering Employees to Deliver Excellence Employees at mobileLIVE are empowered to take ownership of projects that directly impact customer experiences. This empowerment is facilitated through access to the necessary tools, authority, and confidence to make decisions that benefit the customer. As a result, employees feel more vested in the outcomes of their work, driving them to excel and innovate in ways that significantly enhance customer satisfaction.Through these strategies, you can foster a positive and proactive workplace and ensures that the company’s objectives of delivering exceptional customer service are met. This alignment of employee and customer goals creates a dynamic where both the company and its clients thrive together.Lessons Learned and Future Directions Capitalizing on Past Experiences Over the years, mobileLIVE has learned valuable lessons that have shaped its strategies and approaches to business. One of the key insights is the importance of staying adaptable and responsive to both market changes and internal feedback. Hussain stresses that this adaptability allows companies to continuously refine their services and internal practices to better meet the evolving needs of customers and the aspirations of their employees.Anticipating the Future Looking ahead, Hussain suggests focusing on leveraging your past experiences to anticipate future challenges and opportunities. mobileLive plans to integrate technology and innovative practices further to streamline operations and enhance the customer and employee experience. This includes adopting more advanced data analytics for deeper customer insights and further automating processes to free up employee time for more strategic tasks.Continued Commitment to Core Values As it moves forward, mobileLIVE remains committed to its core values as they guide the company’s decisions and strategies, ensuring that as they grow, they continue to provide value to their customers while maintaining a supportive and engaging work environment for their employees.Through thoughtful reflection on past lessons and a clear vision for the future, you will be well-positioned to continue your trajectory of growth and success.Takeaway As we’ve delved into mobileLIVE’s successful strategies with Hussain Qureshi, it’s evident that the key to thriving in today’s dynamic market lies in simultaneously enhancing customer experiences and nurturing employee engagement.This exploration offers a guide for businesses to understand the importance of balancing these critical areas effectively. Organizations adopting similar approaches can foster a more engaged workforce and a more satisfied customer base, propelling themselves toward sustained growth and success.If you found these insights valuable, be sure to tune into the full episode of “Behind the Growth” for a conversation you won’t want to miss!Find it here:Prioritizing Customer and Employee Experience
Prioritizing Customer and Employee Experience to Drive Business Growth
User personas are one of the many tools a designer can use to guide the design process.The intention of these semi-fictional representations of your audience is to aid in humanizing your approach and understanding who you’re designing for.Thinking about the motivations, goals, challenges and preferences of your target can help in designing in a more user centric and thoughtful way – but are these useful?In this round of “Ask a Designer,” we asked members of our design team about their experience with navigating user personas and how they remain mindful of empathy in design.Question 1How often do you create user personas? Name: Irum TariqTitle: Lead Product DesignerAnswer: Creating personas is typically common when working on a new project or a large-scale initiative. Several factors contribute to this practice, such as budget availability, time constraints, and the need for personas given existing data. Ideally, personas should be created frequently and updated after significant market research activities, like surveys, interviews, or usability testing, as these activities reveal new user information. However, due to various constraints, personas are usually created only when embarking on new projects.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer: In my 8 years of experience, I have created personas from scratch only on a few occasions. I have assisted in developing personas to support the establishment of a research team and utilized them to illustrate the typical day of our target users, providing valuable insights for the design process.Additionally, I curated personas and developed broad archetypes (a higher-level abstraction of a collection of personas) to facilitate internal consensus on the user base for a platform I was helping build. These archetypes aided in aligning internal stakeholders and provided clarity on the objectives and obstacles faced by the end users of our platform.Name: Sheyla AmaralTitle: Lead Product DesignerAnswer: It’s not very common. When working on existing products, it’s typical for user personas to have been established at the start of the project. In my experience, I’ve encountered situations where the target users for these personas changed slightly, requiring a reassessment. For new products, the process is different, and the method for defining personas can vary based on the product’s goals, and I have worked defining some of them, which is an interesting and deep process.Question 2What methods do you use to ensure relevance when creating user personas? Name: Irum TariqTitle: Lead Product DesignerAnswer: My process for creating user personas is straightforward. I begin by setting the objective and scope to determine why we need personas and how they will be used. Next, I conduct research using qualitative and quantitative methods. I recruit participants from targeted age groups for interviews and distribute questionnaires. Once the data is collected, I organize it according to different segments such as demographic and behavioral. I then create persona profiles, adding details and summarizing key insights from the data. Before utilizing the personas in my design, I validate and refine them by sharing with key stakeholders to ensure accuracy and relevance.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer: It is essential to know why personas are needed, the context behind creating one, and how the team plans to use them. It is also helpful to align with the key partners and requestors to flesh out any existing assumptions and curate available resources to build a baseline understanding of the organizational needs before creating the plan for building a persona.Personas are ideally created by conducting research with the target segments and analyzing the patterns and trends that emerge from the insights. The team working on building personas can leverage analytics and support data, such as call and chat transcripts, to identify the target segments and recruit appropriately for conducting persona research.Name: Sheyla AmaralTitle: Lead Product DesignerAnswer: To create user personas, I start by identifying and segmenting our in-house customer data. Once that’s done, I conduct a quantitative survey to gather additional information and confirm the in-house data. Depending on the project, interviews and focus groups are essential for obtaining more detailed analysis of behaviors, goals, and pain points. One particularly interesting project I worked on involved conducting a diary study with kids aged 8 to 12. It’s crucial to choose the method that aligns with your target audience so they can actively participate.Question 3Have you encountered any challenges in using user personas? If so, how have you addressed them? Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer: One of the challenges I have encountered in using personas is that there is great enthusiasm for building one or more personas at the start of the exercise, but the energy and commitment dwindle once these personas are created. The personas also tend to become stale over time, and often, there is a lack of real application in day-to-day work. An alternative might be maintaining a list of Jobs-to-be-done by each persona that gets refreshed periodically in the context of the problems being solved.Name: Sheyla AmaralTitle: Lead Product DesignerAnswer: One of the challenges I faced before was regarding the huge number of personas. Sometimes, businesses want products to range from 18 to 99 years old, and it makes it hard to prioritize design decisions when we have so many different user needs. While having a clear problem statement helps, it’s still too broad.Another major challenge is keeping the personas updated. As user goals change over time, so do their needs. Continuously validating and analyzing data is costly and time-consuming, so often we end up not doing it.Name: Irum TariqTitle: Lead Product DesignerAnswer: Creating and using user personas can present several challenges. One of the biggest challenges I face is the lack of accurate data. Personas are only as reliable as the data they’re based on, and inaccurate or insufficient data can result in personas that do not accurately represent the target audience. To address this, I validate my data by conducting follow-up interviews and surveys. Additionally, working closely with the analytics team and gathering as much information as possible helps me validate my research and ensure the personas are accurate.Question 4Are there specific scenarios where user personas prove more valuable than segmentation models, and vice versa? Name: Irum TariqTitle: Lead Product DesignerAnswer: There are specific scenarios where user personas and segmentation models can each be more valuable. Understanding the contexts in which each tool is most effective can help make informed decisions about when to use them.For example, when designing a new product interface or improving an existing one, user personas provide a detailed, narrative-driven understanding of users’ goals, behaviors, and pain points, helping designers create more intuitive and user-friendly interfaces.On the other hand, when conducting market analysis and defining market entry strategies, segmentation models offer a broad overview of different market segments based on demographics, behaviors, and other quantifiable criteria, enabling businesses to identify and target high-potential market segments.By recognizing the specific contexts and scenarios where each tool excels, you can effectively combine user personas and segmentation models to enhance decision-making and user-centered design processes.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer: Personas often provide the most value when kickstarting product development from scratch. During this time, the team needs clarity to identify where to invest its time and resources to maximize value creation and minimize the risk of failure.Personas can also be an excellent tool for onboarding new team members and orienting them to the nuances of the problem space.Segments are more pertinent to targeting users for large-scale quantitative exercises such as surveys and growth-oriented activities such as ad-targeting.Name: Sheyla AmaralTitle: Lead Product DesignerAnswer: When building a product from scratch, starting with personas can help the team visualize the target users. It also assists in defining the tone and language for marketing efforts to reach those users. Product iterations and features are typically based on user needs, which can be validated using personas.However, before using personas, it’s important to develop segmentation models. These models can help us understand promising markets and trends.Takeaway The insights provided by our design team members underscore the significance of empathy in design, particularly through the utilization of user personas.User personas offer a means to humanize the design process, facilitating a deeper understanding of the target audience’s motivations and preferences. However, the frequency of creating user personas varies, often occurring at the onset of new projects due to constraints like time and resources.Ensuring the relevance of user personas involves a meticulous process of research and validation, utilizing both qualitative and quantitative methods. Challenges such as maintaining updated personas and managing a large number of personas are acknowledged, requiring continuous validation and collaboration to address effectively.In discerning between user personas and segmentation models, both serve distinct but complementary purposes. User personas guide product development and interface design by providing a narrative-driven understanding of user behaviors, while segmentation models offer broader market insights for strategic decision-making.By integrating user personas and segmentation models effectively, designers can foster user-centered design approaches that resonate with their target audience, ultimately driving meaningful engagement and lasting connections.Liked the insights shared in this article? Catch our previous Ask A Designer articles here:Ask A Designer: An Open Conversation with Product & UX Designers Ask A Designer Round 2: Questions For Designers, From Developers Ask A Designer Round 3: Staying Curious Ask A Designer Round 4: Considering Accessibility and Designing Inclusively Ask A Designer Round 5: The Dos and Don’ts of Design Thinking
Ask A Designer Round 6: Empathy in Design
Transform your legacy code effortlessly with RefactorX – a Generative AI-powered Code Converter.Our solution, RefactorX, converts any legacy code to your desired code, including version upgrades (like JavaScript to NodeJS). With RefactorX, you can also create technical and business level documentation regarding legacy or transformed code with an excellent search facility through existing documents and knowledge base.Innovative Use of Generative AI Utilizes advanced AI models to automate the transformation of legacy code into modern languages or updated versions.Comprehensive Transformation Suite Offers code translation, optimization, documentation generation, AI-enabled unit testing, and a knowledge management system.Targeted Solution Approach Designed to address the specific needs of industries heavily reliant on legacy systems, such as Telco, Banking, and Retail.Optimization Improves code quality and maintainability without changing its external behavior and business logic.With RefactorX 59% Increased Agility 57% Reduced Maintenance Costs 70% Better Integration with Other Platforms Watch now for a live RefactorX demo showcasing its GenAI-based approach to legacy modernization and experience efficient, error-free, and scalable code development.
Code Conversion: Modernizing Legacy Code with GenAI
Toronto, ON–May 14, 2024— mobileLIVE, a Toronto-based technology services and consulting company, was recognized for its industry-leading performance, its global business practices, and its sustained growth by receiving the prestigious 2024 Canada’s Best Managed Companies award.Celebrating over 30 years, Canada’s Best Managed Companies program awards excellence in private Canadian-owned companies with revenues of $50 million or greater. To attain the designation, companies are evaluated on their leadership in the areas of strategy, culture and commitment, capabilities, and innovation, governance and financial performance. “Being recognized as a Best Managed Platinum Club winner is a testament to our team’s relentless dedication and adaptability,” said Jahan Ali, Founder and CEO at mobileLIVE. “In an era marked by uncertainty, this achievement showcases our ability to overcome challenges and exhibit resilience at the highest levels. We are proud to set the standard for excellence in Canada’s private business sector, impacting how our country is perceived on the global stage.”Canada’s Best Managed Companies is one of the country’s leading business awards programs recognizing innovative and world-class businesses. Every year, hundreds of entrepreneurial companies compete for this designation in a rigorous and independent evaluation process. Applicants are evaluated by an independent panel of judges with representation from program sponsors and special guests.“The 2024 Best Managed winners exemplify the highest Canadian business standards of innovation, adaptability, and bold leadership,” said Lorrie King, Partner, Deloitte Private, Global Best Managed Leader and Co-Leader, Canada’s Best Managed Companies program. “Their relentless ambition, determined focus, and strategic agility have led them to remain competitive on the world stage, creating sustainable economic growth in an evolving global market.”The Best Managed assessment process is rigorous and comprehensive, evaluating a company’s overall performance and practices in areas such as strategy, leadership, innovation, culture, governance, and financials. Companies that achieve Best Managed status are recognized as industry leaders, demonstrating their commitment to long-term growth and sustainability.The 2024 cohort of Best Managed companies share common themes such as having a people-centric culture, targeting effective ESG strategies, and accelerating operational digitization.This year’s winners demonstrate exceptional commitment to drive growth in today’s competitive and dynamic business landscape,” says Blair Cowan, Executive Vice-President, Head of Commercial Banking. “CIBC is proud to sponsor a program that represents the best in Canadian business, as these privately owned companies continue to be the engine of our economic growth by creating jobs through strong leadership and innovation.”
mobileLIVE is a 2024 Winner of Canada’s Best Managed Platinum Club Designation
71% of consumers prefer brands that offer personalized experiences – and 76% get frustrated when that does not happen.The modern customer seeks a personalized experience, yet delivering it at scale is a constant challenge. In this webinar, learn beyond the foundational concepts on:Customer 360 View (with contact center data, social media analytics, and digital experience score) Discovering and Analyzing Customer Patterns (through contact channels to help decide the next best action) Personalizing Offerings and Actions (using real-time interaction management and Gen AI) Whether you’re a business leader, customer service professional, or AI enthusiast, by the end of this webinar, you will be equipped with the latest strategies, real-world examples, and industry use cases to drive customer satisfaction, engagement, and personalization.Watch now transform your customer interactions and excel in the rapidly evolving domain of customer service technology.
From Insights to Action: AI-powered Customer Experience
Design Thinking describes an approach used to solve problems in a creative and user-centric way.Sometimes, and frustratingly so, the term gets thrown around as a buzzword without an understanding of its true principles.At its core, the process is straightforward – considering your users, developing and refining ideas, seeking feedback and incorporating it into your product.In this round of “Ask a Designer,” we asked members of our design team how they navigate through the essential dos and don’ts of design thinking, and how it aids in coming up with creative solutions.Question 1How do you describe design thinking to stakeholders? Name: Viviam AlcaldeTitle: Product DesignerAnswer:A way to explain design thinking is through the initial processes of empathizing and defining before the design itself, prototyping, testing and implementation.We first need to understand the need for a solution through research and/or data, so we can identify the user pain points and then, we can try to solve those through design.It isn’t just a matter of “design this and that” only considering the initial requirements of a problem, but to understand what the users really need so we can grasp what is generating the problem. The design is just one part of a bigger process that comes after those findings.Finally, we need to continue researching to verify if the solution addresses the user needs and the problem, so we can improve or change it. If we “just design” without the other parts of the design thinking process, the time and money invested won’t be worth the effort because the problems will continue.Name: Pranavi LMKTitle: Associate Product DesignerAnswer:To ensure that stakeholders understand what Design thinking is, it is important to explain its value and potential impact on their projects or goals.Design thinking centers the problem-solving process around the end-user, producing more efficient and intuitive solutions.First, we need to understand the problem. We must put ourselves in the shoes of the user and empathize with the problem they face. This step is important to understand the user’s needs and perspectives, methods like interviews and surveys are used to gather insights.Once we understand the problem, we can define the problem statement and start coming up with ideas to solve it.The next step is to pick out the best ideas from your ideation phase and turn them into representations like wireframes, sketches, prototypes.Finally, you test your solutions with people who have the problem to see if they help. You listen to their feedback and iterate. Overall, the design thinking process will have better user satisfaction, space to innovate, and less chance to waste time and resources on creating products that do not resonate with user’s needs.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer:Design thinking is a methodology that weaves empathy for the user, creativity in ideation, and rationality in systematizing the user’s needs into a viable business strategy. I describe it to stakeholders as a lens through which we view our product development process, ensuring that every feature, interaction, and design decision is made with the end-user’s experience in mind.It’s not just about aesthetics; it’s about creating solutions that are as functional and user-friendly as they are visually appealing.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer:Design thinking is a systematic way of solving problems. We can break down this problem-solving process roughly into two parts:Finding out what needs to be solved and for whom, and which are the bigger problems that need attention right away Among the different ways in which we could solve these problems, what approach or approaches could we take to solve the problem in a way that is meaningful for the end users and the business We do the first part by learning more about the problem and the audience through activities such as research, competitor analysis, and coming up with ‘How Might We’ questions that describe the problem in a general way without pointing to a specific solution.The second part is about brainstorming, ideation, prototyping concepts, and testing to ensure that the problems are solved in the most efficient and elegant way within the scope of the constraints present.Name: Dana MitchellTitle: Seniors Product DesignerAnswer:Designs are typically something that evolves and changes over the course of its lifecycle. Design thinking is the iterative problem-solving process that supports that growth.When I apply design thinking to a problem, my goal is to develop a deep understanding of a problem and to create solutions with that knowledge.To help facilitate this, there are an endless number of tools and methods available. From interviewing the stakeholders or users to gain a better insight into their goal and pain points, brainstorming in a Figjam session, to mapping flows and analyzing metrics and user feedback. All the tools we use allow us to approach each new problem or project knowing that we will understand the problem and iterate our way to the best solution possible.I would argue that so long as you are trying to understand your problem so you can create better solutions, you are employing design thinking (and by extension, designing) regardless of the tools you use.Question 2What do you appreciate the most within the design process? Name: Viviam AlcaldeTitle: Product DesignerAnswer:I mostly appreciate the challenge of finding a design solution from the findings we have had, because that challenge leads us to great things professionally and personally. The fact that the empathizing and defining phases structure the thinking of the design solution leads us to a different kind of art. In other types of art, we have a blank canvas (or artboard) and struggle to start – but any result could be appreciated in the end.In Product Design, many times we come up with different solutions for the same problem but not every of them will address the problem properly. The struggle is to get the best one right away, that’s why we need data and testing after we deliver our designs.Therefore, the challenge is what motivates me to explore more and question myself. It makes me humble but also proud when I can improve user experiences.Name: Pranavi LMKTitle: Associate Product DesignerAnswer:I really appreciate the process of knowing what we will be designing instead of jumping right into it. With the design process, I have a clear understanding of the user, problem and solution that I want to create.Personally, I really enjoy the ideate and prototype segment of the design process.By exploring various ideas, we can uncover unexpected insights and alternate approaches to the solution. It pushes us to think out of the box. Prototyping helps me visualize the solution I had in mind. This helps the stakeholder, client, users understand the solution better. It allows us to iterate and test giving us the best result by the end.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer:What I most appreciate within the design process is the ideation phase. It’s an opportunity to diverge into a realm of creativity, brainstorming a myriad of possibilities without the constraints of feasibility, just yet.This freedom to explore often leads to innovative solutions that might not have been discovered through a linear approach. It’s a collaborative dance of creativity that, when followed by convergent phases of refinement and testing, leads to impactful designs that resonate with users.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer:I appreciate the challenge of fundamentally viewing problems with a beginner’s mindset in the design process – to be able to explore and understand how we could learn and address opportunities with a fresh lens and come up with different solutions depending on the context and the resources available at our disposal.The design process is, in its essence, iterative, which means there is an opportunity for improvement or change always round the corner.Name: Dana MitchellTitle: Senior Product DesignerAnswer:Within the overarching design thinking process, I appreciate how easy communication can be when everyone is on the same page. Creating and sharing project documents and knowledge keep the silo walls down.There is nothing so relieving as having a tricky flow mapped out during a conversation with stakeholders or developers, ensuring we all have the same understanding of the problem and/or solution.Question 3When shouldn’t design thinking be applied to a problem? Name: Viviam AlcaldeTitle: Product DesignerAnswer:When we already had had research and exploration with the same kind of users not long ago, or if something is “common sense” with a solution that is commonly successful.For example, we don’t need to “reinvent the wheel” and use completely new concepts of buttons or icons to refresh the page, minimize a window, expand a dropdown, among other known actions. The users in these cases are familiarized with certain icons and behaviours. That could also be applied to flows like sending a message or getting notified of a new message. We, the users in general, expect certain things from those types of experiences.Name: Pranavi LMKTitle: Associate Product DesignerAnswer:Design thinking is a great approach to problem solving, but it might not be necessary in problems that are not very complex. Design thinking requires a good amount of time and resources.If the problem you are looking to solve is small, need to sort quickly and You already know the answers to your key questions like who, what, why, you do not need to go through the entire process.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer:Design thinking might not be the best approach when the problem at hand is well-defined and calls for a straightforward solution, or when there are time constraints that don’t allow for the iterative, explorative nature of the design thinking process.It’s also less suitable for situations where legal, regulatory, or safety constraints limit the scope for creativity and user-led design.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer:It is helpful to view design thinking through the team’s confidence about a problem and the value a feature or a product or a service might create for the end user and the business.Design thinking is often helpful in the context of meaty and complex problems, which, when solved, can improve parameters such as user satisfaction, delight and ease while contributing to the business’s bottom line.A lean research framework called ‘Sprint‘ is more relevant when design thinking needs to be applied to move the effort directionally in a short time, such as a week.Name: Dana MitchellTitle: Senior Product DesignerAnswer:I’ve literally used a design thinking mindset when I’m trying to make dinner. Have I made this dish in the past, if so, was it successful? How will I change the recipe this time? What do I want my dinner to taste like? Sure, it may only take me 5 minutes to solve the “problem”, but I’m still leveraging the iterative nature of design thinking.Question 4What is one of the biggest mistakes being made within the design thinking process? Name: Viviam AlcaldeTitle: Product DesignerAnswer:Thinking that when the design is delivered the work is done and the problem is solved.The feedback, data, and use of the solution will say if the work is done or not, and when it needs to change. Those are part of the design thinking process and when we forget about them, we aren’t doing proper design thinking.Sooner or later, we will need to change the solution because things always change requirements, problems, types of users or audiences, or even design trends.Name: Pranavi LMKTitle: Associate Product DesignerAnswer:One of the biggest mistakes that is made during the design thinking process is assuming you know the user and their needs well and jumping to the prototype/solution phase. Understanding the user and empathizing with them will help you stay relevant to the problem.Sometimes design thinking can be a long process with repetitive iterations and going around the circle over and over. You should plan out time and resources well to manage that. Empathize and define phases help you set up that plan, because you can identify the solution and can estimate the time needed to ideate, prototype and test.Name: Carlos SalgueroTitle: Senior Product DesignerAnswer:One of the biggest mistakes in the design thinking process is skipping the empathy stage and jumping straight to ideation and prototyping. Without a deep understanding of the user’s needs, pain points, and contexts, the solutions we create may be innovative but not necessarily effective or needed. It’s like trying to write a novel without knowing the main character – it results in a narrative that lacks depth and resonance.Name: Ram KumarasubramanianTitle: Senior UX ResearcherAnswer:Not involving the designers and researchers early in the process and including them only to test concepts or build designs based on ideas already set in stone. By failing to include those who might help develop a more comprehensive view of the problems that need to be solved early enough, organizations risk building things that need to be reworked.Name: Dana MitchellTitle: Senior Product DesignerAnswer:Honestly, thinking that design thinking is something that is only part of the “designer’s toolkit”. You can apply the mindset that design thinking fosters to so many different scenarios, both personally and professionally.From my work as a mentor, I would say that the biggest mistake I’ve seen is using design thinking as a specific process.It’s important to consider the why behind the tools and methods that we use, or we risk creating work that doesn’t actually help solve the problem. Not every project will need the same activities or workshops.I would argue that one of the marks of “senior-ship” in designers is being able to identify a problem and use the most appropriate tool for the job, while being able to explain why.Takeaway The insights shared by our design team members highlight the nuanced and holistic approach of design thinking.It’s evident that effective design thinking goes beyond mere aesthetics; it’s about deeply understanding user needs, empathizing with their experiences, and iterating solutions based on feedback and data. Stakeholders must grasp the value of this approach, recognizing its potential to yield efficient, user-centric solutions that resonate with end-users while aligning with business objectives.The dos and don’ts outlined underscore the importance of each phase in the design thinking process, from empathizing and defining the problem to ideating, prototyping, testing, and iterating. Skipping or rushing through any stage risks compromising the effectiveness of the solution.Moreover, design thinking shouldn’t be confined solely to designers; its principles can be applied across various domains, fostering innovation and problem-solving in diverse contexts.The discussions herein emphasize the iterative nature of design thinking and the necessity of continual refinement based on user feedback and evolving requirements. Overlooking this iterative aspect or treating design thinking as a rigid process can lead to missed opportunities and ineffective solutions.Ultimately, embracing a comprehensive understanding of design thinking as a collaborative, user-centered, and iterative approach is essential for addressing complex problems, fostering innovation, and delivering impactful solutions that meet both user needs and business objectives.Liked the insights shared in this article?Catch our previous Ask A Designer articles here: Ask A Designer: An Open Conversation with Product & UX DesignersAsk A Designer Round 2: Questions For Designers, From DevelopersAsk A Designer Round 3: Staying CuriousAsk A Designer Round 4: Considering Accessibility and Designing Inclusively
Ask A Designer Round 5: The Dos and Don’ts of Design Thinking
63% consumers expect customer service agents to know their unique needs and expectations83% customers will switch to a competitor after a frustrating customer service experience79% customer service leaders plan to invest in more AI capabilities over the next two yearsWith the increasing number of customer-facing platforms, customer expectations are also growing. Today, understanding and solving customer needs heavily depends on leveraging customer interaction data and personalizing the customer service experience.In this webinar, learn how to proactively exceed customer expectations using AI and Analytics in Customer Service.By the end of this session, you will find yourself equipped withA Deep Understanding of how predictive analytics and AI anticipate customer needs Personalization Strategies to tailor customer interactions and enhance satisfaction Real-world Case Studies showcasing successful AI and Analytics implementation Integration Strategies to seamlessly integrate AI with existing customer service systems Emerging Trends to stay ahead and secure a competitive edge for your organizationJoin AI Practice Lead Inanc Cakiroglu in conversation with host Katy Unger and harness the full power of AI and Analytics to transform your customer service strategy.
Leveraging AI and Analytics to Revolutionize the Customer Service Experience
Keeping up with the speed of AI-Innovation is overwhelming, particularly given its profound impact on industries like Telecommunications, Banking and Financial Services, Retail, Agriculture, and Manufacturing.Tailored for decision-makers, senior executives, and business and technology leaders, in this webinar, AI Practice Lead Inanc Cakiroglu showcases the What, Why, and How of upcoming AI-Enablers through practical applications and multiple use cases of:Explainable AI Neurosymbolic AI Edge AI (Cloud and 5G) Computer Image Applicable AIJoin him in conversation with host Katy Unger as they discuss actionable insights on how you can drive AI adoption and create value for your organization.
How Latest AI-Enablers Are Driving Business Impact
As product designers and teams, we often face the challenge of finding new and imaginative design solutions or rethinking existing ones.Inspiration can come from anywhere; from brainstorming with colleagues to conducting user research, there are a variety of techniques you can use to generate and develop ideas.However, while the ideation process in design thinking is critical for solving problems creatively, it can often be more time-consuming, daunting, and frustrating than expected.For this reason, having a reliable AI-supported toolkit to draw upon can vastly speed up and improve creative problem-solving outcomes.In this blog post, we’ll explore three key tools that offer great capabilities and support for the process of ideation – allowing product designers and teams to connect ideas, identify patterns, develop prototypes as well as gain insights using data analysis – so you can unlock new pathways for innovative product design.How AI Tools Are Transforming the Way We Approach Research and Ideation in the Design Process Artificial intelligence (AI) has taken the world by storm, and the design industry is no exception.In recent years, we’ve seen the introduction of AI tools that are transforming the way we approach design research and ideation.By taking advantage of machine learning algorithms, natural language processing, and other AI techniques, designers and product teams are able to leverage massive amounts of data to generate insights, uncover trends, and make informed decisions.Let’s take a closer look at how AI tools are changing the way we do research and ideation in the design process:1. Enhancing User Research User research is a crucial aspect of designing products that effectively meet the needs of the target audience.Traditionally, this involved conducting focus groups, interviews, and surveys.However, AI tools are making this process more efficient and accurate. Machine learning algorithms can analyze large sets of data to identify patterns and trends in user behaviour.Tools like Google Analytics and Hotjar use AI to help designers understand how users navigate websites and products. This insight can help teams improve usability, identify pain points, and optimize user experience.2. Streamlining Idea Generation One of the biggest challenges in the design process is coming up with innovative ideas that meet user needs and solve business problems.AI tools can help teams generate and evaluate ideas more efficiently. Idea management software like IdeaScale and Spigit use AI to automate the idea generation process.These tools can quickly analyze large amounts of data to identify trends and suggest new ideas based on user feedback.3. Automating Prototyping Prototyping is a critical phase in the design process, as it allows teams to test and validate ideas before committing to development. But creating prototypes can be time-consuming and labor-intensive.AI tools can automate aspects of the prototyping process.For example, Sketch2Code is a Microsoft tool that uses AI to convert hand-drawn sketches into working prototypes. This eliminates the need for manual coding, saving time and money.4. Augmenting Designer Creativity While AI tools can automate many aspects of the design process, they’ll never replace human creativity. However, AI can help designers augment their creative abilities.Augmented creativity systems use AI to analyze patterns in user behaviour and provide designers with suggestions and recommendations for new design directions. This allows designers to create more targeted and effective designs that are more likely to resonate with users.5. Improving Decision-Making Finally, AI tools are helping teams make more informed decisions throughout the design process. AI-powered decision support systems use algorithms to analyze data and present designers with recommendations based on that analysis. For example, Attention Insight is a tool that uses AI to help teams identify which design changes will have the greatest impact on user engagement and conversion rates.By leveraging these insights, teams can make better-informed decisions about design changes and improvements. This demo shows exactly how you can leverage Attention Insight to make more efficient and accurate design choices: Optimizing the Design Experience with User Research using Attention InsightBy automating aspects of the process and providing designers with insights and recommendations, AI is making it easier to create effective, innovative products that meet user needs and solve business problems.While there’s still an important role for human creativity and intuition in the design process, AI is helping designers work more efficiently and effectively than ever before.For design teams and product designers, AI-powered tools are quickly becoming an essential part of the toolkit, and below are three top-rated tools that help teams collaborate and execute with success.3 Tools That Need To Be Part of Your Design Ideation Toolkit 1. Notion Notion is a popular collaboration tool used by many design teams.It’s an all-in-one workspace that allows for notes, to-do lists, databases, calendars, and more.For brainstorming and ideation, Notion’s flexible workspace provides a great platform for organizing user research, sketching ideas, and collaborating with colleagues.It comes with some great capabilities in the way of brainstorming that facilitate idea sharing. Some of the things you can do with Notion include:Experience seamless collaboration on a shared document, regardless of your location. Easily provide inline comments and tag teammates. Engage in asynchronous brainstorming sessions. Share ideas within the same space as your project management tools or team homepage. Notion also integrates with various tools like Figma, Slack, and Gmail, providing an effortless workflow for design teams.2. ChatGPT ChatGPT is a powerful AI language model that can generate text-based on the prompts provided. This tool is useful for ideation because it can generate ideas based on user questions, brainstorming prompts, or even real-time feedback.It’s particularly useful for remote teams that don’t have the luxury to brainstorm together in person.ChatGPT’s intelligent algorithm is trained on vast amounts of data, making it incredibly effective at generating creative solutions.In the way of brainstorming and ideation particularly for the design process, ChatGPT stands out amongst most AI tools due to its versatility.Amongst its many benefits are:It saves a lot of time by evaluating research information and deriving key insights from it. It does competitive analysis within the context of users’ likes and dislikes. It helps with things like content strategy, storyboarding, design documentation and general guidance when working on something. It helps you set a design direction and ideate when you begin working on something new. To understand these benefits through a use case, let’s look at this example where I used ChatGPT to improve the user experience of a fitness app.Using ChatGPT to Improve the User Experience My first step was to put in some data from user research to generate key insights:The prompt I used is one answer which a user gave when asked about their experience of using a fitness app.This is helpful when you have complex and extensive information to work with. ChatGPT generated key insights, which include the pain points as well as what the user liked.Next, I wanted to know which fitness applications out there don’t have the issues mentioned above, so I gave it the following prompt:The answer I got was a list of several different real-time apps that I could look into and use as an example to make design improvements on the app I was working on, making it an extremely handy software to use when it comes to analyzing what’s out there, do a quick competitive analysis, and extract funnelled information we can use ourselves.Another great thing about ChatGPT is that you can also put in product details and design goals to brainstorm and expand on the ideation process if you’re working on a new feature, potentially uncovering some novel approaches and solutions.For example, I wanted to create a gamified experience for an online fitness coaching feature.For this, I gave it the following prompt:Remarkably, ChatGPT gave me a long list of suggestions that can be incorporated in the feature when designing it:Achievement Badges: Award users badges for reaching specific milestones in their fitness journey. For example, a “30-day Streak” badge for consistent workouts, a “Newbie” badge for beginners, or a “Master of Yoga” badge for yoga enthusiasts. Points and XP System: Implement a point system where users earn points or experience points (XP) for completing workouts, attending live classes. or hitting fitness goals. These points can be used to unlock rewards or compete on leaderboards. Leaderboards: Create leaderboards that display users’ rankings based on their XP or points. Users can compete with friends, fellow class attendees, or the community to encourage friendly competition and motivation. Virtual Challenges: Introduce virtual challenges or quests that users can participate in. For example, a “Mountain Climbing Challenge” where users virtually climb mountains as they complete workouts, earning rewards upon completion. Avatar Customization: Allow users to create and customize their fitness avatars. As users earn XP, they can unlock new outfits, equipment, or accessories to personalize their avatars. Daily or Weekly Challenges: Offer daily or weekly challenges that push users to try different workouts, such as “Core Strength Monday” or “HIIT Challenge Friday.” Completing these challenges could yield extra points. Social Integration: Enable users to connect with friends or other app users. They can challenge each other, share achievements, and provide support, creating a sense of community. In-App Currency: Introduce an in-app currency that users can earn through workouts and use to purchase virtual goods, such as workout equipment, nutrition plans, or premium features. Progress Tracking: Provide detailed statistics on users’ progress, including calories burned, time spent working out, and improvements in strength or flexibility. Visualizing progress can be motivating. 10. Leveling Up: As users earn XP. allow them to level up within the app. Each level could come with increased benefits or virtual rewards. Mystery Rewards: Occasionally surprise users with mystery rewards or power-ups they can use during workouts. This adds an element of excitement. Narrative Storytelling: Create a fitness adventure by embedding a narrative into the app. Users can progress through a story as they complete workouts, making it feel like a game. Multiplayer Workouts: Offer multiplayer workout sessions where users can compete or collaborate with others in real-time. This can make workouts more interactive and engaging. Feedback and Coaching: Provide personalized feedback and coaching based on users’ performance, helping them improve and stay motivated. Virtual Events: Host virtual fitness events, such as themed challenges, live classes with fitness experts, or charity runs, where users can participate and earn rewards. With ChatGPT, I can also make different wireframes, prototype and test all of these different solutions and feature recommendations that it provided me with.To watch my full demo explaining in detail how I use ChatGPT for my design projects, be sure to tune into this video: *embed link*3. QoQo QoQo is Figma plugin that enables design teams to maximize their productivity and quality of output by quickly benefiting from research data. It helps them save precious time and effort in the user research and discovery stage. QoQo is designed specifically for UX design and is meant to serve as a companion that helps you get a broad and organized picture at the early stages of the design process.Based on your input, it generates cards to build a persona with user goals, needs, motivations, frustrations and tasks, or it can help you identify challenges, risks and key elements for your design brief.Final Takeaway In conclusion, the ideation process is critical for identifying creative solutions to complex problems. There are various tools and techniques that can help facilitate the process.Notion, ChatGPT, QoQo are some of the most popular and effective tools for the ideation stage.Whether you choose to use one or several of these tools, it’s essential to remember that ideation is a creative process.So don’t hesitate to try new things, experiment, and explore different perspectives with your team to develop the most innovative solutions.
AI Toolkit for Creative Problem-Solving: 3 Tools to Enhance Design Ideation
Designers are faced with numerous decisions when it comes to creating visual elements in the design process, such as the colour palette, typography, layout, and more.These design choices may seem trivial, but they hold immense power in determining the success or failure of a product; the tiniest design changes can influence user behaviour and increase conversion rates, which is the end-goal with any new design.That’s why it is so critical for designers to make design choices that are driven by user research – and for that, they can harness the capabilities of AI tools in order to make efficient, accurate, and informed design decisions.In this comprehensive blog post, we will delve into three remarkable tools: Attention Insight, Maze, and Hotjar – and explore how they assist design teams and product designers in making data-driven design decisions.By leveraging these tools, designers can gain valuable insights and optimise their design choices based on concrete user research and data rather than guesswork, leading to more impactful and successful outcomes.But first, let’s dive into a bit of a background of Design Thinking and the role of design decisions.Design Thinking and the Role of Design Decisions Design thinking offers a solution-centred approach that makes it an integral part of successful product development.It involves understanding the users’ needs, challenging assumptions, and redefining problems in an attempt to identify alternative solutions that might not be immediately apparent. To reach this understanding and to get your team on the same page, you can conduct various kinds of design thinking workshops as well.A pivotal part of this process is making design decisions, which involves selecting the most appropriate design elements that will deliver a superior user experience.And as it goes without saying, the lesser uncertainty there is in these design decisions, the higher are the chances of your design being a success.For a deeper understanding on how you can reduce these uncertainties, give this article a read: Reducing Uncertainties With Design Thinking: The 7 Uncertainty IndexDesign Decision-making Before AI In the early days, these decisions were primarily driven by designers’ intuition and experience.Without the assistance of AI, designers relied on their expertise and past knowledge to make informed choices about design elements.They would analyse user feedback, conduct usability tests, and iterate on their designs to improve the user experience.While this approach was effective to some extent, it often lacked the data-driven insights and predictive capabilities that AI brings to the table.Additionally, this approach left room for subjectivity and bias, leading to designs that may not effectively address user needs or business goals.Design Decisions With AI With the advent of digital technology and AI tools, the process of design decision-making has evolved significantly.With artificial intelligence, designers now have access to sophisticated algorithms and machine learning models that can analyse vast amounts of user data, identify patterns, and make data-informed design decisions.This integration of AI in the design process has revolutionised the way user research is done and, thus, how design decisions are made, allowing for more precise and optimised user experiences.Today, design decisions are more data-driven, relying on insights gathered from user behaviour and market trends.These informed decisions lead to designs that are not only aesthetically pleasing but also functional, user-friendly, and conversion-oriented.3AI T ools To Make User-focused Design Decisions Leveraging AI tools to make user-focused design decisions is becoming increasingly crucial in today’s digital landscape.By harnessing the power of artificial intelligence, designers can gain invaluable insights into user behaviour and preferences, enabling them to create intuitive and personalized experiences.These AI tools analyse vast amounts of data, uncovering patterns and trends that might otherwise go unnoticed.The following are some of the top AI tools that employ a data-driven approach that facilitates user research and empowers designers to make informed decisions, resulting in products and services that truly resonate with their target audience.As the field of AI continues to evolve, its potential to revolutionise user-focused design is only set to grow, and these 3 AI tools are playing a big role in that:Attention Insight At the top of the list of AI tools for design decision-making is Attention Insight, which uses AI and eye-tracking technology to predict how people will interact with a design.It helps designers understand what elements of their design will attract the most attention and what parts of the design will be ignored by the users.The tool works by analysing the visual hierarchy of a design, identifying the elements that are most likely to be noticed by the user.Designers can then use this data for user research and make improvements to their designs accordingly, eventually being better equipped to optimize them for better engagement.Attention Insight is a valuable tool for anything from website design to advertising, and is quickly being adopted by designers all around for its immensely crucial benefits:Time saved: Attention Insight provides quick results in less than 60 seconds, allowing users to efficiently analyse and make design decisions without wasting valuable time. Accuracy: With an impressive accuracy score ranging from 90% to 96%, Attention Insight ensures reliable and precise insights into users’ attention and focus areas, enabling designers to create highly effective designs. Compatibility: It seamlessly integrates with Figma, a popular design tool, making it convenient for designers to incorporate eye-tracking studies and heatmaps into their design workflow. User Insights: By utilizing eye-tracking studies and heatmaps, Attention Insight offers valuable insights into where users’ attention is directed within a design. This enables designers to optimize their designs and enhance user engagement. Validity: Attention Insight is backed by MIT scientists, ensuring its validity and credibility in providing accurate and scientifically validated data for design decision making. A/B Testing with Attention Insight Particularly for A/B testing, which is a very big part of design and helps us base our decisions on data while removing the guesswork, Attention Insight is a great tool that can quickly analyze A/B Testing results.To watch this tool in action, here’s a demo that will help you see exactly how it can be used to conduct A/B tests with far more efficiency, speed, and confidence than you’d be able to without it:Maze Maze is another tool that uses AI to generate design variations.All you need to do is upload a design to Maze, and it will generate multiple design options based on the inputs provided.The tool can also learn from user feedback and generate better designs over time. In essence, Maze helps designers to create a large number of design variations in a short amount of time.One of its biggest benefits is that it scales user insights through AI-powered user research based on prototype testing, website testing, interview studies, card sorting, and feedback surveys.By doing so, Maze helps product designers save time, and it can also inspire new design ideas that otherwise may not have been considered.Hotjar Hotjar is an AI tool that helps designers understand how users interact with their design and create designs with the target audience in mind.Hotjar captures user data such as mouse movements, clicks, and scrolls, and then generates heat maps showing where users spend the most time on a page. This data helps designers understand how users are interacting with their designs, identify areas of improvement, and make data-driven design decisions.Hotjar can also provide designers with insights into user behaviour, usability issues, and potential opportunities to improve the user experience.Some of its top advantages are that it provides unbiased feedback, provides compelling data to remove any room for opinions and guesswork, and ensures that any new design created with Hotjar hit the targets without ever compromosing the user experience.Final Takeaway AI addresses many of the challenges that visual designers face.Data-driven design decisions using AI can help designers make better decisions quickly and efficiently. The tools mentioned in this post, including Attention Insight, Maze, and Hotjar, are just a few examples of the vast potential that AI has to offer for design teams and designers.Check out all of these other tools that can be used at different stages of the design process and reap various benefits in this article: AI Tools For Designers: 5 Ways To AI-Power Your Design Process.With the help of AI, designers can create designs that are more engaging, lead to more conversions, and ultimately create better user experiences.Want to learn more about how you adopt AI for your design processes? Reach out and speak to our experts now!
3 AI Tools To Conduct User Research That Drives More Optimized Design Decisions
Any product team, or design team, knows the importance of properly documenting product requirements, but the process can often be time-consuming and tedious.User stories are a crucial component of this documentation, as they outline the needs and behaviours of the user in a simple, easy-to-understand format.And while everyone wants to create better user stories, not everyone may have the know-how to do so efficiently.As most product people would be able to relate to, coming up with these user stories can be challenging, especially when dealing with complex products and various stakeholders.Luckily, there are several AI tools available to us now that can help product designers with the way they create designs efficiently.In my opinion, out of multiple AI tools that I’ve experimented with, one of them has stood out when it comes to streamlining this process and making creating user stories a breeze – and that’s ChatGPT.Understanding ChatGPT and its Role in User Story Creation ChatGPT is a chatbot that uses artificial intelligence to understand natural language and generate relevant responses.Its user-friendly interface allows product managers and designers to quickly create user stories by simply conversing with the chatbot.For example, you can tell ChatGPT something like “I need a user story for a user who wants to order a pizza online,” and it will generate a user story that fits that description.ChatGPT is also great for collaboration among team members. As each team member adds their own user stories, ChatGPT learns from this input, becoming better at generating relevant responses over time. This means that the more you use ChatGPT, the more efficient and accurate it becomes.Another key benefit of ChatGPT is its ability to organize user stories.With ChatGPT, you can create different categories and tags, making it easy to group similar user stories together and find them when you need them. This can save time and reduce the likelihood of duplicate user stories being created.The process of creating user stories with ChatGPT is simple and straightforward. After interacting with the chatbot to generate a user story, you can review and edit it within the tool’s user-friendly interface. From there, you can easily export the user stories in a variety of formats, making it easy to share them with stakeholders and collaborate with your team.Why ChatGPT is a Class Apart From Other AI Tools As product managers, we often grapple with the challenge of creating comprehensive user stories.To simplify this task, we rely on AI tools such as ChatGPT, Notion, and Perplexity.Among these, ChatGPT stands out as a preferred choice due to its ability to generate user stories quickly and accurately.Additionally, through ChatGPT, we find that we’re able to:Accelerate the user story creation process Instead of manually writing each user story from scratch, Chatgpt can generate a list of potential user stories based on conversation with the project’s stakeholders.It can then be further tailored to match the exact specifications of the project.Additionally, Chatgpt helps users to identify potential problems or unanswered questions, as well as providing suggestions for improvement that could help accelerate the user story creation process.This allows teams to focus their energies on creating better solutions faster and accelerating the production cycle.Ensure a standardized and consistent format of the user stories Chatgpt ensures a standardized and consistent format for user stories by utilizing natural language processing (NLP) technology.NLP is a type of artificial intelligence (AI) that enables a computer to understand and interpret human language.With this technology, Chatgpt can process natural language input and produce output in a predefined format. This allows the system to provide users with a structured template that they can use when creating their own stories.Additionally, the system can make suggestions based on the content of the user’s story, helping them stay within the desired framework.This helps to ensure that all user stories are consistent in terms of style and structure, helping developers find user stories easier and use them more efficiently.Enhance clarity for development teams Writing user stories with ChatGPT helps enhance clarity for development teams by providing a more natural and intuitive way to describe the desired feature.By using natural language processing, chatgpt is able to analyze the input and generate a more detailed description of what needs to be done.This helps ensure that the development team understands exactly what needs to be accomplished in order to meet the customer’s needs. Additionally, this can help reduce ambiguity and misunderstanding between stakeholders, as they are all looking at the same story written in plain English.This also allows for quicker feedback loops, as developers can quickly make changes if needed without having to constantly refer back to technical documentation or business requirements documents.Ultimately, writing user stories with ChatGPT can provide development teams with a clear direction and help them deliver high-quality features faster.Stay more efficient and user-focused ChatGPT allows us to quickly and easily create detailed specifications for product features.Because the user stories are created using natural language, it is easier for product people and designers to quickly understand what users are expecting from a particular feature, since the language used is similar to what they would use in conversation with their customers.This allows product teams to focus on building the right product experience in the most efficient way possible, rather than spending too much time trying to interpret customer feedback or deciphering complicated technical requirements.Additionally, ChatGPT helps reduce friction in the design process by providing ready-made templates that help designers complete tasks faster, while ensuring that all user stories accurately reflect customer needs.How Does ChatGPT Improve the User Story Creation Process: Learning with an Example To dive into how each of the above outcomes can be achieved with ChatGPT, let’s take the following as a user story creation example:You’re a product manager tasked with developing a new feature for a mobile shopping app that enables users to create and share wish lists.With the help of ChatGPT, you can effortlessly generate the necessary user stories to build this functionality.You also have the flexibility to generate multiple user stories with all the required details simultaneously.This not only saves time but also enhances clarity for development teams. By streamlining the user story creation process, product managers can focus more on user-centric tasks and improve overall efficiency.To explain how ChatGPT accomplishes this, the following is a demo I’ve done to showcase how I used the tool to create a user story for a new feature for a hypothetical shopping app that allows users to create and share wish lists.*embed full video of demo*Going Beyond Story Writing: Acceptance Criteria, Risks, and Mitigation Strategies ChatGPT goes beyond just user story writing to also provide acceptance criteria, potential risks associated with the user story, and mitigation strategies to minimize those risks.This is a great thing for product designers because it allows them to clearly define what they expect from each user story and anticipate any potential problems before deploying the product.By including acceptance criteria, designers are able to identify specific requirements so that the product meets customer expectations.With potential risks identified in advance, designers can plan a strategy to mitigate those risks and make sure that the product remains stable and secure.In addition, if any unexpected issues arise during development, the mitigation strategies can be used as a reference in order to take corrective action.All of this helps designers create products that meet users’ needs while minimizing risk and increasing stability, which is why it’s so critical to nail the storyboarding process right from the beginning.For a quick refresher on exactly what user stories are, what all they encapsulate, why they’re essential, and how product designers go about creating them, be sure to check out this article: Storyboarding in UX Design: Learning the What, When, Why, and How.Final Thoughts In conclusion, ChatGPT is a game-changer as a user story creation tool.Its ability to generate relevant responses quickly, learn from team input, and organize user stories efficiently makes the process of documenting product requirements much more efficient and manageable.By using ChatGPT, product teams and design teams can save time and hassle while creating high-quality product documentation that accurately reflects user needs.Give ChatGPT a try and see how it transforms your user story creation process.Got any questions or want to discuss this further? Speak to our experts here!
Leveraging AI For Product Documentation: How ChatGPT Enhances User Story Writing
Maximizing Business Outcomes Through AI Adoption
Take a minute and think about your favourite service that you interact with.When I say service, I’m referring to the journey or process of getting a task accomplished, not necessarily getting a good as the end product but achieving a desired task. Uber, Amazon Prime, Netflix are all examples of services that people like you and I regularly interact with.As per this definition, a lot of businesses we see today are at the middle of goods and services.The end outcome may be a physical product, but the journey of getting that product and all the interactions in between, whether they’re digital or physical, all come under the service.So, where does Service Design fit into this?A Background of Service DesignAccording to the Nielsen Norman Group, "Service design is the activity of planning and organizing a business's resources (people, props, and processes) in order to directly improve the employee's experience, and indirectly, the customer's experience."Primarily termed by a marketing executive back in 1982, Service Design initially came about as the idea that every service, every product, or everything that we’re building right now is a service.However, this interpretation of Service Design got lost in the midst of the Dot Com Bubble — in this era, everybody was all about digital design and product design, and usability became core to it all.The current world that we live in is an amalgamation of the physical and digital.And in the realm of the digital world, we often don’t get to see the people and processes that are working behind the scenes.That’s where Service Design comes in, encapsulating the entire process, end to end.What is Service Design?Service design is a relatively new discipline that has gained significant recognition in recent years, especially in the context of technology, user experience (UX), and customer experience (CX) design.Essentially, service design is the process of creating, optimizing, and innovating services that cater to the needs and expectations of customers and users alike.The goal of service design is simple, yet complex – to design and deliver services that are effective, efficient, engaging, and enjoyable for everyone involved.In this blog post, we’ll explore the fundamentals of service design, including its core components, scope, methods, and benefits.More specifically, we’ll discuss the three key components of service design – people, props, and processes – and how they interact to create a seamless service experience.We’ll also delve into the scope of service design, including the concept of frontstage and backstage operations.Service Design: The Fundamental ConceptService design, as mentioned earlier, is the process of designing or redesigning services to improve the overall user or customer experience.This process involves a deep understanding of the customers’ needs, preferences, pain points, and motivations.Service design is also about identifying and mapping the various touchpoints, interactions, and processes involved in delivering a service from start to finish.Service design is an iterative process that involves researching, ideating, testing, and refining various aspects of a service.For instance, a service designer may use various design thinking tools and techniques to identify and create personas, customer journey maps, service blueprints, and prototypes.The goal is to create a cohesive and comprehensive service experience that meets (or exceeds) the expectations of the customers.The Three Components of Service DesignAt a high level, Service Design is composed of three major components:People: Any person who creates, uses or interacts with the service or service ecosystem Props: Any physical or digital touchpoints, artifacts needed to execute the Service Process: Any workflows, procedures, or rituals performed by either the employee or the user throughout a service These three components interact with each other to create a seamless service experience for the customers.PeopleThe people component of service design refers to the customers, users, and staff involved in the service delivery. Service designers need to understand the needs and motivations of each stakeholder involved in the service journey to ensure that the overall experience is positive. This could involve interviewing customers, observing staff, or conducting surveys to gather insights.PropsThe props component of service design includes all of the physical and digital elements involved in delivering a service, such as the website, mobile app, signage, furniture, equipment, etc. These elements play a crucial role in shaping the user’s perception of the service and can greatly impact the overall experience. Service designers must ensure that all props are designed to be functional, aesthetically pleasing, and easy to use.ProcessesThe processes component of service design includes all of the steps involved in delivering a service, from the customer’s point of view. This could include the ordering process, the payment process, the delivery process, etc. Service designers must ensure that the service processes are easy to understand, efficient, and effective. They should also design processes that are flexible and can adapt to the changing needs of the customers.The Scope of Service DesignThe scope of service design can be divided into two broad categories – frontstage and backstage.Frontstage refers to all of the activities that the customers can see or experience directly, such as the website, the mobile app, the physical store, or the call center.Backstage, on the other hand, refers to all of the activities that are not visible to the customers, such as the inventory management, the logistics, or the administrative tasks.Service designers must take into account both the frontstage and backstage activities to create a cohesive service experience.They should design processes, systems, and strategies that enable effective coordination between the stakeholders involved in the service delivery. This can involve optimizing workflows, enhancing communication channels, and streamlining operations between different teams or departments.What is Service Blueprinting and Why You Need ItService blueprinting is a tool used by service designers to map out the various touchpoints, interactions, and processes involved in delivering a service. A service blueprint is essentially a visual representation of the service journey, from the customer’s perspective. It includes different layers that help service designers identify pain points, opportunities, and areas for improvement.Service blueprinting typically includes five key layers:The customer journey: This layer includes the different stages involved in the service journey, from the customer’s point of view.Frontstage actions: This layer includes all of the activities that the customers can see or experience directly, such as the website, the mobile app, or the physical store.Backstage actions: This layer includes all of the activities that are not visible to the customers, such as the inventory management, the logistics, or the administrative tasks.Support processes: This layer includes the supporting processes that enable service delivery, such as internal communication channels, workflows, and standard operating procedures.Physical evidence: This layer includes all of the physical and digital elements involved in delivering a service, such as signage, furniture, equipment, or branding materials.Service designers can use service blueprinting to identify areas where the service is falling short or where improvements can be made. This can lead to a better overall customer experience and more efficient service delivery.UX Vs. CX Vs. SDUX (user experience) and CX (customer experience) are two terms that are often used interchangeably with service design.While there are similarities between these disciplines, they are not the same thing.UX focuses on the user’s interaction with a particular product or service, whereas CX focuses on the customer’s overall perception of a brand or company.Service design, on the other hand, is focused on the end-to-end service experience, including all of the touchpoints, interactions, and processes involved in delivering a service.Service design can be seen as an umbrella term that encompasses both UX and CX. This is because service designers need to take into account both the user’s interaction with the service (UX) and the customer’s perception of the service (CX) to create a seamless service experience.Final TakeawayService design is a multidisciplinary and iterative process that involves deep research, ideation, testing, and refinement.By understanding the three key components of service design (people, props, processes), the scope of service design (from frontstage to backstage), and the basics of service blueprinting, service designers can create a service experience that is effective, efficient, engaging, and enjoyable for everyone involved.Service design is a highly collaborative and customer-oriented approach that can lead to increased customer satisfaction, retention, and loyalty, as well as reduced costs and improved efficiency for the service provider.As such, it’s a valuable tool that can benefit a wide range of industries, from healthcare to finance to retail.
The Fundamentals of Service Design: An Introductory Crash Course
As system management becomes increasingly complex, effective monitoring tools are more important than ever.Enter Prometheus and Grafana – a dynamic duo for anyone concerned with system observability.The combination of these popular tools offer a comprehensive ecosystem for monitoring, alerting, and data visualization, providing valuable insights into the health and performance of your systems.Prometheus is an open-source monitoring and alerting system that excels at collecting and storing time-series data, while Grafana is a powerful visualization and dashboarding tool.Together, they provide a comprehensive solution for monitoring, visualizing, and alerting on the performance and health of systems and applications.This guide will walk you through the process of integrating these tools into your system management strategy, offering practical solutions to commonly faced challenges like data overload, lack of real-time visibility, and complex system diagnostics.These crowd favorites form a powerhouse that can handle monitoring, alerting, and data visualization, truly serving as a lifeline to complex system management challenges.Armed with this guide, you'll gain the ability to detect issues swiftly and respond to them effectively, thereby enhancing the overall reliability of your systems.What is Prometheus?A Go-based open source monitoring programme called Prometheus gathers metrics data and saves it in a time series database. It was created by SoundCloud at the beginning in 2012, then in 2016 it joined the Cloud Native Computing Foundation (CNCF). PromQL, a potent query language, is used to query your time series data.Prometheus is a core technology for monitoring and observability of systems, but the term “Prometheus” can be confusing because it is used in different contexts. Understanding Prometheus basics, why it’s valuable for system observability, and how users use it in practice will both help you better understand it and help you use Grafana.Features of PrometheusData Model and Query Language Time-Series Database Data Collection Service Discovery Alerting and Notification Visualization and Dashboards Scalability and Federation Reliability and High Availability Pull and Push Models How PromQL HelpsThe functional query language known as PromQL, or Prometheus Query Language, enables you to choose and aggregate time series data. With its flexibility and strength, you can manipulate your data however you see fit.For example, if you want to retrieve CPU usage metric from a target named "my_app", you would use this:cpu_usage{job="my_app"}What is Grafana?Grafana is an open-source tool for executing data analytics with the aid of metrics that, when combined with scalable dashboards, provide us an understanding of the intricate infrastructure and vast amounts of data that our services handle.Every data source imaginable, including Graphite, Prometheus, Influx DB, ElasticSearch, MySQL, PostgreSQL, etc., is connected to by Grafana.As an alternative, we may create our own custom plugins to link with any data source of our choosing thanks to the open-source nature of the solution.Technically known as time series analytics, the technology aids in the study, analysis, and monitoring of data over a period of time.By providing pertinent data, it enables us to track user behavior, application behavior, the frequency of problems occurring in production, pre-production, or any other environment, the sort of faults occurring, and contextual settings.Features of GrafanaDashboard Templating Provisioning Annotations Kiosk mode and playlists Custom plugins Alerting and alert hooks Permissions and multiple teams SQL Databases Monitoring your monitoring AuthenticationUnderstanding the Grafana DashboardThe dashboards draw their information from connected data sources like Graphite, Prometheus, Influx DB, ElasticSearch, MySQL, PostgreSQL, etc. These are just a few of the numerous data sources that Grafana by default supports.The dashboards have a wide range of visualization options, including the many charts and graphs and geo maps, heat maps, histograms, and other visualizations that a business generally needs to analyze data.On the grid of the dashboard, there are numerous separate individual panels. There are many functionalities for each panel.Getting started with Prometheus and GrafanaAfter installing Prometheus and Grafana, you need to set Prometheus as the data source in Grafana and you can do that using the following steps:Click on the Grafana logo to open the sidebar. Click on “Data Sources” in the sidebar. Choose “Add New”. Select “Prometheus” as the data source. Set the Prometheus server URL Click “Add” to test the connection and to save the new data source. Why use Prometheus and Grafana?There are many reasons why the combination of Prometheus and Grafana can prove to be very powerful when integrated correctly into your systems.For starters, you have control over the metrics you want to report, their origin, and their destination when Prometheus and Grafana Agent are used together.Once the data is in Grafana, a Grafana Mimir database can be used to store it. The combination allows you to monitor and visualize the health and performance of your systems comprehensively, making it easier to detect issues and respond to them effectively.TakeawayOverall, Prometheus and Grafana are popular tools that provide a robust ecosystem for monitoring, alerting, and visualization, making them valuable assets in maintaining the reliability of your systems.The additional functionality of PromQL further enhances the value delivered by this combination, offering granular control over metrics and data.With the ability to accurately monitor health and performance, respond to issues effectively, and visualize data in an intuitive manner, these tools become instrumental in ensuring system reliability.Their power rests in their flexibility, scalability, and the profound insights they provide, making them a game-changer in the landscape of system administration and data analysis.
Mastering System Monitoring with Prometheus and Grafana: Your Handy Guide
We’ve all heard the news: AI is taking the world by storm.As a designer, one would think it to be a grave threat to our profession, as artificial intelligence (AI) is also very much capable of executing creative work.In a lot of ways, AI carries out creative tasks up to some extent that humans have also been doing, if not beyond that.So it’s not hard to notice the potential that AI has to replace human-dependent jobs.In the field of UX Design, however, AI is not likely to take over fully.In fact, from what I’ve learned and observed while using AI tools in my design process, I’ve realized that AI can act as a co-pilot that can enhance our products while working.If you look at the AI tools for designers out there, in particular those that are both image and text generation tools, designers have found that they’re not always very accurate and often require human judgment.With that being said, there’s no reason not to make use of AI tools; they can do wonders in terms of acting as an aid to super-charge your design process.In this article, I share my findings from my first-hand investigation into AI tools for designers and implementing them to boost different stages of the design process.Requirement Analysis Project requirements are integral to the design process, and this is one big area where using digital tools like Notion can be very beneficial.On its own, Notion is a great tool for documentation and project management.But we’ve only gotten to really see its super-charged potential ever since the integration of AI.With AI, Notion has turned into a powerhouse, enabling you to carry out a plethora of tasks with your document, like:Summarizing a requirement document Generating user stories Finding action items Improving your writing (you can even change the language or the overall tone of your writing) Once you have all of these documented and generated, you can put them in a separate file so you can look at it at another time in the future.User Research We’re all familiar with OpenAI’s ChatGPT.This AI model can be used for various aspects of user research for design, including:User Personas Empathy Maps Competitive Analysis Storyboarding Information Architecture Let’s take an example that I worked on:By providing the right prompts, I was able to generate a user persona for someone who frequently uses food delivery apps.ChatGPT provided detailed information about the persona’s demographics, goals, pain points, and behaviors.It also helped with empathy mapping, competitive analysis, and even created a basic layout for the information architecture of the food delivery application.To delve deeper into the user’s pain points, I asked AI to elaborate on the pain points of users who use food delivery applications. The information provided was insightful and could be stored for future reference.Next, I also wanted to also do some competitive analysis.So, I asked the AI model, and the results it popped were able to explain to me how popular food delivery products work, with examples like UberEats, DoorDash, GrubHub, Postmates, and Seamless.In case of storyboarding, while ChatGPT cannot give you image results, it gives you information in frames, and different frames have different illustrations of what the user does while interacting with an application.Through text, ChatGPT gives you a rough direction of what each frame in a storyboard would look like.One con, however, of this model is that it lacks contextual knowledge.So even if you generate such information, you have to take a close look at it and modify it according to your project.Wireframes Wireframe designer is a plugin that is integrated in Figma and allows you to generate wireframes based on prompts.As an example, I asked it to generate the wireframe for the homepage of a food delivery application, and while the result was a bit unconventional (we don’t see many homepages in food delivery applications where there’s a banner on top and different categories of the items available in the menu), it stood out as different.What this example shows us is that this is definitely a step in the future, and wireframes and design generation from text can be way more accurate.Speaking of which, another tool called User ID lets you create wireframes from your paper scripts and sketches directly, and makes them editable (although, they cannot be imported in Figma.High Fidelity Design As we move into high fidelity design, there is another tool called Auto Designer that also lets you create high quality screens from prompts — and they’re highly editable.Just like Figma, this tool is also very collaborative.If you look at the basics of high fidelity design (the layout, spacing, and the structure of your pages), those are all things you have to manage yourself and can’t rely on AI to handle. This is where the creativity gap lies between humans and AI: AI lacks the creative input that is so vital to the design process.Another AI tool that is particularly helpful with the coloring part of the process. is Chroma.Chroma is an AI tool that generates color palettes, and their combination with text.On the platform, you can select colors of your choice and the AI tool will train its model over the colors you select.Finally, the tool gives you the results in the form of color palettes and color swatches with text as well.This is a very interactive tool that lets you swap through colors, get a lot of inspiration, and add them as favorites to add in your high fidelity design. ,Testing Once the high-fidelity design is ready, tools like the Attention Insight plugin in Figma can provide valuable insights.The plugin creates a heat map and a focus map for the screen, showing where users are likely to focus more.This offers valuable insight and information that can be used to make necessary amendments in your design as needed.Takeaway AI tools for designers, although not flawless, can substantially improve your design process.They can serve as an initial point, offer inspiration, and even carry out tasks that save designers valuable time.What AI is definitely not is a substitute for human discernment and creativity.Designers must still scrutinize and fine-tune the results from these tools to ensure they align with the project’s needs and standards.Ultimately, AI tools for designers are just that – tools. They are here to assist us, not supplant us, in our creative endeavors.
AI Tools For Designers: 5 Ways To AI-Power Your Design Process
In the midst of ever-increasing competition, the need to deliver high-quality products is now more critical than ever.And while product quality has always been an essential factor in any industry or field, the aftermath of the pandemic has led to shorter attention spans, meaning it only takes a split second for users to develop an opinion about a website and decide whether to wait or switch over to a competitor.Eventually, the responsibility falls on QA analysts’ shoulders: are they doing enough to achieve a seamless customer experience?The importance of product quality is not lost on us. But are test automation engineers — or anyone involved with QA — doing enough?As a way of improving the efficiency of manual testing, Intelligent Automation Testing came as the new (and quite popular) solution — and for the right reasons.Why Use Automation Testing? The reason why Automation Testing arose in popularity is evident: it saves time, costs, and the human effort to sift through multiple application screens and manually compare the results of various input combinations.On top of that, recording each result manually and conducting the tests repeatedly is a hassle that test automation engineers have gladly replaced with automation testing tools that do the work for them.To put it precisely, Test Automation is the means to achieve bigger, better, and faster QA. Watch this video on QA Test Automation to take a deeper dive into what automation testing can achieve.But all said and done, despite the benefits and opportunities that come with automation testing, it seems that there’s still a need for a better, even more intelligent solution.And the reason why is simple: Automation Testing hasn’t fully delivered on its promise to streamline modern software delivery.Instead, it comes with its own set of challenges, and even created more bottlenecks. Let’s see how.Challenges with Automation Testing Assume you’re working for a mid-sized company that’s still young and undergoing the designing phase of their processes and protocols.All while simultaneously trying to deliver a good quality product.Let’s say you’re hired to automate their regression suite to ensure nothing breaks during delivery. Test automation engineers will mostly be seen designing and writing programs that will run the automated tests on the software.So, you start writing the test cases and immediately identify the lack of best practices followed while developing the product (in this case, the test-ids).Now you have two options:Ask the team to update the code to include the test-ids (which we know will probably never happen), or Work with whatever you have. So you continue writing test cases that, from the get-go, are built to be flaky and possibly flawed.Fast forward, you have a few tests in the pipeline regularly failing — not due to the actual issues in the product but the use of highly dubious selectors.As a result, you end up putting in a lot of your effort, time and company resources into maintaining the tests and fixing them while you could’ve spent it trying to attain an ample percentage of test coverage.Whether you admit it or not, this is what all of us “Automation Testers” have experienced at least once in our careers. It changes our mindset and diverts us from writing a test that would rather ‘Pass’ than ‘Fail’ while finding an actual bug in the product.Clearly, the amount of maintenance effort required in automation testing takes up more time — and more importantly, distracts you from the true results you want to achieve.So, what can be done instead?Enter: Autonomous Testing.What is Autonomous Testing? To put it simply, Autonomous Testing is the next, enhanced version of automation testing.Think about the number of tests you’d have to write for testing each browser in a different OS setting and on many other devices (desktop, mobile, tablets), and then debugging the individual issues.Autonomous Testing removes the hassle and stress of maintenance so that you can focus on the single most important goal of QA testing automation: the ability to simply pass the test by writing one with ample test coverage, and identifying new bugs while you’re at it.As the name clearly suggests, if Autonomous Testing is adopted by test automation engineers, they’d be responsible for less manual work like maintenance and debugging (since testing tools provide the Root cause analysis of the bug, you don’t have to debug manually). This is a win-win situation for everyone involved.And how does Autonomous Testing offer this capability? It brings AI into test automation.How Can Artificial Intelligence Be Used in Automation Testing? AI-based visual test automation offers the ability to identify the root cause of a specific problem, pointing developers to the exact piece of code to be fixed.It introduces the autonomy that lets test engineers focus their attention and energy on attaining more test coverage rather than on writing hundreds of lines of code to validate just how the website visually looks.In the past, we have seen some solutions to overcome this bottleneck of visual testing in test automation. These came in the shape of Pixel and DOM differences validation.But still, there is a downside: both of these have some limitations that don’t translate well into the ideology of Autonomous Testing.This gap gave birth to Visual AI — and Visual Artificial Intelligence Testing.What is Visual Artificial Intelligence? Visual AI is simply artificial intelligence technology being able to see what humans see — and intelligently make visual understanding of what it sees to make decisions and carry out commands accordingly.And as far as web/app automation testing goes, this is a hugely revolutionizing facility for UI automation testing.What is Visual UI testing? In web development, visual UI testing works by running visual tests that detect, analyze, and compare various visual elements of a website or application. By doing so, it ensures that the look and feel of the page is as per the design, and whether or not the required element sections are displayed on the page.Many tools in the industry claim to be the ultimate solution offering visual UI testing, but in my opinion, Applitools and Percy are both at the top, owing to their easy-to-follow starter kits and sufficient documentation found on their websites to help get you started.In this tutorial, we’ll look at Applitools in motion, and you’ll learn how you can set it up and use it to implement intelligent automation testing on your websites/application for visual UI testing.Setting Up Visual AI For UI Automation Testing To conduct UI testing, be it for web or app automation testing, visual AI will prove to be an integral part of the process.To integrate AI in test automation, you want to use automation testing tools that can seamlessly adapt and work with your existing QA testing automation framework.In this tutorial, we’ll be using Applitools with Cypress.(Note: Applitools supports nearly every other popular front-end testing framework. You can find one that you prefer here).For the sake of this tutorial, we’ll be working with a Cypress framework, assuming you have it and know how it works. (If you don’t, let us know and we’ll help you set it up.)Let’s get right into it.Step 1 Install & setup Applitools using the following commands:Step 2 Add the global configuration for your visual tests in the root folder:applitools.config.jsStep 3 First, follow the steps to find your Applitools API key and set it as environment variable APPLITOOLS_API_KEY before running the visual test.There are two options: either set it through your IDE or use the following command:For Mac & Unix:For Windows:Step 4 Design your test.demo.cy.jsReplace the URL in cy.visit() with your web-site/page.Step 5 Execute the sample test using the following command:After your tests run, you should see results in the Applitools Eyes Test Manager dashboard, which will look something like this:Command for executing the sample test in the visual AI test automation toolThe first highlighted column displays the status, and as this is the first build, there is nothing there to compare it with — hence the status “New”. The second column highlights all the devices and viewports we globally set for our visual tests in Step 2. In terms of setting up the visual artificial intelligence testing too, this is pretty much all of it.But let’s rerun the same test and see what happens.Execute the same “run” command again.This time, the dashboard will display this:Rerunning the tests. The automation testing dashboard now shows a 'Passed' status.The tests have the status ‘Passed’ because there is no visual change between the latest web page and the baseline saved in the last build.Now let’s see what happens in the case of a visual change.Add the following code to your test file demo.cy.js after the cy.visit() command.Save and rerun the test. The dashboard will now look like this:Changes in the status when running the automation testing due to a visual change.This time, since there was a visual change, the initial status was ‘Unresolved’ as it wasn’t sure if the changes detected were expected. Therefore, it lets users decide for the first time whether to pass or fail the build.Based on your response, the baseline image will be updated. In addition to this, you can:View the root-cause analysis for the identified difference Create a bug Add a remark Toggle between baseline & current build Highlight the identified difference Specify a portion of the page that will be ignored and won’t be checked for any visual changes And the list continues..From this point onwards, it’s just about adding more and more layers to the AI-based test automation process based on your preferences; add the visual tests in your CI/CD pipeline or integrate Slack to get test results instantly — the world is your oyster.Takeaway While we do have cutting-edge ML/AI technologies weaving their way into the software testing and development process, independently, they’re still not enough.There is still a pressing need to implement best practices, introduce formulated processes, and educate the community accordingly to utilize and understand the real power of using AI in test automation.Revolutionary methods and approaches like Hyper-Automation (which also largely uses AI to automate multiple IT processes) are bound to be more beneficial than risky as we move into the future. To learn how you can overcome E2E complexity with Hyper-Automation, watch this video here.But what can be said with a lot of certainty is that Autonomous Testing is the future we’re looking at — and it only makes sense to start adapting these trends today that will be the new norms tomorrow.
Using Visual AI in Test Automation: A 5-Step Tutorial For Autonomous Testing
Being digitally native presents young companies with a predominant advantage over large corporations:Being able to quickly implement, scale and steer innovative technologies tomaximize profitability through experience, and minimize the time to market. With the advent of intelligent technologies, many startups have managed to leverage their overall growth through personalization strategies.This raises an alarming concern for large corporations:Will they ever be able to match up with digitally native companies in terms of speed of customer experience adaption and enhancement?To answer this question, we need to dive into how Hyper-Personalization in the customer experience strategy can be achieved for large-scale organizations.Evolution of Hyper-PersonalizationThe era of Industry 1.0 was all about mass production.Only quality and quantity mattered. Customer experience was the last priority when it came to budgeting activities.Then, things started to change: customers wanted differentiation and value addition on top of what they were paying for the product. This gave birth to branding.Brands started to market the value addition of their products and created unique propositions to attract customers. Factories started mass customization of products to facilitate various brands from the same assembly line. Some of the innovative companies started to invest heavily in categorizing customers based on their needs. This segmentation approach enabled the brands to customize products as desired by that customer segment.But this wasn’t enough.There were still several untapped customers whose needs were not being fulfilled through basic consumer categorization.In turn, micro-segmentation was born, which let companies tap into more variables related to consumer preferences, buying behavior and interests.With advancements towards the digital era, this approach saw further changes, to the extent that now, customers expect brands to know them at their very first interaction.Today, most customers prefer to purchase from brands that offer personalized experiences. And, more often than not, they will respond to the brand’s marketing messages only if they feel those messages are crafted to their needs.Personalization Vs Hyper-PersonalizationHyper-Personalization is personalization on steroids.In customer experience strategy, ‘personalization’ itself is technically based on contextual data that is used to tailor the experience as per the customer’s need through trend analysis of that particular customer (or the segment to which the customer belongs).On the other hand, hyper-personalization factors in real-time customer data, weighs fresh insights with historical data, and then tailors the experience as per customer preference — and that too in real-time.In essence, hyper-personalization is personalization powered with artificial intelligence to analyze, predict, and service the customer in real-time.Implementing Hyper-Personalization in Your Customer Experience StrategyAs simple as the idea sounds of offering the right experience to the right customer and adjusting according to the next predicted customer action, its actual implementation requires an entire ecosystem modernization.On the whole, the implementation of such a hyper-personalized customer experience strategy may seem like an impossible task, but adequate strategic evaluation of the transformation and prioritizing right will help achieve maximum value as you go about the complete overhaul.1. Evaluate existing digital ecosystemLarge-scale companies that have processes and systems working successfully for many years are least likely to swiftly energize the effort towards hyper-personalization.The biggest challenge is replacing legacy platforms to cloud-based ecosystems. Without the free flow of data in a scalable ecosystem, real-time customer insights cannot be fully utilized.However, from a strategic viewpoint, the long-term benefits of customer engagement (topline revenues, reduced churn, and low cost of superior customer servicing) outweigh the costs involved in the short-term.Just by experimenting with some hyper-personalized customer experience journeys, many organizations start to realize the need for advanced intelligent customer systems.Key Actions:Instead of considering the short-term business case for hyper-personalization in the customer experience strategy, evaluate the benefits for 3+ years. Evaluate the existing customer experience journey across multiple channels against the value garnered through intelligent automated servicing, personalized cross-selling, and marketing. 2. Centralized customer profilingCompanies with a diversified portfolio of products face challenges in sharing customer insights at scale.Such siloed insights are only useful for a particular product, leading to a nightmare for businesses that miss out on the cross-sell opportunities and customer servicing without a centralized customer system. An omnichannel customer experience strategy will just cover the needs of the customer experience journey and fail to predict the next action the customer is going to perform.Complete customer profiling can be achieved through both quantitative data (big data mining) and qualitative data (customer research) and can be combined to form powerful insights for your customer experience strategy.Customer interaction for research has to become a governed process that helps in capturing the emotional elements associated with a product and the brand. Through such a centralized customer profiling system, all channels can benefit from any of the products that the company can has to offer. This system can then be designed to make adjustments in customer profiles automatically through new data.Key Actions:Quantitative customer data alone is not enough. A combination with qualitative customer data will help develop the most impactful customer profile. Operations of such a centralized customer profiling system can become a costly nightmare. Design it to intelligently and automatically adjust profiles based on new events. 3. Using AI to Power Up the Customer Personalization StrategyPersonalization strategies get the companies to a point where they are able to offer the experience based on contextual data.With advancements in artificial intelligence, companies can now capture real-time customer behavior, mix it with the captured emotional state of the customer, and adapt to a serving, selling or retaining mode.There are many customer and channel-related use cases that the brand can use to maximize value by providing the desired customer experience and changing the experience focus in real-time:a) Customer action prediction: Gives the ability to assess the next action the customer is going to perform and display the right message on any particular channel.b) Channel attention prediction: Predicts the customer touchpoint traffic and process glitches, and automatically triggers a suitable response.c) Natural Language Processing: Enables real-time interaction analysis to extract the most relevant insights from any live interaction. Its use can range from gauging the emotional state of the customer through speech analytics to evaluating the propensity to churn through keyword extraction.d) Interaction Assist: Enables the company’s human resources to assist in creating a personalized and pleasant interaction in real-time.Keeping the customer interaction humanized and personalized with the backing of AI is itself an evolving process where interconnected systems like CRM, product catalog, complaint management system, order tracking and others work in harmony.Key Action:Initiate the implementation of your CX strategy by enabling AI to assist human resources by converting ambiguous data into useful insights during the customer interaction. Making this a part of the customer experience strategy will set the pace for the overall transformation. 4. Connected design systemsWhen large-scale organizations aggressively adapt to digital strategies, the design processes and repositories usually lie in silos across multiple divisions of the company.This creates an inherent challenge in reusability of design systems.Not only should these design systems be interconnected, but a free flow of information regarding design principles, libraries, practices and guidelines should be integrated at a cultural level across the organization.To attain maximum benefits from the customer experience strategy, the system should be sufficiently agile to quickly adapt to changing customer needs for both new and existing products.Key Actions:Strongly bind design systems to maintain experience harmony across the product portfolio. Structured flow of design information across the departments should be made a cultural value of the organization. 5. Hyper-personalized content strategyScattered product catalogs and content management systems are one of the biggest challenges of large-scale organizations, especially if the company offers a diversity in products.A centralized product and content catalog becomes the one and only true source of all product-related information to offer a personalized experience in terms of pricing, customer messaging, tailored content generation, and personalized promotions.This system is then actively utilized across all customer touchpoints and with predictive AI, it provides the opportunity to offer the right product at the right time to the right customer to maximize the chances of conversion.The importance of making this a part of the customer experience strategy is further realized by observing customer behaviours:Every 3 in 4 customers will get frustrated if they are shown products that are not relevant to them, and most expect the brands to only recommend products that are relevant to them.Key Action:Personalized product recommendation with the right key message across multiple channels is by far the most attractive experience for customers. Therefore, it should be made an integral part of the customer experience strategy. 6. Automate at scaleAs the AI ecosystem grows with more tools, it will consume more resources.Automation and simplicity of both quantitative and qualitative data collection should be made part of the transformation strategy. The ecosystem should be robust enough to self-correct itself by absorbing data from both internal and external sources.Additionally, automation can also be utilized for activities like content generation, product personalization and customer servicing that constitute an integral part of the overall customer experience strategy.Key Action:Make automation part of the AI transformation strategy. TakeawayFrom a strategic viewpoint, a hyper personalized customer experience strategy may seem like a complete overhaul of the existing ecosystem.In reality, organizations start by creating analytics-driven customer experience to maximize personalization opportunities.Once the practice of data-driven personalization is established, they start introducing bits of AI into facilitating the brand’s human resources towards preferred customer recommendations.Finally, they synergize automation, analytics, and AI in the overall customer experience strategy to offer a wholesome hyper-personalized, omnichannel customer experience at all touchpoints in both marketing and customer servicing.To achieve success in personalized customer experiences, hyper-personalization of your customer experience strategy serves as a revolution to the way large organizations analyze, understand and serve their customers in real-time.FAQsWhat is the hyper-personalization model?The hyper-personalization model uses AI, automation, and analytics to analyze, predict, and service the customer using real-time data on customer behaviours.What is the difference between personalization and customization?Personalization is when you analyze customer data to create a product or add modifications to it that meet your customers’ needs. Customization is what changes the customer makes to the product to meet their specific requirements.
Leveraging AI and Beyond: 6 Customer Experience Strategies For Hyper-Personalization
Unless you have been hiding under a rock, the odds are that you have heard about the fascinating world of Artificial Intelligence and the transformational impact it is going to have on our lives. Certain parts of the euphoria remind us of the dot-com bubble from the ‘90s – when the technology hype hit its peak. There seems to be intense excitement at all levels, from CEOs and analysts to grassroots developers; all are expecting Artificial Intelligence, or AI technology, to not only dramatically change our lives but the world. Well, we really can’t blame them, given all the excitement created by the billions of dollars being invested in the Artificial Intelligence domain. With all the stars perfectly aligned, perhaps it is best to “peel the onion” a little bit in hopes of gaining a better understanding of what AI really is, why now and why all the hype, and how AI technology and AI companies can dramatically change the Telecom Industry landscape, in particular.What is Artificial Intelligence and why the hype now? AI is essentially humans teaching machines how to learn and mimic their intellect.Quite like a toddler- who learns by experience, constantly absorbing information, decoding it and understanding patterns – we feed machines huge amounts of data that create their own algorithms and constantly tweak them to meet their objectives. Over time the program gets smarter and very human-like, i.e., less artificial and more intelligent.ICT & big dataDuring the past few years, some factors have led to AI technology coming closer to unleashing its true potential – mainly, a ton of data becoming available along with faster GPUs for processing it.With billions of things getting connected to the web, there is a tsunami of information being generated. Since the dawn of time until 2004, around 5 exabytes of information have been created and captured. To put that visually, try and imagine a stack of books that spans from earth all the way to Pluto and back 80 times over. Impressive, certainly, but over the last decade, the amount of data being captured has exploded with 90 percent of the world’s collective data reportedly being generated in the past two years alone. And now thanks to advances in processing speeds, computers can make sense of all this information faster and more efficiently.Because of this, tech giants and VCs are pumping the market with cash and new applications and investing into AI companies. China alone has already invested $109BN in this space, and the race between countries is heating up. The reason being that 15-20 percent of the global GDP is forecasted to be linked to AI-related industries by 2025! Mind numbing, isn’t it?IoT solutions1100+ New startups raised 1st equity since 2016, $15.2B Equity funding in 2017 alone, a 141% jump from the previous year, 300+ Companies entered incubations in 2017, 3x more than 2016. Source: CBInsights How is Artificial Intelligence different from Predictive Analytics and Big Data? Are there financial benefits of investing in all three? Often, people inquire what is the difference between Big Data, Predictive Analytics, and Machine Learning. They want to have the ability to differentiate between the three to ensure adequate allocation of resources and to set right expectations with all the stakeholders..Big Data: Collection of “data sets” which are large, complex, and difficult to process using on-hand database management tools.Predictive Analytics: Practice of extracting information from existing data sets in order to determine patterns and accurately predict future outcomes, trends, and behaviour.Machine Learning: Machines performing tasks that normally require human intelligence (visual perception, decision-making, learning, etc.). This takes into account the entire context of the situation.big data analysisIn a nutshell, creating Big Data lakes for gleaning insights and learning is the foundation in which all corporations must invest. Using Machine Learning, the self-learning engine to unlock value from the data, organizations can then extract insights, predict outcomes, and perform human-like tasks. Investment in all three, i.e., Big Data, Predictive Analytics, and Machine Learning is quickly being elevated to table stakes. Business and IT teams need to work “hand in glove” to prioritize the required output. On the inverse, we have found that working in isolation leads to converting the proverbial haystack into a bunch of needles rather than finding the one insight which is critical to business.Should we bet on AI and Robotic Process Automation (RPA)? Everyone seems to be deploying them, should we invest in them now? These, my dear Watson, are the wrong questions to be asking! The metaphorical cart is coming before the horse, if you would. The correct question should be, “How do I use technology to solve my business problem quickly and efficiently?”First, however, let’s dive into both RPA and AI to distinguish the two. The goal is to bridge the journey from Human to Digital labour in the most efficient way. Given the pressures on the bottom line, “Digital Labour” is no longer a consideration, rather, a mandate for most companies across the world. RPA and AI are the two unique sets of tools used to traverse the journey from one end of the spectrum to the other, i.e., Digital Labour = Cognitive Computing (AI) + RPA (Digital work).Robotic Process AutomationBasic RPA consists of automating entry-level transactional, rule-based and repeatable processes. It involves working with structured data and within well-defined parameters in order for the bots to complete the tasks autonomously. However, digital labour is automation driven by self-learning and adaptive technologies. It involves self-learning, digestion of super data sets, hypothesis generation, and evidence-based learning. All fun stuff!Most companies and businesses we speak to love the concept of AI and want to jump right into it. However, it is a journey of realization, meaning you need to ask yourself some questions first. What really are the use cases? Can you articulate the pain points? Can you clearly quantify benefits to help determine whether AI and/or automation is necessary? It is then that collectively decided on which is the best course of action and implementation.What are the telecom specific use cases of AI technology? What are the financial benefits? The telecom industry is at a crossroads. No other industry is driven as much by bits and bytes while at the same time faces disintermediating challenges for the same reasons. Business models are changing, customer expectations and behaviour are constantly evolving, legacy economics and paradigms are becoming untenable. This is why 2014 was the first year when the total global revenue of telcos had marginally negative growth. From then on, every investor relations call is bombarded with questions on increasing revenue, reducing OPEX and CAPEX, and here is where AI technology opens up an opportunity for AI companies.Capital Reduction ($10-12 BN savings over a decade)Between the three telcos in Canada, the collective annual capital spend is ~$10BN to grow and sustain their respective networks. Using this run rate would peg the capital spending over the next decade to be at ~$100BN, even when not taking into consideration a spike of an estimated 40 percent due to an increased spend on 5G and fibre deployments.Given that the lifecycle of technologies has reduced considerably – CDMA lasted 25 years, HSPA possibly 10, and LTE even less. It’s time to recuperate that investment cycle is reducing. In the meanwhile, cost of electronics has gone down, and most of the capital effort is spent on the optimization of networks and installation.At last count, 70 percent of capital cost to upgrade from one technology to another was spent on labour and infrastructure, and only 30 percent on the actual OEM related electronics. That would technically peg the telcos as infrastructure players, vis-à-vis, a pure play telco. However, AI and other combinatorial techs can be leveraged to reduce that labour cost for optimization significantly. A lot of the OEMs have started providing their own software for SON (Self Optimizing Networks), but that would really be akin to asking the wolves guard the sheep. Telcos need to have their POV and build on it over time, to ensure basic hygiene and best practices are kept in-house. Also, predicting usage of subscribers by geography and ensuring the network is dimensioned correctly will not be done on the side of the desk of engineers, rather, through deep scenario analysis and planning, considering all parameters. Lastly, optimization of spectrum auction pricing can be done using game theory and decision trees. Given the appetite of operators to spend billions on it, we expect a healthy dose of AI to be used to save thousands of consultant hours and save millions (if not billions) of dollars in the process.We estimate at least 10-15 percent ($10BN) of the capital envelope can be reduced using cognitive technologies holistically across the process.OPEX Reduction ($3-5BN savings over a decade)Predictive Maintenance: The ability to fix problems with telecom hardware (such as cell towers, power lines, etc) before they happen and by detecting signals that usually lead to failure. This significantly reduces downtime and dramatically improves Customer Experience and churn metrics.Video Analysis: Instead of sending field workers to check on-site hardware periodically, AT&T has invested heavily in drones and in AI to analyze video data captured, which proactively raises flags for the field team to go in and fix issues.Self-Driving Networks: In the future it’s likely that governments might ban humans from driving cars as it is a reckless or unnecessarily risky activity. Similarly, there is no reason why we shouldn’t be done away with the Network Engineers! A perfect world really would be where networks would run themselves; away from our grubby, inefficient hands. This provides an opportunity for cost-cutting and efficiency gains. Ideally, we should not need so much staff to manage operator networks. A lightly staffed network powered by an economical but sophisticated AI will look more desirable than ever. That is truly aspirational today, but something to work towards.Customer Service ChatBots: Every telco customer calls in four times a year at an average cost of $15/call for password resets, billing inquiries, or changing plan details. This OPEX of ~$2BN/year across the three operators can be dramatically slashed by using chatbots which are equipped to do the same. Automating 10-15 percent of the customer service inquiries, routing customers to the proper agent, and routing prospects with buying intent directly to salespeople will make a material impact on the bottom line! Further, leveraging NLP, sentiment analysis and text to speech virtual customer service agents could be used to replace humans, with the customer not being able to distinguish one from the other. That could be a tangible reality in 5-10 years’ time; taking the cost down from $15 to 15 cents per call.Speech and Voice Services for Customers: Allowing customers to browse, explore, and buy content by spoken word rather than remote control (e.g., DISH network and Amazon’s Alexa partnership).Security and Fraud: Detect fraudulent activity in credit-card transactions or identifying web traffic to the website and customers, looking to exploit network and infrastructure vulnerabilities.Revenue Enhancement I algorithms can help combine historic patterns, psychographic analysis and behaviour (plus “look-alike” patterns) with ongoing real-time engagement to provide relevant, targeted, contextual experiences for consumers. The outcome will be upselling recommendations and offers, helping improve the conversion rate of offers, and enabling incremental wallet share. The same algorithms could be potentially leveraged to predict subscribers willingness to pay a particular price for a product, estimate product price elasticities, and quantify sales leads likelihood to close.How do we tackle AI as an organization? How much investment is required? It is best to use a good framework to get the ball rolling organization-wide. Some frameworks that we have used in the past include:Cognitive strategyCognitive StrategyHost an innovative workshop that showcases the potential of cognitive discovery, With perceived value in the forefront, identify, and rank use cases, Who? How? What? Dive into the current state of the use cases while being sure to include areas for improvement (scale, errors, non-blue etc.), Present a finalized cognitive strategy to key stakeholders for approval based on prioritised use cases.Detailed AssesmentDetermine detailed use cases and a variety of scenarios, Establish concrete user personas of user on the cognitive automation platform, Define content source(s), type of content, system interactions and integrations required, and system of record, Determine what operational requirements are needed to support the solution (Operational Management, Operational Reporting etc.), For training and configuration purposes, identify all required attributes and level of content annotationsSizing and Investment CaseEstablish criteria to estimate the beneficial impact on the organization, Determine the size of this potential business benefit – both quantifiable and non-quantifiable, Establish a high-level solution concept overview, Estimate the required investment required, Detailed solution blueprint, Build and train, Deploy and maintain, Confirm and validate benefit of investment case with key stakeholders, Conclude investment case for approval and fundingWe understand that AI can be intimidating and complicated, which is why we encourage you to engage us for a better understanding of the foundational capabilities, needed to get an AI program up and running. From building use cases, decision-making paradigms, economic impact analysis, choosing governance model and platforms to be deployed, we have gone through the process a few times. Every business is unique; we look forward to learning more about your specific needs to determine how we can accelerate your digital transformation.
From Self-Driving Cars to Self-Driving Networks: Rise of AI in Telecom
The more enriched an experience, the more dopamine is released into our brain and more profound is the interactivity associated with it. The feel-good factor or the user experience (UX) of any product, platform or service is the quintessential key to deliver a prolonged and definitive image of the brand in the mind of the customer. But how we create that indelible mark in the user’s mind or what technological advancements will lead to enhance the effect, undoubtedly needs our attention as we progress towards the UX of tomorrow. Can artificial intelligence lead Chatbots distinctly be the buddies in changing the future of UX?Let me first take you through what is at the heart of past and present UX paradigms, what has made the UX of yesterday and today successful, and what technologies have been employed in realizing it.Cave drawings, tapestries and even written manuscripts provided a UX, wherein storytellers, oracles and poets would “hard-code” their messages. These technologies were to take a life of their own through the ages, with abstract content engaging its audience’s imagination through reflection and thought. Whether sculpted, drawn or written, its purpose was to convey an intended natural meaning, even when done so artificially in hindsight.Progressive Advancement in UX With the advent of modern computing in the 20th century, engineers attempted to “hard-code” computers to interact with users. It was limited and primitive by today’s standards, but it was something new! Even when projecting into a fictional future, HAL 9000 from Space Odyssey, the original Robocop, and Star Trek’s LCARS voice interface were deliberately designed to sound and feel mechanized. They meant to convey a sense of artificiality to the viewers of the 60’s through 90’s, and for a good reason. Human-machine interface grabbed attention of that era’s audience simply by the virtue of novelty; it needed to be dichotomized from human-human interaction.However, with the changing times, it is not the same today where the term “AI” is taken for granted. We have moved along in our expectations of what AI should be capable of. Today’s chatbots and Interactive Voice Response (IVR) systems – therefore – are not meant to sound or feel mechanized and boring. They actively engage users by sounding more “human.” And it will be far more interesting in the immediate future. That will be the time of ontology in its truest form, where intelligent systems will actively drive the user, and not the other way around!We are soon entering the age of evolved chatbot customer experience that will concurrently span a multitude of modalities – not (just) text. It will rely on voice interaction, mixed-reality graphics, gestures, and cybernetic interfaces, all topped by AI concurrently performing linguistic and behavioural analyses. Undoubtedly it would be a truly enriched and immersive user experience that will know how to grab the user’s attention at will.A Time of Multimode User Interaction Now that we are steadily stepping onto the time of ontology, with intelligent systems driving the user to take his/her decisions, it’s time to audit and learn how certain existing content presentation techniques, UI designs, and UX methods might stand the test of time. And the role advancements in Artificial Intelligence (AI) and Augmented/Mixed Reality (AR/MR) will play in harnessing their true potential.Human language, when textualized, is just one mode of communication. Advances in Natural Language Processing (NLP) have made great strides within this modality. Supervised machine learning techniques, such as Support Vector Machines (SVMs) can now extract relevant information from text if trained to do so for a given set of contexts (domains.)By introducing Neural Networks and Deep Learning techniques, NLP systems can even be made to classify the intent more holistically – and in a limited fashion can readily uncover the underlying sentiment behind the textualized content as well.Deep Learning and natural language processingBody language, tone of voice and selective vocal emphasis are still additional modes of communication that needs consideration. These added modalities can provide far greater cues into the intent and meaning behind linguistic expression. Recently, two teams from Nanyang Technological University of Singapore, Carnegie Mellon University in the USA, and Instituto Politecnico Nacional de Mexico have independently published research papers validating the methods and practical efficacy of Multimodal Sentiment Analysis (MSA) by adding information from video and audio content along with established NLP techniques. And it is the author’s opinion that MSA will gain considerable traction in terms of the practical application of NLP in chatbots.NLP in chatbotsFurthermore, incorporating physiological biometrics to this analysis (e.g. either non-intrusively or using wearables) can make machine learning systems even more effective and efficient. For instance, adding heart rate, voice fluctuations, and posture can provide substantial information about the user’s intent. Adding these subtle modalities can propel an interactive AI much closer to realizing true ontology; to not just do what the user seemingly asked for, but what the user may actually have wanted or needed.An Era of On-Demand Mixed Reality Even in current times, people tend to own more than one device and can switch between them part-way through their task. Studies have shown that up to quarter of users can switch to a tablet from their smartphone and more than half switch to a laptop midway through any given activity. With the advent of new and more creative tools, it is only reasonable to expect that omnichannel UX will require absolute consistency in the age of multi-device use-cases.UX therefore will not be limited to a simple “flat 2D screen” interaction but rather involve AR/MR as is the case with devices such as Microsoft’s Hololens. AR/MR computing platforms will need to include any given device they identify, and interoperate with it at run time. Therefore, such platforms will have the capability to recognize and connect to environmental sensors when entering a room, or identify and interact at run time with smart outdoor cameras for enhancing the user’s perceptual UX. Individual applications will not need to re-implement any lower-level interfaces and would bring in a fine seamless interaction for the user.game scenario of Microsoft HololensPhoto by Microsoft / CC BY 2.0 However, simply immersing a user into graphically rich, and detailed contextual content may not be enough to keep them engaged in the long haul. Can you quickly recall the mechanized voices of fictional AI’s of the 60’s-90’s? Agreed, they sound boring by today’s standards – but were novel and “hip” during their respective eras. The same may be the eventual aftermath of AR or MR once the “novelty honeymoon” is over. To ensure that the users truly benefit and remain engaged, humanizing the digital experience will be as necessary tomorrow as it is today for UX and UI, and more.Humankind’s initial experimentation with the art of myth and storytelling, though synthetic, is in fact the oldest known technique to share experiences through analogy. It is this technique, that remains at the heart of humanizing the digital UX. With the abundance of content existing on the web, being different will continue to be critical. Creating a story around a given product and inviting users to become a part of it might be the easiest way to engage them. Perhaps, in the era of MR/AR, such storytelling will be of vital importance to let the user immerse into the experience – thereby staying engaged throughout the UX.Conversational interfaces may also prove invaluable in engaging the user to intuitively access content and assist in decision making. Currently, such interfaces, or UI – though effective – are limited to text- or voice-based chatbots. However, conversational interaction for AR or MR will be fundamentally different due to the enriched content rendered within an arbitrary 3D space. Free from the confines of a 2D “flatland,” UX of the future will demand digital content to be more than what is currently accessed over the web. UX designers and engineers alike will need to prepare AI for verbal, as well as cues to interact with the users via augmented reality or mixed reality; not just by displaying audible or visual content, but by introducing key micro-interactions through evolutionary storyboarding that trigger the user’s active thought process.game scenario of Microsoft HololensCombining conversational paradigms and on-demand storytelling to create engaging UX will require an abundance of information processing power and storage capacity, whether onboard an AR/MR device, or distributed across a network of devices and appliances. And certainly, it will require substantial content generated at run-time in addition to existing pre-processed and raw data. The key demand for businesses and social networks of the future will be in how to restructure and optimize their content and UI experiences so that it is served readily through AR/MR interfaces. It will not just be flat UI, image and text on a website; but disparate content and metadata that can be rendered on-demand in the form of virtual objects within an augmented voxel space as part of a storytelling experience through AR or MR.Artificial Intelligence Personified Let’s take a closer look at how content and data served through various AI led technologies will change the perspective of UX designers, budding technology entrepreneurs as well as mature technology professionals’ understanding of the UX and its future. We have already witnessed the explosion of chatbots and smart machines within the market due to their versatile applicability in implementing conversational AI. App fatigue may further fuel the growth of chatbot assistants – so as to delegate sifting through apps and content based on their utility for a user’s specific personality, needs, and habits. Additional drivers in favour of chatbots are top brands who are directly integrating them into their customer experience channels starting from direct marketing through customer service and support, to direct sales – thereby increasing efficiency.Business and social websites too publish their Frequently Asked Questions (FAQs) collectively. Yet they also include chatbots to fetch the underlying content from a single source on demand structured data repository within a Content Management System (CMS). However, such hard-coded content may not necessarily connect with users in a way that a natural dialogue would. In an age, where we are rapidly advancing towards conversational commerce, businesses and individuals alike will need to ensure that they are ahead of the game in capturing their audience. This can only be achieved if they can repurpose abstract information to generate responses from raw data – not such that AI chatbots create their own language – but that they are capable of dialogue with their audience to connect at a personal level.game scenario of Microsoft HololensHowever, such chatbots – might always be tainted with the perception of bias towards the business they are designed to generate. Instead of succumbing to the business of bots, users of tomorrow will demand independent aggregator assistant bots not linked to a single platform, but capable of automatically analyzing the user’s needs, all available products and services, their price and value, and act as an advisory assistant without vendor bias. Development and productizing of such “untethered” buddy chatbots are the next big opportunity for the start-ups and businesses of today.Enhancement of the resulting UX can be exponential if additional modalities are included within the user interaction. Consider that upcoming trends for UX point to the proliferation of AR/MR in the immediate future.Putting it all Together In the pursuit of advancement, we forget sometimes what really drives the technological continuum. As social beings, we do not really embrace technology for technology’s sake. We do so to enrich our interactivity and enhance our experiences. It is no secret therefore, that the more targeted and emotionally uplifting the UX and UI, the more rewarding is its perception by the human brain. Only then is it able to capture a user’s attention and imagination – irrespective of technology used to create that UX or UI design.In the coming years, it will become even more important for product and UX or UI designers to include the “human” element. In the next few years, conversations are the next layer of UX and user interaction. Chatbots in their current state might not be able to handle the required complexities arising from the expectations of today’s users. Advances in machine learning have certainly improved the adaptability of current chatbots in the UX. But the art of storytelling dependent heavily on contextual nuances and creativity are still very much beyond the scope of current technology. In the age of conversational commerce, it will become crucial to consider the dynamics of natural conversational dialogue, and impartiality of presented information to truly capture the attention of the user.how artificial intelligence helps human robot interactionThe future of UX will indeed be extremely interesting for those of us who witness it in full swing. With a rich plethora of immersive content, interactive UI designs, and customized experiences available on-demand and with additional advances in human-machine interfaces to directly link the human brain to information access peripherals, such interactions can eventually become part of the user’s active thought process. But until a time, humans practically become cyborgs, “un-tethered” buddy chatbots rendered as human-like guides within AR/MR environments may act as the user’s “inner voice” so as to assist in decision-making – via conversational discourse as well as through micro interactions in the UX journey. Perhaps taking the form of the user’s own “mirror image” to look and sound familiar, or that of the user’s idol so as to be more convincing, such personifications of AI, with help from augmented reality or mixed reality, may be key to the UX of tomorrow.Want to learn more? Let’s talk
Buddy Chatbots, AR/MR, and UX: the User Experience of Tomorrow
Customer experience transformation is a multifaceted process that relies on various factors, including:Channel optimization, Systems/technology enhancement, and Customer journey transformation. By focusing on these key areas, businesses can create a holistic approach to improving customer experiences.In this comprehensive deep-dive article, we will delve into each of these elements, providing insightful details and practical strategies to help organizations drive meaningful changes in their customer experience initiatives.By understanding the importance of channel optimization, leveraging advanced systems and technology, and reimagining the customer journey, companies can unlock new opportunities for growth and establish a competitive edge in the market.Keep reading as we explore these crucial aspects and uncover the secrets to delivering exceptional customer experiences.A. Channel Transformation: Enhancing Customer Touchpoints1. Call CenterSmart Agent SchedulingEffective agent scheduling ensures the right agents with the appropriate skills are available to handle customer inquiries. By leveraging data analytics and forecasting algorithms, organizations can optimize agent schedules, minimize wait times, and improve customer satisfaction.Smart Agent RoutingSmart agent routing utilizes intelligent algorithms to route customer queries to the most suitable agent based on factors such as language proficiency, expertise, and customer history. This ensures customers are connected to the right resource, leading to faster resolutions and improved customer experience.Priority CallingImplementing priority calling mechanisms allows organizations to prioritize urgent or high-value customer calls. By assigning appropriate priority levels, critical issues can be addressed promptly, resulting in improved customer satisfaction and loyalty.First Touch ResolutionFirst touch resolution focuses on resolving customer issues during the initial interaction, eliminating the need for follow-up contacts. By empowering agents with comprehensive knowledge bases, streamlined processes, and decision support tools, organizations can enhance their ability to resolve issues promptly, minimizing customer effort and improving satisfaction.Call Center OutsourcingOutsourcing call center operations can be a strategic decision to enhance scalability, cost-efficiency, and expertise. Organizations can partner with specialized service providers to ensure round-the-clock availability, multilingual support, and access to advanced technologies.AHT Insights & OptimizationAverage Handling Time (AHT) insights and optimization involve analyzing and streamlining call center processes to reduce call duration while maintaining service quality. By leveraging analytics, organizations can identify bottlenecks, automate repetitive tasks, and enhance agent productivity, leading to shorter call durations and improved efficiency.Agent EmpowermentEmpowering agents with the right tools and resources is crucial for delivering exceptional customer experiences. Key components of agent empowerment include agent effort scoring.Agent Effort ScoringAgent effort scoring measures the effort required by agents to resolve customer issues. By assessing the complexity of tasks and providing relevant tools and training, organizations can reduce agent effort and improve their ability to deliver efficient and effective solutions.AI Fraud Detection & PreventionIntegrating AI-powered fraud detection systems within call centers helps identify and prevent fraudulent activities, protecting both customers and the organization. Advanced algorithms analyze patterns, detect anomalies, and provide real-time alerts to agents, enhancing security and trust.2. Self-careWeb & AppOffering intuitive web and mobile applications enables customers to access self-service options, manage their accounts, and resolve basic queries independently. User-friendly interfaces, personalized content, and seamless integration with backend systems contribute to an enhanced self-service experience.Digital IVRDigital Interactive Voice Response (IVR) systems leverage natural language processing and speech recognition technologies to enable customers to interact with automated systems in a conversational manner. This streamlines self-service processes and reduces customer effort.Centralized Real Time Complaint Management SystemA centralized complaint management system consolidates customer complaints and requests, providing a unified view for agents and enabling effective tracking, resolution, and analysis of customer issues. This helps organizations identify recurring problems and implement targeted improvements.Digital Kiosks & VendingDigital kiosks and vending machines provide convenient self-service options in physical locations, such as retail stores or service centers. Customers can perform tasks like bill payments, SIM card issuance, and device troubleshooting independently, reducing queues and enhancing the overall in-store experience:Financial Self-ServeFinancial self-serve options enable customers to manage their account balances, make payments, and view transaction histories through self-service channels. This empowers customers with greater control over their financial interactions, reducing reliance on manual assistance.GSM Self-ServeGSM self-serve capabilities allow customers to perform SIM card-related tasks, such as activation, deactivation, and plan changes, through self-service channels. This reduces the need for customer care interventions and enhances convenience.SIM Issuance and ReplacementStreamlining the process of SIM card issuance and replacement through self-service kiosks or vending machines reduces wait times and improves the overall customer experience.3. Walk-InPhysical Experience ManagementPhysical experience management focuses on optimizing the in-store experience for customers. It includes factors such as store layout, signage, staff training, and wait-time management, ensuring a pleasant and efficient in-person interaction.Self-Serve ExperienceProviding self-service options in physical stores, such as self-checkout counters or information kiosks, allows customers to independently access information and perform transactions, reducing reliance on staff and enhancing convenience.4. IVRVoice BiometricsVoice biometrics enable secure customer authentication by analyzing unique voice patterns. This eliminates the need for customers to remember passwords or answer security questions, streamlining the IVR authentication process and enhancing security.Speech Recognition - Speech to TextSpeech recognition technology converts customer speech into text, enabling automated transcription and analysis of IVR interactions. This helps organizations identify customer sentiments, extract valuable insights, and identify areas for improvement.Personalized IVRPersonalized IVR experiences leverage customer data and preferences to tailor IVR menus and prompts according to individual needs. This minimizes customer effort, speeds up call routing, and improves overall satisfaction.5. Social MediaSocial Media ListeningSocial media listening involves monitoring social media channels to understand customer sentiment, identify emerging trends, and promptly respond to customer queries or concerns. This real-time feedback enables organizations to proactively address customer issues and engage in meaningful conversations.Smart Lead GenerationSocial media platforms provide opportunities for lead generation through targeted advertising, content marketing, and social engagement. By leveraging data analytics and machine learning algorithms, organizations can identify and engage with potential customers, driving business growth.Sentiment AnalysisSentiment analysis analyzes social media posts and comments to determine the sentiment associated with a brand, product, or service. This helps organizations gauge customer perceptions, identify areas for improvement, and develop effective strategies to enhance the overall customer experience.Social Media AutomationAutomation tools facilitate efficient management of social media interactions, including content scheduling, response automation, and sentiment tracking. These tools enable organizations to scale their social media presence and deliver timely and consistent customer engagement.6. Smart BotVirtual AssistanceVirtual assistants, powered by artificial intelligence and natural language processing, provide automated support to customers across multiple channels. They can handle routine queries, provide personalized recommendations, and guide customers through self-service processes, improving efficiency and convenience.AI-Powered Contextual ChatContextual chatbots use AI algorithms to understand customer intent, provide relevant responses, and engage in natural, context-aware conversations. This creates a more interactive and personalized customer experience, mimicking human-like interactions.Social OTT CentralizationSocial OTT (Over-The-Top) centralization integrates various messaging platforms, such as WhatsApp or Facebook Messenger, into a unified customer communication platform. This streamlines customer interactions, allowing agents to respond to messages from multiple platforms in a centralized manner.B. Systems Transformation: Enhancing Operational Efficiency1. Centralized Product CatalogA centralized product catalog consolidates product and service information, enabling consistent and accurate offerings across multiple channels. This simplifies product management, reduces errors, and ensures a seamless customer experience.2. Service Order ManagementService order management systems streamline the end-to-end process of fulfilling customer requests, from order placement to delivery and activation. This improves order accuracy, reduces processing times, and enhances the overall service experience.3. Connect Knowledge Management SystemA connect knowledge management system provides a centralized repository of product information, troubleshooting guides, and FAQs. This equips customer care agents with accurate and up-to-date knowledge, enabling efficient issue resolution and ensuring consistent customer support.4. Zero Touch Operations100% Auto Help Request Processing - Zero Touch HelpZero touch help aims to automate the handling of customer requests without requiring manual intervention. Through intelligent automation, organizations can enable self-healing systems, self-service options, and AI-powered resolutions, minimizing the need for human involvement.RPARobotic Process Automation (RPA) automates repetitive and rule-based tasks by utilizing software robots. By deploying RPA in backend processes, organizations can enhance operational efficiency, reduce errors, and accelerate response times.Smart Service UpdatesSmart service updates leverage machine learning and predictive analytics to proactively identify potential service disruptions or outages. This allows organizations to provide timely notifications to customers, manage expectations, and minimize the impact on the customer experience.5. IntegrationsNetworkIntegrating customer experience transformation initiatives with the network infrastructure enables proactive monitoring, self-healing capabilities, and personalized service delivery. This ensures a seamless experience for customers and reduces the likelihood of service disruptions.BSS - CRM, Charging Billing, Product Catalog, Order ManagementIntegration of these systems enables end-to-end visibility and process automation. This integration facilitates accurate billing, personalized offers, and seamless order fulfillment while enabling one-window view for the customer agents as well.6. Agent ProgramAgent TrainingComprehensive agent training programs equip customer care agents with the necessary skills, product knowledge, and communication techniques to deliver exceptional customer experiences. Continuous training and upskilling ensure agents stay updated with evolving customer expectations.Agent Screening & RecruitmentImplementing robust screening and recruitment processes helps identify candidates with the right aptitude and customer-centric mindset. By selecting agents who align with the organization's values and objectives, organizations can build a customer-centric workforce.Agent Target Setting and MeasurementSetting clear performance targets and measuring key metrics, such as average handling time, first contact resolution, and customer satisfaction, ensures accountability and enables continuous improvement. Regular performance evaluations and feedback sessions further drive agent excellence.Smart Real-Time NotificationsReal-time notifications provide agents with contextual information, updates, and relevant customer insights during interactions. This empowers agents to deliver personalized and informed responses, enhancing the overall customer experience.7. Crowdsourcing PlatformTestingCrowdsourcing testing initiatives involve leveraging a pool of external testers to validate new features, identify bugs, and provide feedback on the customer experience. This accelerates the testing process and ensures a robust and error-free product launch.Content GenerationCrowdsourcing content generation allows customers to contribute reviews, ratings, and user-generated content, such as tutorials or troubleshooting guides. This fosters a sense of community, provides authentic information, and enhances the overall customer experience.Innovation & IdeasCrowdsourcing innovation and ideas involve gathering suggestions and feedback from customers to drive product and service improvements. By involving customers in the innovation process, organizations can align their offerings with customer needs and preferences.Customer 2 Customer (C2C) ServicingCustomer 2 Customer (C2C) servicing leverages the power of the customer community to provide support and assistance to fellow customers. This peer-to-peer support network allows customers to share their knowledge, experiences, and solutions, fostering a sense of collaboration and empowering customers to help each other.8. Customer Profiling SystemUpsell AnalyticsA customer profiling system utilizes data analytics to identify opportunities for upselling and cross-selling. By analyzing customer behavior, preferences, and purchase history, organizations can personalize offers and recommendations, increasing revenue and customer satisfaction.Churn Prediction and ManagementChurn prediction models use historical data and predictive analytics to identify customers at risk of churn. By proactively targeting these customers with retention strategies, organizations can reduce churn rates and preserve valuable customer relationships.Next Best OfferNext best offer algorithms analyze customer data and preferences to determine the most relevant and personalized offers for each customer. This enables organizations to deliver targeted promotions and recommendations, increasing customer engagement and loyalty.Loyalty EngineA loyalty engine consolidates customer loyalty programs, rewards, and incentives into a unified platform. By implementing gamification elements, personalized offers, and tiered reward structures, organizations can foster customer loyalty and advocacy.9. One View/One WindowAgent Virtual AssistantAn agent virtual assistant provides real-time insights, recommendations, and suggested actions to support customer care agents during interactions. By leveraging AI and machine learning, agents can access relevant information and resources efficiently, improving their productivity and effectiveness.UI Design Revamp for AgentRevamping the user interface (UI) design for agent-facing systems ensures a seamless and intuitive experience for customer care agents. User-centric design principles, streamlined workflows, and easy access to information empower agents to deliver efficient and personalized customer support.KMS Driven Information ArchitectureA knowledge management system (KMS) driven information architecture organizes and structures information in a way that facilitates easy access and retrieval for both customers and agents. This ensures consistency, accuracy, and quick resolution of customer queries and issues.C. Customer Journey Transformation: Fostering End-to-End Experiences1. GovernanceVoice of Customer ForumA voice of customer forum serves as a platform for capturing customer feedback, suggestions, and insights. By actively listening to customer voices, organizations can identify pain points, prioritize improvements, and align their strategies with customer expectations.2. Omni-Channel Experience EnablementOmni-channel experience enablement focuses on providing consistent and seamless experiences across all customer touchpoints, whether digital or physical. By integrating channels and data, organizations can deliver personalized and contextually relevant interactions throughout the customer journey.3. Digital Channels Journey RevampRevamping digital channels involves optimizing websites, mobile apps, and other digital touchpoints to enhance usability, navigation, and overall user experience. This ensures a frictionless and engaging digital journey for customers.4. Physical Channels Journey RevampRevamping physical channels, such as retail stores or service centers, involves reimagining the customer journey, enhancing in-store experiences, and providing seamless transitions between digital and physical touchpoints. This creates a unified and consistent customer experience.5. Channel Servicing Analytics & InsightsChannel servicing analytics and insights involve leveraging data and analytics to understand customer behavior, preferences, and interactions across various channels. This enables organizations to optimize channel performance, personalize experiences, and drive customer satisfaction.6. Customer ResearchCustomer Integration LabA customer integration lab serves as a dedicated space to test and validate customer experience initiatives, prototypes, and new features. By involving customers in the design and development process, organizations can ensure that their offerings meet customer needs and expectations.Customer Integration ProcessThe customer integration process involves engaging customers in co-creation sessions, surveys, interviews, and feedback loops. This collaborative approach helps organizations gain valuable insights, validate ideas, and build customer-centric solutions.Customer RecruitmentStrategic customer recruitment ensures the inclusion of diverse customer segments and personas in research and testing activities. By representing the customer base accurately, organizations can gain comprehensive insights and design experiences that cater to different customer needs.Organization Design Thinking MindsetAdopting a design thinking mindset across the organization fosters a culture of innovation, empathy, and customer-centricity. This involves encouraging cross-functional collaboration, embracing iterative problem-solving, and valuing customer feedback throughout the organization.7. Auto CSAT, NPS Process & InsightsAutomating the customer satisfaction (CSAT) and Net Promoter Score (NPS) processes enables organizations to collect feedback, measure customer sentiment, and identify areas for improvement in real-time. By analyzing CSAT and NPS data, organizations can gain actionable insights and prioritize initiatives that enhance the customer experience.By focusing on these various dimensions of customer experience transformation, large enterprises can cultivate a customer-centric organizational culture, optimize operational efficiency, and deliver exceptional experiences at every touchpoint. Embracing these strategies and technologies will not only result in short-term benefits, such as improved customer satisfaction and increased revenues but also position organizations for long-term success by aligning with the evolving needs and expectations of their customers.8. Customer Journey AnalyticsCustomer journey analytics involves the systematic collection, analysis, and interpretation of data related to customer interactions and experiences across various touchpoints. By leveraging advanced analytics techniques, organizations can gain deep insights into customer behavior, preferences, pain points, and moments of truth throughout the customer journey.9. Journey MappingJourney mapping involves visually representing the end-to-end customer journey, capturing customer touchpoints, emotions, and key interactions. By mapping out the customer journey, organizations can identify opportunities for improvement, uncover pain points, and design targeted interventions to enhance the overall experience.Behavior AnalysisBehavior analysis focuses on understanding customer actions, such as website browsing patterns, app usage, purchase history, and interactions with customer support. By analyzing customer behavior, organizations can identify patterns, segment customers, and personalize experiences based on individual preferences.Predictive AnalyticsPredictive analytics utilizes historical data and machine learning algorithms to forecast future customer behavior and preferences. By predicting customer needs and anticipating their actions, organizations can proactively deliver personalized experiences and tailor their offerings to meet customer expectations.Sentiment AnalysisSentiment analysis involves analyzing customer feedback, social media mentions, and online reviews to gauge customer sentiment and perception. By monitoring sentiment, organizations can identify areas of dissatisfaction, address negative sentiment promptly, and amplify positive experiences.Real-time Journey OrchestrationReal-time journey orchestration combines data analytics, automation, and decision-making capabilities to deliver personalized experiences in real-time. By dynamically adjusting interactions based on customer context and preferences, organizations can create seamless, relevant, and engaging journeys for customers.10. Voice of Customer AnalyticsVoice of customer analytics involves analyzing unstructured customer feedback, such as survey responses, call transcripts, and chat logs, to extract actionable insights. By understanding the voice of the customer, organizations can identify emerging trends, uncover hidden issues, and make data-driven decisions to improve the customer experience.By harnessing the power of customer journey analytics, organizations can gain a holistic understanding of the customer experience, identify areas of improvement, and take proactive steps to deliver personalized and seamless journeys. This data-driven approach enables organizations to continuously optimize the customer experience and stay ahead of evolving customer expectations.Final TakeawayCustomer experience is a pivotal business driver that organizations must prioritize.Particularly so in today's competitive landscape, achieving excellence in customer experience is paramount.This comprehensive article has laid a roadmap for you to prioritize your channel, systems, and customer journey transformation to drive CX excellence and master it.By embracing strategies like smart agent routing, AI-powered chatbots, omni-channel experiences, and predictive analytics, businesses can not only enhance immediate customer satisfaction but also foster long-term loyalty.As the digital landscape evolves, it's imperative for organizations to proactively adapt and align with their customers' changing needs. By implementing these strategies, businesses can position themselves at the forefront of customer-centric innovation, ensuring sustainable growth and success.
Mastering CX Part 2: The ‘How’ of Customer Experience Excellence
In today's cloud-centric world, managing infrastructure has become increasingly complex.As organizations strive for scalable, flexible, and cost-effective solutions, cloud providers like Amazon Web Services (AWS) have gained immense popularity.However, setting up and managing AWS resources can be daunting — this is where Terraform, an open-source infrastructure as code (IaC) tool, comes into play.Why Use Terraform on AWS?Terraform enables you to define and provision infrastructure resources across multiple cloud providers, including AWS, in a declarative and consistent manner. By treating your infrastructure as code, you can automate the creation, modification, and destruction of resources.In this blog, we won’t just understand the power of Terraform; in fact, we will go through an in-depth exploration of Terraform integration with AWS, providing you with a comprehensive guide to creating and managing your AWS infrastructure using Terraform.Whether you are new to Terraform or an experienced user, this blog will equip you with the knowledge and practical examples needed to accelerate your infrastructure provisioning and management processes.How Does Terraform Integrate with AWS?Terraform, developed by HashiCorp, stands at the forefront of infrastructure as code tools, revolutionizing the way organizations provision and manage their infrastructure resources.With Terraform, infrastructure configurations are defined declaratively, enabling teams to treat infrastructure as code and apply software engineering practices to their infrastructure management.This powerful tool offers multi-cloud support, allowing users to provision and manage resources across various cloud providers, and provides a unified workflow for infrastructure automation and scalability.With Terraform, organizations can achieve consistent, reproducible, and efficient infrastructure management, simplifying the process of building and scaling modern cloud architectures.Getting Started with AWSCreate an AWS Account:Visit the AWS website (https://aws.amazon.com/) and click on the "Create an AWS Account" button. Follow the instructions to set up your account by providing necessary information such as your email address, payment details, and contact information. Install AWS CLI:Visit the official AWS CLI installation documentation (https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) to find instructions specific to your operating system. AWS CLI supports Windows, macOS, and Linux. Follow the installation guide to install AWS CLI on your machine. Configure AWS Credentials:To access your AWS resources, you need to configure your AWS credentials. Run the following command in the terminal or command prompt: `aws configure` You will be prompted to enter your AWS Access Key ID, Secret Access Key, default region, and output format. Obtain your AWS Access Key ID and Secret Access Key from the AWS Management Console by following the AWS documentation on accessing and managing your security credentials. How do you Create AWS Resources using Terraform?Terraform resources are created using JSON or the HashiCorp language, popularly known as HCL.Terraform's supplier is HashiCorp, and their native tongue is simple to write and read. Use the Visual Studio Code editor with Terraform plugin. It assists with input availability, autocompletion, and other things.We'll be using the HashiCorp language, which has the ".tf" extension, in this blog. To build the infrastructure, Terraform uses these files.By naming providers and resources, you define the infrastructure. We want to communicate with the providers, and on that provider, we want to build or manage resources.Provider:You list the provider block and a few arguments that are used to authenticate with the provider in the provider block.1. Create the provider.tf file:Create a new file named `provider.tf` in your Terraform project directory. Open the file in a text editor. 2. Configure the Azure Provider:Inside the `provider.tf` file, add the following code to configure the AWS provider:Resources:You describe what you wish to build in your provider in the resources blocks. Each resource block has its own set of necessary and, for the most part, recommended parameters.The fact that the parameters don't overlap between suppliers is crucial to understand.The code for AWS resources differs from the code for Azure resources as a result. Following is an example of how to create a resource in AWS using Terraform:1. Create the main.tf file:Create a new file named `main.tf` in your Terraform project directory. Open the file in a text editor. 2. Define the Azure Resource:After adding an AWS provider, add the following code to define an AWS resource in the main.tf file. For example, let's create an AWS S3 Bucket Resource:In here you can see we want to make a resource with the type “aws_s3_bucket” and with the name “example_bucket”. In the block we give the information that is needed to create such a resource.Working with TerraformTerraform has three main commands which are used to initialize, plan and apply resources.Once you have created your infrastructure according to your requirements, you will have to run these commands to create resources in Azure.These commands are called:Terraform init Terraform plan Terraform apply Terraform Init:Initializes a Terraform project in the current directory. Downloads the necessary provider plugins and sets up the backend. This command should be run once when setting up a new Terraform project or when making changes to the provider or backend configurations. Terraform Plan:Generates an execution plan based on the current Terraform configuration and state. Shows a preview of the changes that Terraform will make to the infrastructure. Helps you identify any issues or potential problems before applying changes. Terraform Apply:Applies the changes defined in the Terraform configuration to the infrastructure. Creates, modifies, or deletes resources according to the desired state. Terraform will prompt for confirmation before making any changes. TakeawayTerraform makes it feasible to use a single Infrastructure-as-Code tool to develop, manage, and apply infrastructure across several platforms.The infrastructure layer is another area where Terraform clearly focuses.This indicates that it offers skills like the ability to display updates, manage provider development, and manage your on-premises and cloud infrastructure in a single language.
Creating and Managing Amazon Web Services (AWS) with Terraform: A Hands-on Guide
When you hear the term "Customer Experience" (CX), do you instinctively relate it to UX design or customer care?Well then you need to hear this too: that’s not the case!In its truest essence, the full breadth of customer experience extends far beyond the responsibilities of UX designers and customer care teams.Crafting an extraordinary customer journey necessitates a holistic, well-integrated, organization-wide approach.And for large enterprises seeking sustainable business growth and a competitive edge, integrating customer journeys across channels, processes, and tools is not just beneficial, but essential.Cultivating a Customer-Centric Organizational Culture: Embedding CX into the Company’s DNAIn the grand scheme of enterprise operations, customer satisfaction must not be viewed as just another department.Instead, it should be regarded as a foundational aspect of the company's culture.Customer experience needs to be a strategic priority championed by everyone in the organization, from top-level executives to frontline employees.The successful execution of CX strategies demands a shared vision, a sense of collective responsibility, and an unwavering commitment across all departments.It is about creating a work culture that continually seeks to align actions and initiatives with customer needs and preferences.From marketing campaigns to sales processes, product development initiatives, technology implementations, and operational procedures – each facet of the business plays a pivotal role in shaping the overall customer experience.This necessitates an end-to-end commitment, with all departments contributing to a holistic, superior customer experience, for all the right reasons.The following encapsulate all the ways that user experience strategies, including those aimed at customer engagement and customer journey mapping, can not just be integrated but made optimal use of in large enterprises.Ownership and AlignmentCustomer experience is a strategic priority that should be owned and driven by the entire organization, from top-level executives to frontline employees.It requires a shared vision and commitment across departments to align their efforts towards delivering exceptional customer experiences.End-to-End Customer JourneyCustomer experience encompasses every touchpoint and interaction a customer has with the organization, from pre-purchase to post-purchase stages.It goes beyond the design of user interfaces and customer service interactions. It involves product development and marketing, sales, technology, operations, and more.Each department plays a vital role in shaping the overall customer experience and bringing together their insights for various areas of the customer journey mapping effort, as well as for customer-focused marketing and customer lifetime value.Consistency and ContinuityCustomers expect a consistent and seamless experience across all channels and interactions.It is crucial for organizations to integrate their systems, processes, and data to ensure a unified and frictionless customer journey.A wide range of functional expertise is required to gather customer insights, identify real pain points, and innovate solutions.Various departments can contribute their unique perspectives and expertise to uncover valuable customer insights and drive innovation in products, services, and processes.Organizational CultureShifting the organizational mindset towards customer-centricity is critical for successful customer experience transformation.It requires a cultural shift top-down, that values customer satisfaction and puts the customer at the center of decision-making.This cultural change must permeate throughout the organization, driven by leadership and reinforced through training, incentives, and performance metrics.For a more in depth understanding of integrating a more agile culture within your people, tools, and processes, be sure to read this article: Explore the Neglected Side of Agile Insights and Innovation.The Strategic Payoff: Short-Term and Long-Term Benefits of Customer Experience TransformationCustomer experience transformation yields both short-term and long-term benefits for large enterprises.Understanding these benefits can help organizations prioritize and invest in customer experience initiatives.The potential rewards of such a transformation are significant, spanning from immediate gains to long-term profitability:Short-Term Gains: Immediate Returns on InvestmentHeightened Customer Satisfaction: Enhancing various customer touchpoints can lead to a significant improvement in customer satisfaction levels. Happier customers are more likely to remain loyal, make repeat purchases, and advocate for the brand, resulting in increased customer retention and acquisition. Boost in Average Revenue per User (ARPU): Satisfied customers are more likely to upgrade their services, purchase additional products, or opt for premium features, thereby contributing to an increase in ARPU. Incremental Growth: Delivering exceptional customer experiences can stimulate positive word-of-mouth and encourage customer advocacy, attracting new customers and driving incremental revenues. Long-Term Rewards: Driving Sustainable and Scalable GrowthCustomer Loyalty and Retention: Superior customer experiences foster loyalty and ensure long-term customer engagement. This reduces customer churn and contributes to stable, recurring revenues. Increased Customer Lifetime Value (CLTV): Focusing on enhancing customer experience can significantly augment CLTV, as satisfied customers tend to have longer relationships with the organization and make repeat purchases. Deep Understanding of Customer Needs: By engaging in CX transformation, organizations can delve deeper into their customers' preferences and pain points. This rich understanding can guide the development of customer-centric products, services, and strategies, offering a strong competitive advantage. Competitive Differentiation: In today's fiercely competitive market, CX can be a potent differentiator. Organizations that excel in customer experience are more likely to stand out from the competition, attract new customers, and retain existing ones. Customer-Centric Innovation: A customer-focused approach encourages innovation that aligns with customer needs and preferences. By incorporating customer feedback and insights into the innovation process, organizations can develop new products, services, and experiences that truly resonate with customers and create value. TakeawayIn conclusion, customer experience transformation goes beyond the responsibilities of just the UX designers and customer care teams.It demands an organization-wide strategic commitment to deliver an exceptional customer journey.By adopting a comprehensive approach and involving various functional areas, organizations can realize a wide range of benefits, from improved customer satisfaction and revenue growth in the short term to customer loyalty, differentiation, and customer-centric innovation in the long term.The strategic power of customer experience, therefore, is an indispensable asset for large enterprises in today's customer-centric business landscape.
Mastering CX Excellence Part 1: The Strategic Significance of Customer Experience For Large Enterprises
When you search for the term “infrastructure-as-code” (or IaC), you’ll quickly find a list of the most popular IaC tools out there:ChefPuppetAnsiblePulumiCloudFormationTerraformHeatAnd while each tool has its own benefits, the difficult part is determining which one to use.All of these tools are capable of managing infrastructure as code, in addition to sharing other commonalities:They are all open-source, have large contributor communities, and work with various cloud providers (except for CloudFormation, which is closed-source and AWS-only). They all provide enterprise support. They are all well-documented in both official documentation and community resources like blog posts and StackOverflow questions. So, how do you make a decision?For us, we do have a top pick: Terraform.In this article, while we will talk about all of these IaC tools, we’ll also find how Terraform compares with the rest and reveal why we prefer it over others.Terraform vs OthersThere’s another challenge when choosing the best IaC tool.Most online comparisons between these tools do little more than list the general properties of each tool, making it sound like you could be equally successful with any of them.While technically correct, this is not helpful; it's a little like telling a programming newbie that you could build a website just as well with PHP, C, or Assembly — a technically correct statement, but one that leaves out a lot of information that would be extremely helpful in making a good decision.In this article, we'll go over the specific reasons why Terraform is our top pick over other IaC tools.Why Terraform: The Trade-OffsAs with all technological decisions, it comes down to trade-offs and priorities.And while your priorities may differ from ours, we hope that sharing our thought process will assist you in making your own decision.The following are the main trade-offs we considered:Configuration Management vs Provisioning Mutable Infrastructure vs Immutable Infrastructure Procedural vs Declarative General-purpose Language vs Domain-specific Language Master vs Masterless Agent vs Agentless Paid vs Free Offering Large Community vs Small Community Mature vs Cutting Edge Using Multiple Tools Together Configuration Management vs ProvisioningChef, Puppet, and Ansible are all configuration management tools, meaning they are intended to deploy and manage software on existing servers.CloudFormation, Heat, Pulumi, and Terraform are provisioning tools, which means they are meant to supply servers (along with the rest of your infrastructure, such as load balancers, databases, networking setup, and so on), leaving the chore of configuring those servers to other tools.But, if you are not utilizing server templating tools, a configuration management and provisioning solution combined is an excellent option.For example, using Terraform to provide your servers and Ansible to set up each one is a popular combo.Mutable Infrastructure vs Immutable InfrastructureMutable infrastructures allow regular updates and changes after the software is deployed, while immutable infrastructures do not allow changes after the software is deployed.Procedural vs DeclarativeChef and Ansible support a procedural approach in which you write code that explains how to attain a desired end state step by step.Terraform, CloudFormation, Pulumi, Heat, and Puppet all support a more declarative approach, in which you write code that defines your intended end state, and the IaC tool determines how to attain that state.General-purpose Language vs Domain-specific LanguageChef and Pulumi enable you to manage infrastructure like code by using a general-purpose programming language (GPL): Pulumi supports a broad range of GPLs, including JavaScript, TypeScript, Python, Go, C#, Java, and others, whereas Chef supports Ruby.To manage infrastructure as code, Terraform, Puppet, Ansible, CloudFormation, and OpenStack Heat all employ a domain-specific language (DSL): Terraform makes use of HCL; Puppet makes use of Puppet Language; and Ansible, CloudFormation, and OpenStack Heat make use of YAML (CloudFormation also supports JSON).DSLs have some benefits over GPLs:That is simpler to learn More concise and clear Greater consistency GPLs offer some benefits over DSLs as well:Sometimes there is no need to learn anything new More sophisticated toolset and a larger ecosystem Greater strength Master vs MasterlessChef and Puppet, by default, require you to maintain a master server for preserving the state of your infrastructure and distributing changes.By default, Ansible, CloudFormation, Heat, Terraform, and Pulumi are all masterless.To be more specific, some of them rely on a master server, but it's already a part of the infrastructure you're utilizing and not an extra component you have to maintain.For example, Terraform interfaces with cloud providers via their APIs; therefore, the API servers are similar to master servers in certain ways, except that they don't require any additional infrastructure or authentication processes.Agent vs AgentlessChef and Puppet require the installation of agent software (e.g., Chef Client, Puppet Agent) on each server to be configured.There are some challenges that come with this:Bootstrapping Maintenance Security Ansible, CloudFormation, Heat, Terraform, and Pulumi do not require any additional agents to be installed.To be more specific, some of them necessitate the use of agents, which are often already deployed as part of the infrastructure you're utilizing. Amazon, Azure, Google Cloud, and all other cloud providers, for example, install, manage, and authenticate agent software on each of their physical servers.You don't have to worry about any of that as a Terraform user: you simply give instructions, and the cloud provider's agents execute them on all of your servers.Ansible requires that your servers execute the SSH daemon, which is standard on most servers.Paid vs Free OfferingCloudFormation and OpenStack Heat are absolutely free: the resources you deploy with those tools may be expensive, but you don't have to pay anything to utilize them.Terraform, Chef, Puppet, Ansible, and Pulumi all have free and commercial versions: for example, you may use Terraform's free and open-source version by itself, or you can combine it with HashiCorp's premium product, Terraform Cloud.Large Community vs Small CommunityWhen you select a technology, you are also selecting a community. It's tough to make an exact comparison between localities.The table below compares popular IaC tools based on data I gathered in June 2022, including whether the IaC tool is open source or closed source, what cloud providers it supports, the total number of contributors and stars on GitHub, how many open source libraries are available for the tool, and the number of Stack Overflow questions listed for that tool.(Note: The data on contributors and stars comes from the open source repositories for each tool, but as CloudFormation is closed source, this information is unavailable).Mature vs Cutting EdgeAnother vital thing to consider when selecting a technology is maturity.The table below displays the first release dates, current version numbers (as of June 2022), and my subjective assessment of each IaC tool's maturity.Pulumi is the most recent IaC tool in some comparisons and, arguably, the least mature: this becomes clear when searching for documentation, best practices, community modules, and so on.Terraform is a bit more mature these days: the tooling has improved, best practices are better understood, there are far more learning resources available, and it is a far more stable and reliable tool now that it has reached the 1.0.0 milestone than it was when the first and second editions of Terraform: Up & Running were released.Chef and Puppet are the most mature and oldest tools.Using Multiple Tools TogetherDespite the fact that I've been comparing IaC tools throughout this article, the reality is that you'll most likely need to use a combination of tools to build your infrastructure.The sections that follow show three common combinations that I've seen work well at a variety of companies.Provisioning plus configuration management Provisioning plus server templating Provisioning plus server templating plus orchestration Provisioning plus configuration managementAs an example, consider Terraform and Ansible.Terraform deploys all underlying infrastructure, such as network topology (e.g., virtual private clouds, subnets, route tables), data stores (e.g., MySQL, Redis), load balancers, and servers.You then use Ansible to deploy your apps on top of those servers.This is a simple strategy to begin with because no additional infrastructure is required (Terraform and Ansible are both client-only programmes), and there are several ways to enable Ansible and Terraform to operate together.Provisioning plus server templatingTerraform and Packer are two examples.Packer is used to package your apps as VM images.Terraform is then used to deploy servers with these VM images, as well as the rest of your infrastructure, such as network topology (i.e., VPCs, subnets, route tables), data stores (e.g., MySQL, Redis), and load balancers.This is also a simple technique to begin with because no additional infrastructure is required (Terraform and Packer are both client-only programmes).Furthermore, because this is an immutable infrastructure solution, maintenance is simplified.Provisioning plus server templating plus orchestrationTerraform, Packer, Docker, and Kubernetes are a few examples. Packer is used to produce a VM image that includes Docker and Kubernetes agents.Terraform is then used to deploy a cluster of servers, each of which runs this VM image as well as the rest of your infrastructure, such as the network topology (i.e., VPCs, subnets, route tables), data stores (e.g., MySQL, Redis), and load balancers.Finally, when the server cluster powers up, a Kubernetes cluster is formed, which you can use to execute and manage your Dockerized apps.The benefit of this strategy is that Docker images develop rapidly, you can run and test them on your local computer, and you can use all of Kubernetes' built-in capabilities, such as multiple deployment strategies, auto-healing, auto-scaling, and so on.The disadvantage is the added complexity, both in terms of extra infrastructure to run (Kubernetes clusters are difficult and expensive to deploy and operate, though most major cloud providers now provide managed Kubernetes services, which can offload some of this work) and several extra layers of abstraction to learn, manage, and debug (Kubernetes, Docker, Packer).Final TakeawayConsidering everything, the table below compares the most popular IaC tools, depicting the most common or default configuration of the various tools.However, it’s important to note that, as discussed earlier, these IaC tools are flexible enough to be used in other configurations as well (e.g., you can use Chef without a master, you can use Puppet to do immutable infrastructure, etc.).Want more expert insights into what an ideal DevOps culture looks like and how it can help? Check out this article: 6 Reasons Why You Should Adopt a DevOps Culture in Your Business
Managing Infrastructure As Code: Terraform Explained
As a team, we recognize the importance of designing good experiences for everyone.And as the world becomes more and more impacted by advancements in technology and the evolution of digital experiences, our design team decided to actively play a responsibility in ensuring everyone is accounted for.Making it a priority during the first quarter of this year, our designers have been taking a course on Digital Accessibility from the W3C to learn the ins and outs of digital accessibility and how we can adopt it to design inclusive experiences.This fourth round of the Ask A Designer series is dedicated to shedding light on these learnings, as our designers answer questions that help us understand how Accessibility can be adopted.Question 1What does digital accessibility mean to you?Name: Grace XuTitle: Sr Graphic DesignerResponse:Digital accessibility is not just a nice to have, but a must have. It is mandatory for some but beneficial to everyone.Equal access to digital information has to be something that every designer strives for, and it goes beyond meeting federal or even international laws and policies on this matter.This is a basic human right and these accessibility practices empower my ability to empathize and understand others who use and experience our digital products from a different angle, one that we often don’t consider but absolutely should.Name: Usman KhanTitle: Product DesignerResponse:Digital accessibility is the important practice of designing and developing digital content and technologies that are accessible to all people, including those with disabilities.This includes making sure that digital content, including websites, mobile applications, electronic documents, and multimedia, is accessible to and intelligible by all users, regardless of their abilities or limitations.The use of technology that can be accessed by assistive devices like screen readers and the provision of clear, easy-to-read text are a few examples of how this is done.Another is the provision of text replacements for non-text information (such as photos or videos).All things considered, It is very important because it ensures that all individuals have equal access to digital content and technology, which can help to promote inclusion, independence, and participation in society.Name: Pranavi LMKTitle: Associate Product DesignerResponse:Digital accessibility ensures that everyone has equal access to information: every website and application must be built with this in mind.Digital accessibility is essential because it empowers one and makes them independent in this digital world.Designing a website that is accessible to everyone makes them feel included and is a professional moral to follow.Making a website/app accessible means adding interactive elements like buttons/controls that lets users access the information through clicking, scrolling, voice commands etc.Accessibility exists on a large scale.When it comes to using digital products, users have a wide range of requirements and preferences, so there is no one-size-fits-all accessibility definition.But as designers, It is our responsibility to try and assist as many people as possible.Name: Usama JavedTitle: Product DesignerResponse:Accessibility is all about people. It is about ensuring all users can access the same information, regardless of the impairments they may have.Whether a visually-impaired person uses a screen reader to access a webpage or someone has a cognitive disability that requires straightforward content and navigation, there are many reasons to make your digital presence accessible.Digital accessibility is when technology has been designed in a way so that it can be accessed by all types of people.This includes electronic documents, websites, software, hardware, video, audio, and other digital assets.Question 2What kinds of tools do you use to check for accessibility?Name: Grace XuResponse:The WAVE Evaluation Tool is pretty robust for most web cases, the contrast checker is the one I use the most! However I also want to shift the focus away from tools and more on how we need to keep in mind that tools won’t give you all the answers on how to make something accessible. For example, a tool can inform us that a piece of alternative text is missing from an image, but it can’t tell us if in the case there is alt text, that what it has displayed is actually appropriate or helpful for users who need it. We need to use manual testing as well to fully see if our work is actually compliant and always consider it in every step of the design process.Name: Usman KhanResponse:The WAVE tool is pretty good and it can help to identify a wide range of accessibility issues, including missing alt text for images, low contrast text, missing form labels, and more.It can improve the overall checking of accessibility in several ways:Easy to understand results: The WAVE tool provides a detailed report of all accessibility errors, warnings, and features. This report is color-coded to make it easy to understand which issues need to be addressed first and provides clear guidance on how to fix each issue. Real-time testing: The WAVE tool allows you to test web pages in real time, so you can see accessibility issues as you make changes to your website. This makes it easy to test and fix accessibility issues as you work on your website.Overall, the WAVE tool is a powerful tool that can help improve the accessibility of the website. By using it to identify and address accessibility issues, I can ensure that the website is accessible to all users, regardless of their disabilities. Name: Pranavi LMKResponse:I use https://webaim.org/resources/contrastchecker/ and https://coolors.co/contrast-checker/112a46-acc8e5 to check color contrast.Webaim has other articles, checklists and resources to check your design accessibility. It has resources that lets you do manual testing even for screen readers.Name: Usama JavedResponse:One tool I use is Getstark, which is a tool that helps ensure that my designs are accessible to users with visual impairments. This tool checks for issues like color contrast, font size, and other factors that can impact how easily users can read and understand your content.Another tool is Contrast Checker, which helps with color contrast.This tool enables you to check whether your text and background colors meet accessibility standards, which is particularly important for users with visual impairments.Last but not the least, There is the Lighthouse tool. It is built into Google Chrome that can help you test the accessibility of your site.It provides a comprehensive report that includes accessibility, performance, and SEO metrics.Question 3How do you integrate accessibility into your work?Name: Grace XuResponse:One thing I’ve committed to memory are the most common forms of assistive technologies and how people use it to interact with digital products.I try to imagine what they would do and if there is a production link to our product then open up one of these tools and try it myself.I also think that you never stop learning as some things come with experience; overlook a part of your design’s accessibility and once that’s pointed out as being inaccessible, you won’t forget the second time.Thankfully there are always multiple checks along the way, and no digital product would get released without evaluation from devs, POs, content, and even an organization accessibility champion in our case.Name: Pranavi LMKResponse:I always make sure I do not forget the basic principles, like the size of the text, button, elements and color contrast. These are the general guidelines to start with making a design universal. I try to put myself in the shoes of the user and see if the design is accessible or not. It is good to review the accessibility beforehand with the team and developers which will save time and give more insights. I cross check my design with the web accessibility checker.Name: Usman KhanResponse:I always make sure that along with normal people, there will be people with disabilities who can use my product.Here is a quick example on how I keep track of accessibility while I am working on a website by:Using proper heading tags (h1, h2, h3, etc.) to create a clear and logical content hierarchy. Ensuring that all images have alt text that describes their content and function. Ensuring that all forms are labeled correctly and are accessible to screen readers. Using high-contrast colors and fonts that are easy to read for people with visual impairments. Name: Usama JavedResponse:Good question! I always ensure that I follow basic guidelines and principles of accessibility to ensure that designs meet the accessibility requirements. There are some other important key things which i follow while creating designs for products:I use proper color contrasts because it is very important to ensure that there is sufficient contrast between text and background colors.I provide alternative text for images.It allows me to understand the context of images and other visual elements.I understand the needs of the users with disabilities throughout the design process, from ideation to implementation.Question 4What was eye opening for you in your training?Name: Grace XuResponse:“Good design makes a website, application, or any other product or service, one that we all want to use – it just works better.” Having teams of people who care about digital accessibility will create products that are better for everyone who experiences it. The course showed many types of differently abled people and how they use assistive technology or omit certain technologies we commonly use to achieve the same goals that you and I would. It made me poke around my frequented digital websites, apps, tools more and see where the gaps are and notice how often many of these accessibility issues are simple to solve.Name: Pranavi LMKResponse:The training undoubtedly showcased various disabilities and the assistive technologies that are available to enhance their access to the digital world. Screen readers, motion tracking software, screen magnifiers, and braille display are new technologies that I got to know of. I learnt that accessible products help in boosting businesses by increasing SEO, Customers. There are laws that protect and demand digital products to be accessible for the differently abled.Name: Usama JavedResponse: The training emphasized the importance of good design in creating products that are user-friendly for everyone. By having a team of people who prioritize digital accessibility, businesses can create products that work better for all the users. During the training, I was introduced to many different abled individuals and how they use assistive technology to achieve their goals. This made me more aware of the accessibility gaps in the digital products I use regularly and how simple solutions can make a big difference.Overall, the training emphasized the importance of creating accessible products that are usable for everyone. By understanding the needs of differently abled individuals and incorporating digital accessibility into the design process, businesses can create products that are better for everyone.Name: Usman KhanResponse: This training helps me understand the importance of website design that is accessible to everyone, regardless of their abilities. By designing accessible websites, I can now create a better user experience for everyone, including people with disabilities, and help organizations reach a broader audience. Additionally, the course teaches about accessibility standards and guidelines, helping organizations avoid legal and financial consequences associated with non-compliance. I can now confidently become an advocate for web accessibility and promote its importance across my circle.
Ask A Designer Round 4: Considering Accessibility and Designing Inclusively
Design Thinking is not new.It started thousands of years ago when our ancestors were looking for answers from the open world by observing it, interpreting it, bit by bit adapting to it, and quickly pivoting when a critical threat is observed by their sensory knowledge.Because of the Design Thinking approach, we survived extreme weather conditions and the deadliest of predators, and equipped ourselves with the right tools and ways of survival.We transitioned from hunter-gathering, to growing crops and into creating industries and digitizing them.From a cosmological viewpoint, we are merely designers, experimenters, and doers who proceed with caution.Design Thinking helps surface the uncertainties that have some level of doubt attached to them, and then we address those uncertainties through an iterative way of proceeding with caution.We shouldn’t forget, however, that design thinking has also been the buzzword for a decade now where small to enterprise-level organizations are continuously dedicated to establishing design thinking as a panacea.But when organizations fail to realize the situational application of Design Thinking, they generate waste in the name of experimentation.Shall Design Thinking Be Used in Every Situation?Instead of using Design Thinking in every situation, there is a way to identify which situations require Design Thinking.This can be achieved through the ‘Uncertainty Index’ for a given requirement.Instead of using Design as a buzzword for showing some level of upskilling within the organization, these can be used selectively and more efficiently to solve the right problem for the right people.Otherwise, it can become devastating if not used for the right problem.The conceptual understanding of Design Thinking has been improving, where Product Managers and Designers have started to factor in different uncertainties to make it an effective method of solving customer problems.What Are These Uncertainties?At mobileLive, we believe in adopting the most effective approach to address uncertainties while minimizing waste, by identifying and researching our customers' and clients' true needs.To achieve this, we suggest selectively using a set of uncertainties that can be applied to any given problem or need.Progressive companies typically consider multiple uncertainties to ensure that design thinking is only used where it can uncover real needs.If the company has relevant historical data, research, or expert opinions on the problem at hand, they assign an uncertainty threshold to determine whether the problem falls below the threshold and can be solved using a tried and tested approach to product development.This approach saves time and cost while still achieving the same level of product development as a design thinking process.With the ‘Uncertainty Index’, uncertainties of all kinds associated with a problem, a product, or service can be mapped in a singular view.This helps us to be able to gauge better if the problem at hand warrants a design thinking approach to solving the problem, or if conventional methods need to be used for better outcomes.The Seven Uncertainty IndexThe following are the seven types of uncertainties for which a quantitative or qualitative threshold can be defined and practiced across the Products cross-functionally:1. Customer PreferenceInsufficient or inaccurate data on customer preferences, such as incomplete or outdated market research or survey results Limited understanding of customer needs and pain points can result in misaligned products and services that do not meet customer expectations Rapidly changing customer preferences due to shifts in consumer behavior or the emergence of new technologies or trends Inconsistent or unreliable customer feedback and satisfaction data that can lead to inaccurate conclusions about customer preferences Lack of clarity or articulation of customer expectations, which can make it difficult to develop effective marketing strategies and product roadmaps If there is minimal uncertainty related to customer preferences, it may indicate that a design thinking approach is not necessary.Instead, product strategists can focus their efforts on leveraging existing data and research to inform their product development decisions.This can help to optimize resources and ensure that solutions are aligned with the needs and preferences of the target customer base.2. Market VolatilityRapid changes in consumer demand and supply due to changing market conditions Shifting consumer preferences and behaviors, including emerging trends and fads Technological advancements and disruptive innovations that can quickly change the competitive landscape Intense competition and pricing pressures, including the emergence of new competitors or business models Economic and financial instability, including changes in interest rates, exchange rates, and other macroeconomic factors that can impact market dynamics If there is a low level of uncertainty related to market volatility, it may be an indication that the market is relatively stable and well-understood.In such cases, design thinking may not be the most appropriate approach to use, as the focus should be on leveraging existing knowledge and data to drive product strategy and decision-making.However, even in relatively stable markets, there may still be opportunities to innovate and differentiate, and a design thinking approach can still be valuable in identifying and capitalizing on these opportunities.It is important to assess the level of uncertainty related to market volatility on a case-by-case basis and to use a range of tools and approaches to identify and minimize these uncertainties.3. Regulatory AmbiguityAmbiguity in regulatory requirements and compliance standards Lack of clarity in legal frameworks and guidelines Uncertainty about how regulatory bodies will interpret and enforce regulations Compliance costs and risks associated with regulatory non-compliance Potential reputational risks associated with regulatory violations In cases where the regulatory landscape is relatively stable and clear, design thinking may not be necessary as existing regulations can guide decision-making.Similarly, in situations where the regulatory requirements are well-defined and leave little room for interpretation, other methodologies such as legal or compliance-focused approaches may be more appropriate.However, if there is significant uncertainty surrounding regulatory requirements, design thinking can be useful in exploring potential solutions and identifying areas where further regulatory guidance may be needed.It's important to assess the level of regulatory ambiguity and determine whether design thinking is the most suitable approach for addressing the specific challenges and opportunities at hand.4. Technological UncertaintyRapid pace of technological change, which can make it difficult to keep up with new developments and trends Uncertainty around the commercial viability of emerging technologies, including questions around market demand, scalability, and return on investment Complexity of emerging technologies, which may require specialized skills and expertise that may not be readily available in-house Risk of technological disruption that can create new threats to established business models and markets Potential for emerging technologies to create new opportunities and business models, including the potential to improve operational efficiency, enhance customer experiences, and create new revenue streams When faced with uncertainties related to technological disruptions, it's important to assess the level of uncertainty and the potential impact on the product.If the uncertainties are conventional and there are proven solutions or approaches that have been successful in the industry or by the organization, then design thinking may not be necessary as product strategists can rely on conventional methods.However, if the uncertainties are more complex and disruptive, with no clear examples of successful solutions or approaches, then design thinking can be a valuable tool to help product strategists explore alternative solutions and develop more innovative products that can better meet the needs and preferences of their target customers.5. Channel UncertaintyIncomplete understanding of customer behavior and preferences across various channels, which can lead to suboptimal channel selection and poor customer experience Uncertainty around the most effective methods to reach and engage customers through different channels, which can lead to inefficient use of resources and missed opportunities Limited knowledge about the costs, benefits, and risks associated with different channels, which can make it difficult to optimize channel selection and ROI Uncertainty about how different channels will evolve and their future viability, which can make it difficult to develop a long-term channel strategy Lack of clarity about the potential impact of emerging channels on existing channel strategy, which can make it difficult to effectively allocate resources and adapt to changes in the market If there is minimal uncertainty with the channels, using design thinking as the default approach may result in unnecessary time and resource waste trying to reinvent the wheel.However, with the rapid introduction and expansion of digital channels, it is crucial to remain present and relevant while exercising caution. In such cases, applying design thinking can help to identify opportunities for innovation and differentiation, ensuring that your organization stays ahead of the curve and delivers value to customers in new and impactful ways.6. Financial ViabilityLimited understanding of the lifetime value of customers and the impact of customer acquisition costs on profitability Uncertainty about the scalability of the product and its ability to generate sufficient revenue to cover development costs and sustain the business over the long term Limited knowledge of the market size and growth potential for the product, and the potential impact of competitors or substitute products Uncertainty about the financial impact of potential legal or regulatory challenges, such as patent infringement lawsuits or regulatory hurdles Limited understanding of the financial implications of potential product failures or recalls, including the costs of customer refunds or compensation, legal fees, and damage to the brand's reputation If there is minimal uncertainty related to financial viability, it may not be necessary to use a design thinking approach and instead bank on the existing financial models and data.7. Organizational AbilityLack of organizational agility and ability to adapt to changing market conditions and customer needs Limited capability to attract and retain top talent with the necessary skills and expertise Insufficient investment in training and development programs to build necessary competencies and capabilities Inability to effectively manage and integrate acquisitions or partnerships Limited ability to implement and execute strategic initiatives and goals due to siloed or bureaucratic organizational structures If the uncertainty related to organizational ability is low, then it may not be necessary to use a design thinking approach as product strategists may already possess the necessary skills and expertise to develop and execute the product strategy effectively.However, if there is a higher level of uncertainty in this area, it may be critical to adopt a design thinking approach to identify the right organizational skillset needed and to develop strategies to retain top talent possessing those skills to ensure successful product development and execution. The main aim remains to deliver with certainty.Reducing Uncertainties Through the Uncertainty IndexThe uncertainty score can be determined by going through each of the five uncertainties listed under the seven main uncertainty categories.Any organization can set a rule of thumb to kickstart the design thinking process when an uncertainty score of a category is found to be above the uncertainty threshold.Once uncertainty is mapped, the goal is to reduce it through research, rapid prototyping, and pivoting.Analyzing uncertainties with the scope of the ‘uncertainty index’ requires domain expertise and cross-functional collaboration, but rest assured, it will save a lot of wasted efforts in terms of time and money later in the product development and launch cycle.This example below definitely calls for sophisticated data elicitation and research using design thinking principles.Final TakeawayIn conclusion, the uncertain business environment makes it challenging for organizations to make strategic decisions.Design thinking can be an effective approach to navigating uncertainty and creating customer-centric solutions.However, it is important to assess the level of uncertainty associated with the problem at hand before deciding to use design thinking.The uncertainty index can be a useful tool in this regard, helping organizations to evaluate various uncertainties such as customer preferences, market volatility, including uneven competitive landscape, emerging technologies, channel uncertainty, regulatory ambiguity, financial viability, and organizational ability.By carefully considering these uncertainties and using design thinking where appropriate, organizations can make more informed decisions and better position themselves for success in the marketplace.FAQsIs design thinking a panacea for all kinds of problems?No, design thinking is not a one-size-fits-all solution for every problem. It is a framework that is best suited for complex, open-ended problems that require creative solutions.Can design thinking be misused as a buzzword to create the illusion of innovation?Yes, it is possible to misuse design thinking as a buzzword without actually achieving any tangible results. It is important to approach design thinking with a genuine intention to solve problems and create value.Are there situations where design thinking may not be appropriate?Yes, there may be situations where design thinking may not be appropriate. Those problems with low levels of uncertainty usually don’t require the design thinking approach such as when the problem is well-defined and has a clear solution, or when time and resources are strictly limited.Can design thinking be used as a substitute for domain expertise?No, design thinking is not a substitute for domain expertise. It is a complementary approach that can be used in conjunction with the domain expertise to generate innovative solutions.Is it important to involve stakeholders in the design thinking process?Yes, involving stakeholders in the design thinking process is critical to ensuring that the solutions generated are relevant and meet the needs of the end users and all entities in between. The associated uncertainties spread across different disciplines within an organization to be well understood, followed up and reduced.Is it important to evaluate the problem for uncertainties before using design thinking?Yes, it's very important to evaluate the problem for uncertainties as it helps in identifying potential challenges and opportunities that may affect the outcome of the process. This evaluation can also help in adjusting the process to accommodate any unforeseen obstacles.Is it important to evaluate the success of design thinking initiatives?Yes, it is important to evaluate the success of design thinking initiatives to determine their effectiveness and identify areas for improvement.
Reducing Uncertainties With Design Thinking: The 7 Uncertainty Index
Toronto, ON, May 9th, mobileLIVE, a Toronto-based technology services company, achieved Platinum Club status with Canada’s Best Managed Companies program by retaining its Best Managed designation for seven consecutive years or more.Celebrating its 30th anniversary, Canada’s Best Managed Companies program awards excellence in private Canadian-owned companies. To attain the designation, companies are evaluated on their leadership in the areas of strategy, culture and commitment, capabilities, innovation, governance, and financial performance.“It has become increasingly important for businesses to foster collaborative workplaces where employees are empowered to make valuable contributions to their organizations,” said Derrick Dempster, Partner, Deloitte Private and Co-Leader, Canada’s Best Managed Companies program. “This year’s Best Managed winners, including mobileLIVE, embrace a people-first mentality, enabling employees to cultivate important capabilities and integrating diversity, equity, and inclusion initiatives into their core strategy. By prioritizing employee wellbeing and championing professional development, these companies are harnessing their talent pool’s fullest potential and in a stronger position to attract the talents needed to embrace the opportunities and challenges that lie ahead.”Canada’s Best Managed Companies is one of the country’s leading business awards programs recognizing innovative and world-class businesses. Every year, hundreds of entrepreneurial companies compete for this designation in a rigorous and independent evaluation process. Applicants are evaluated by an independent panel of judges with representation from program sponsors and special guests.“We are thrilled to be named a Best Managed Platinum Club member,” said Jahan Ali, CEO and founder at mobileLIVE. “This win is a testament to the hard work and dedication of our team, the success of our business philosophy, and our commitment to excellence in every aspect of our business.”The Best Managed assessment process is rigorous and comprehensive, evaluating a company’s overall performance and practices in areas such as strategy, leadership, innovation, culture, governance, and financials. Companies that achieve Best Managed status are recognized as industry leaders, demonstrating their commitment to long-term growth and sustainability. The 2023 cohort of Best Managed companies share common themes such as having a people-centric culture, targeting effective ESG strategies, and accelerating operational digitization.“CIBC is proud to sponsor Canada’s Best Managed Companies, a program that has been celebrating leading organizations that are working towards building a better future, for the past thirty years,” said Blair Cowan, Executive Vice-President, Head of Commercial Banking and Real Estate CIBC. “Companies such as mobileLIVE, which successfully deliver innovative solutions for longer-term growth and investments, are a testament to our country’s high standards of excellence.”
mobileLIVE is now the Best Managed Platinum Club Member
First brought into practice by a team at Google Ventures, the idea of “sprints” came about as a quick solution to solve the problems of startups that they invested in.They particularly needed “quick” solutions and wanted to be able to solve these problems in a short time because of challenges like:High stakes: The solutions require a lot of money and time, which most startups lack. Limited time: There’s often a deadline and the solution needs to be presented fast. As the word “sprint” suggests, teams need to work quickly and efficiently towards attaining a workable solution. Feeling stuck: If there’s a deadlock in the creative process, a design sprint acts as a catalyst that can pull teams through such situations and give them a creative boost. These were just the challenges that the team who invented “sprints” faced, and today, sprints are adopted across the globe amongst teams in various industries.In the design industry, in particular, design sprints are adopted to solve complexities quickly, and in this article, we’ll break down design sprints into a 5-stage holistic process.What is a Design Sprint?The “sprint” is an effective method of solving complex problems, prototyping ideas and testing them with users.This method can also be used to test new ideas in a product and see what the customer acceptance would look like.Product teams can improve their product development process and create successful solutions in a short amount of time with design sprints.The Process: 5 Phases of a Design SprintTo look at it from a holistic angle, the design sprint process is divided into five different phases:Phase 01: Understand the problem Phase 02: Sketch all possible solutions Phase 03: Decide on the most optimal solution Phase 04: Create a usable prototype Phase 05: Test the prototype with users and gather insights Phase 01The first phase is structured around discussions to create a path for the sprint week and to create a map of the challenge being faced.A long term goal needs to be set for the product.This will act as an anchor point for a mindset towards solving the problems that are preventing the team from achieving that goal.All the key stakeholders (designers, developers, business, project managers, etc.) need to be a part of the team because you need people with different skill sets and experiences to examine the problem — everyone brings something unique to the table.The map represents the user going through your product to achieve the intended goal.All the touchpoints that get the user closer to the goal are noted.Along with visualizing all the steps that the user takes, the map also helps identify if there’s friction between the touchpoints and provides a structure for our sketches & prototype.Now imagine that the goal is achieved.Can we identify any issue that the user might face while going through this journey?These will be put in the form of “How Might We questions (HMW)” instead of simply jotting down the statement e.g. How might we educate the user about this new feature?Put the questions within the respective journey phase (discovery, learning, using).The last part of this phase involves prioritizing the HMW questions and targeting a user.The example shown above has only one user, but in case there are multiple user personas involved, the key stakeholders in the team have to decide which user to focus on for the current sprint.This can be done using dot voting. Phase 02After defining the challenge in detail, the next step is to come up with solutions.The first thing to look out for is if there are other similar products out there and look for inspiration.Afterwards, the team members can ideate upon the solutions by creating sketches and flows.When everyone is done, all the team members can present their solution.Phase 03After everyone explains their solution, its time to choose the most optimal one.This can be done by discussions, which can become very exhaustive because team members will have to critique each other's solution, but it's important to get this right.An optimal solution would be something that most efficiently solves the problem and is also viable for the design and dev team to create.Image credit: Sprint - By Jake KnappA storyboard needs to be created around the chosen solution. This will help us set the mood for prototyping.The storyboard will essentially cover the user journey from discovery of the product to the exploration and usage of the new feature, which is the intended goal.Think of this as a more detailed map (from phase 1), which covers all the touchpoints, hence creating a clear roadmap for the prototype.Phase 04A prototype will be created based on the storyboard that should be realistic enough for testing, such that during the testing phase, the user should be able to properly interact with the prototype without any hiccups.The prototype shouldn’t be very high fidelity because the solution may not work. Extra time, therefore, shouldn’t be spent over something that has a high chance of disposal/changes.Phase 05The final phase of the design sprint is intended for testing the prototype.The format of the interview boils down to this:A friendly welcome and some open ended questions to learn more about the user Introduction to the prototype Detailed task that would require the user to start from the beginning and interact with the prototype till the end A debrief to capture the user’s impressions regarding the prototype Use this feedback to make any amends in the prototype, if required.Final ThoughtsIn the design sprint methodology, research is given very little time.Great products are a result of extensive research, and therefore I’ve tweaked the design sprint approach such that the research in phase 1 and 2 is given much more time.The design sprint is no doubt a high energy task where everyone is excited and ready to roll.But once it's completed and action points are drawn from it, oftentimes there’s a gap that remains till execution.The decision maker (a part of the sprint team) may lose power to allocate teams and budgets for the new project.There may also be structural changes in an organization, or outsourced execution, which means that the team that conducted the sprint are not the ones who develop the whole thing.The design sprint, therefore, needs to be optimized for large companies, in a way that the execution gap is accounted for within the sprint plan.To learn more about how you can integrate design sprints into your design workshops, be sure to give this article a read: Design Thinking Workshops: Choosing the Right One For Your Team
Solving Complexity in Sprints: A 5-Step Breakdown of Design Sprints
Owing to its recent rise in popularity, you've likely heard of design tokens.As a concept, understanding design tokens is quite simple: A design token is a code-based way to maintain how design systems look.For a designer, that means:Faster design changes Improved communication Reduced documentation headache More cross-platform consistency Easier handoffs to developers And while, for the right teams, design tokens can do wonders, it would be a mistake to get into it and try to implement design tokens without the right knowledge or practices.What Are Design Tokens?In essence, design tokens are variables.Think X = Y, where ‘X’ is a design attribute and ‘Y’ is the value.It’s important to note that there is much more to these values than reducing them down to “variables”, but for the sake of understanding design tokens better, this analogy will work for our purposes.For example: ‘borderWidth = 8px’ means that a border’s width will be, you guessed it, 8 pixels. (Pixels are what we see on our devices. These words you are reading right now are being displayed as pixels by your device.)These variables or “design tokens” can help to speed up the design and development process by automating updates, removing guess work, improving documentation, and documenting design decisions.For the right teams it can boost productivity and efficiency by removing some of the tedium that comes from maintaining design systems and documentation.Why Use Design TokensI’m sure you can already see how design tokens can improve your design system:Rapid design changes: Designers and developers can quickly change the look of the design by manipulating the token’s values. Consistency across platforms: Design tokens help ensure consistent design elements across platforms, reducing the need for back-and-forth between designers and developers. Reduced documentation headaches: Documentation can be updated automatically with design token changes, reducing the likelihood of errors and inconsistencies. Future-proofing your design system: Design tokens provide a flexible, modular foundation for your design system that can be easily updated and adapted over time. Additionally, design tokens are gaining in popularity.As I am writing this, there is a W3C community group working to establish a set of standards for the industry to use.The W3C or The World Wide Web Consortium is responsible for creating and maintaining the standards for the world wide web. It’s thanks to them that teams no longer need to spend hours ensuring web apps appear the same across web platforms.This is something that you are going to want to consider with your design systems.Developer Hand-offAh, the joys of handing off design tokens to development!While design tokens offer immense benefits to the design process, communicating and implementing them across platforms can be a daunting task.The first challenge is understanding and communication.Designers and developers need to be on the same page when it comes to naming conventions and values to ensure consistency in design. Here’s an interesting video to understand how design systems help improve and speed up the development process.Secondly, implementing design tokens across platforms can be tricky. Different platforms may require different formats, and converting design tokens can result in discrepancies in values.And if that wasn't enough, managing design token changes can be a headache.With multiple stakeholders involved in the process, keeping track of changes and ensuring everyone is using the most up-to-date design tokens can feel like a game of whack-a-mole.But fear not, there are ways to overcome these challenges and streamline the handoff process.For a deeper dive into how designers and developers collaborate, be sure to catch this insightful conversation between our developers and design teams: Ask A Designer Round 2: Questions For Designers, From Developers.How To Navigate the Challenges of Design TokensHold a workshopResearch! Learn about how design tokens were created and how organizations are adapting them to their needs. Look into the W3C Design tokens community group to understand the standards that are currently being developed. Bring together your stakeholders, developers, designers, and other key personnel Ensure that everyone understands the goals and intent behind adding design tokens to a design system. Make sure to document your decisions! Discuss a naming convention. This step is trickier than you may think. Designers and Developers need to create a common language for discussing the project. If thats not a common practice already, it will take some time to get everyone on the same page. Document. Your. Decisions. Agree on an implementation plan, start small and with a few components to work out any process gaps. It is easier to adjust 3 variables than it is to adjust an entire design system. Consider the future of the project as well, the work you do will have a direct impact on the results in the future. Again, make sure you are documenting this work. DocumentationDocumenting the decisions that build the foundation of your design system is ALWAYS the prudent thing to do.After all, who hates leaving a meeting and realizing you cannot remember key decisions?This opens the team up for misinformation to creep in and disrupt the flow. Were we using a 8pt system or 10pt system? Designer A says 8pt, Developer 1 says 10pt. It's anarchy!Make it easier with documentation tokensWhen it comes to documentation, Design Tokens for Figma can expedite the style documentation thanks to its handy documentation tokens.But what it cannot document is the reasoning and structures that go into creating the style guide.Adding design tokens to your system gives you and your team an opportunity to look at what is currently serving your design practice and determine if it is still serving you.Best Practices To Introduce Design Tokens To Your Design SystemIn order to offer the best chance at organizational adoption, here are a few best practices that will help navigate the nuances of design tokens:Create and enforce clear naming conventionsEstablishing a common language between all involved in a product isn’t always an easy feat. Even in the design community, you hear different terms for the same design patterns: Modal aka pop-up, overlay, or ********dialogue box. Consider the experience a new team member will have onboarding into this system. Use naming conventions that are intuitive and that work for your team. Avoid abbreviations and again, document the decisions that went into it. Be kind when reminding someone of correct naming conventions. Habits are hard to break, so as you start implementing design tokens don’t expect 100% adherence from the start. Maintain a centralized Design Token LibraryConsider using a cloud-based 3rd-party platform such as Storybook as a repository for your design components. This will allow team members to access the design system components from anywhere increasing visual design consistency. Create clear guidelines for how design tokens should be organized within the library to ensure naming and organization consistency. This can also help stakeholders and developers navigate through Figma using wayfinding. Regularly update and review design tokensSet a regular review schedule to ensure that design tokens are regularly audited and updated as needed. Solicit feedback from team members and stakeholders to identify areas where design tokens may need to be updated or added. Document any changes made to design tokens to ensure that all team members are aware of updates and can access the most current version. If you find that you aren’t making much headway with design token adoption, I find it helpful to take a step back and ask “why”.Sometimes you will need to tweak your approach or perhaps your organization lacks the design maturity to support design tokens.Once you have a clearer understanding of the underlying issues, you can begin to address them.When NOT to use Design TokensWhile design tokens can be a valuable tool for maintaining consistency and efficiency in the design process, they're not always necessary.If you're working on a small project, a project with constantly changing requirements, or a project with highly unique visual elements, it may not be worth the effort to create a full library of design tokens.In those cases, it may be more efficient to define visual elements on a case-by-case basis.The key is to evaluate each project on a case-by-case basis and determine if design tokens are the right choice.Real-World Examples of Design Token ImplementationDesign tokens are the secret sauce that many companies are using to create consistent and cohesive design experiences across their products and platforms.From IBM's design language to Salesforce's Lightning Design System, these tokens are being implemented in a wide range of products and services to streamline the design process and ensure visual consistency.Even companies like Shopify and Airbnb have jumped on the design token bandwagon, using these powerful tools to create a consistent look and feel across their websites and mobile apps.So if you want to stay ahead of the design curve and ensure a consistent user experience, it's time to start incorporating design tokens into your design system.TakeawayLet's wrap up this informative piece on design tokens.Design tokens are like the MVPs of the design world, enabling rapid design changes, consistency across platforms, and future-proofing your design system.However, implementing design tokens can have its challenges, such as communicating and implementing them across platforms and managing design token changes.To overcome these challenges, hold workshops, document decisions, establish clear naming conventions, and maintain a centralized design token library.So, start implementing design tokens like a pro today!
Design Tokens: Creating a Single Source of Truth for Your Design System
Developers know that the more they test their code, the better their chances are of developing high-quality software.And with Test-Driven Development, or TDD, they’re not only able to validate their code but also automate their testing process in order to keep the code simple, clean, and bug-free.But even though TDD has been around for several years, there is one big misconception about what TDD is actually about.Is TDD just about testing code?We’ll uncover the answer to this question, the true essence of TDD, and how you can use it to improve the quality of what you develop in this article.What is TDD and What it is NotContrary to popular belief, it’s not a method, but more of a mindset toward designing or developing your code.Before getting into how TDD works and what process is used to implement it, it’s crucial to break some of the misconceptions around what TDD is:TDD is NOT just another method of testing code or a QA replacement Writing unit testing is a method of practicing TDD For each function, you write code to validate your expected flows TDD will increase your development cycle Most people think TDD is about testing code, whereas in reality, TDD goes far beyond simply testing code.Usually, when we think about testing, the process goes that we write the code first and then test it. TDD offers a whole different approach to software development.In TDD, the cycle is reversed: you don’t write the code first, but instead, you write your tests.TDD is about driving development by test. “Life is a highway, I wanna ride it all night long.”What does Rascal Flatts have to do with TDD?You write code, and when it fails, you keep writing until it eventually passes; your code is your highway.So, as the name suggests, TDD is about driving development by test, where you write your test first and then write your code instead of the other way around.If anything, this further strengthens the concept that TDD is not about testing the code, but rather it’s about an approach to software development where the main focus is ensuring that our code is doing what we expect it to do.TDD is a way to verify everything you create. All dev activities are organized around the need to verify what it is that we create. In TDD, therefore, the primary focus is to be able to verify that whatever we create is of good quality and that it can provide the intended results. The essence of TDD is to work incrementally on your design The Value of TDDCode is weird, unusual, complex, and fragile. One tiny error can invalidate the result.Even the strongest developers make mistakes – after all, they’re only human, and minor mistakes can happen by anyone.It’s also interesting to note that the very nature of TDD is to iterate and improve. Step by step, TDD makes it easy to introduce new features incrementally, and in each iteration, we can catch and resolve the defects found during testing. Researchers have found that most problems found in production are caused by simple programming mistakes.“Approx. 60% of all the issues found in production are due to trivial mistakes.”A simple mistake can cause entire systems to fail.A study of 198 production failures found that 58% of the failures were due to trivial mistakes that could've been caught before deploying to production. This means that if we checked that our code actually did what we thought it did, we'd improve our reliability by over 50% - substantial cost savings and, most importantly, improved user satisfactionAnd while we do have tools that catch some mistakes, and our IDE will give us great feedback that is fast and efficient and maybe highlight some errors, the IDE won’t catch all kinds of issues.In software development, TDD is a huge step forward because of this very reason: it ensures that we catch the issues that the IDE could not catch earlier.How TDD worksThere’s a lot that TDD has to offer, and understanding its most significant benefits requires a look into the way it works. The following is a summed-up version of the workings of TDD and what happens at each stage:Writing and checking that the test failsWe can check the mini specification by executing before writing the behavior and checking that it fails. Before even writing the behavior, the focus should be to write the test and to make sure that the test fails. Of course, the first time, the test will fail because there’s no code.If it fails, that’s a good sign – it means we wrote something useful, so now our next focus can be fixing the test. Improving quality with each iterationWhen we fix the test, what we really want to do is implement our behavior. But then, in the very first iteration, we won't be trying any fancy things or getting creative with the code. At this stage, we just do the bare minimum to pass the test, and once it has passed and we’re good with it, then in the second iteration is where we can bring in our creativity. This is where we can add more quality, improve our code with every iteration, and, most importantly, ensure it fulfills our intent. Writing code to fulfil the intentNext, we write some code to fulfill that intent, and we write just enough code to get to the passing test with that failing specification – the bare minimum to meet that mini specification. Run test again to confirm it passesNext, we run our test again to confirm it passes. Now we are in a stable state. All our specifications are passing. Safety checkOnce we run our test, we have the safety check that guarantees that if anyone tries to change the code or wants to modify it, we can run it and still be able to catch any issues with our approach. This safety check guarantees that our test will detect any issues before it goes to production. The open “code” road is ahead of us to express our intent in the form of test mini specification, and here’s where we divide and conquer: we test mini specifications for one behavior, and then we test another one. Following this, we accumulate and record all the requirements we intend to implement. TDD gives us an independent path verification that the coding actions matched our intent. It offers a way to verify that what we have coded matches and implements our intent.Evolving Your CodeNow, we have more time to improve our code and bring more quality and refinement to the code. This is true since we have our tests embedded in the CI/CD pipeline and ready to catch any defects that could have been introduced by the changeTo make our tests/ code more expressive, we can start changing and modifying it to make it more general, readable, and simpler. And to do so requires a three-step process known as the Red-Green-Refactor cycle.The Red-Green-Refactor CycleWhat is the Red-Green-Refactor CycleIn the Red-Green-Refactor cycle, the process of TDD is broken down into three stages:Red: Write a test that failsGreen: Write code and see it passRefactor: Make the code greatWhy use Red-Green-RefactorThrough the cycle Red-Green-Refactor cycle, we try to improve the quality of the code to reach a stage where the code is:Easy to read Easy to change Workable Testable Easy to maintain in the future Simple and efficient And also to achieve the following attributes of good code:Modular Loosely Coupled Cohesive Separation Of Concerns Information Hiding TakeawayWith TDD, it has become easier to achieve the attributes of good code, as mentioned above.When we try to refactor our code to achieve these attributes, TDD gives us the assurance that we have already tested any changes that we want to make, and if any issues arise with our code, they will be detected before production.The outcome of the TDD approach is a unique test, and these unique tests can be integrated into the continuous Integration/continuous delivery (CI/CD) process. With this integration, what we want to achieve – and what TDD helps us achieve – is that whenever someone changes the code, the process of TDD will run. As part of that, we can find any defects or issues before the code goes into the QA environment and, of course, before it goes into production.In software development, TDD is one of the top approaches to tackling complex development and ending up with clean, workable code. To learn more about other methods and what is best suited for your business, this article about TDD, BDD, and DDD is not one to miss!
Test-Driven Development: Improving Efficiency with a Mindset Change
When was the last time you were tasked with test automation but ended up spending more time and effort on maintenance?If you’re reading this article, chances are you’re familiar with test automation and may have even conducted it yourself. In case you’re not, this article on The What, When, and Why of Test Automation will give you a quick refresher on what it is and why/when you should use it.However, if you’re like other newbies, you may have managed to design a framework, but then you’d spend a lot of time, effort, and resources on simply maintaining it.That right there is what happens when you do test automation without knowledge of the best test automation tools and practices.And if you read further, you’ll find precisely what the best practices for test automation are and how they can get any beginner started with test automation in the most efficient way.Framework SelectionTest automation is heavily dependent on tools, low-code or no-code.Therefore, selecting the right tool is essential for high ROI with test automation. Here are a few points you should consider while choosing a test automation tool:Scope of testing: Will you be testing web-based applications or mobile-based applications? Or both. In order to test only web-based applications, there are several tools like Selenium or Cypress that are pretty powerful. In contrast, for mobile-based applications, Appium is still considered the go-to tool. Available support: Whenever opting for a test automation tool, one should always consider the level of documentation and the degree of support you can get from the respective community, if you ever need it. Since Selenium and Cypress have been the favorite tools among the testing community for quite some time, the level of support you can get from the community is incredible. Tech stack: Another factor to consider while selecting the automation tool is the tech stack your dev team uses to develop the application under test. Since you’d want to keep and execute your E2E tests in the same repo and environment, respectively, it only makes sense to use a tool that uses the same stack. For example, if your application is being developed using Angular, you might want to use Protractor, which was developed specifically for testing Angular applications. Open Source or not: Depending on budget constraints, one may choose to use open-source tools such as Selenium or Appium for automation purposes. However, it is important to remember that all open-source tools are not inferior to their commercially available counterparts. Available Features: While choosing the tool, you should check if the automated testing tool supports record-and-playback test creation as well as manual creation of automated tests. Does it include features for implementing checkpoints to verify values, databases, or key functionality of your application? Does your tool integrate with your CI/CD pipeline, such as Jenkins or Azure? Or a source control such as Git? Does the tool have the ability to test enterprise applications? Does your tool offer out-of-the-box support to test packaged applications like SAP, Oracle, and Salesforce? Selecting the Right SelectorsUsing the right kind of selectors to get your elements for your automated tests is the only solution for flaky tests. If you select un-reliable or generic selectors, your tests are destined to fail. For example, using Xpaths or generic class names as selectors will result in flaky tests and a lot of maintenance for the test suite, as they’ll be invalid whenever there is some change in the UI of the page under test. In my personal experience, I’ve found the CSS & JQuery selectors more reliable and effective.Let’s assume we want to get the following element:Which of the following selectors do you think is more reliable?Xpath: //*[@id="container"]/a[1]CSS: a[href="https://youtube.com/]Which Tests Should You Automate?It is impractical to automate all testing, so it is vital to determine what test cases should be automated first. Good test cases for automation are ones that are run frequently and require large amounts of data to perform the same action.You can get the most benefit out of your automated testing efforts by automating the following:Repetitive tests that run for multiple builds Tests that tend to cause human error Tests that require multiple data sets Frequently used functionality that introduces high-risk conditions Tests that run on several different hardware or software platforms and configurations Tests that take a lot of effort and time when manual testing Test Cases that touch a key piece of functionality and should be run before release Test CoverageHere we’ll not be talking about test coverage in terms of unit testing but the different types of testing you should cover as part of your automation test suite even if it weren't required. Here are a few that, in my personal opinion, should be covered as part of best practices implementation:Accessibility Testing: Without going into too many details about accessibility testing, as in what it is or why it is important, the borderline is your application will perform better if it follows the WCAG guidelines, and while it should be part of development itself, nonetheless it should be part of your automation testing. With tools like the AXE engine, it is very easy to integrate the accessibility test cases within your existing test suite. API Calls: Even though APIs are tested separately by the QA and the development teams, it is always better to check the API calls on runtime while they’re being made from the application. For example: If you need to test a login page, sometimes the errors on the FE are very generic, and they’ll not tell you more than ‘Something went wrong.’ Still, as a tester, while you need to observe if something goes wrong, you also need to identify why it did. In this scenario, if you’re inspecting that API call made on clicking the login button, you’ll get the exact response and status code back and can quickly identify what exactly happened based on it. Client-side Performance: While server-side performance is important and is covered extensively during testing, client-side performance is equally essential and, most of the time, is left untested. A general client-side performance audit will tell you things like the FCP and LCP, which is the time your application takes to load the first item versus the page's main content. The easiest way to implement this is by using Google’s lighthouse integration. Visual Testing: I’ve covered this topic in detail in my other article on Visual AI and Autonomous Testing, but just for introductory purposes, with modern AI/ML tools available like Applitools and Percy, it doesn’t make sense to keep validating your application’s look and feel via coding assertions. Implementing Visual AI testing in your automation suite will save much of your time and resources, especially with maintenance. Test Structure & StrategyWhile designing your test automation suite, you must take into account the importance of a good test structure and strategy that can benefit not only the live development but also its maintenance. Following are a few points that have helped us in the past:Test suites: Structure your tests in the form of test suites that can be independently configured and executed. If each test case represents a piece of a scenario, such as the elements that simulate completing a transaction, use a test suite. Self-contained /Independent: While writing your tests, make sure they’re self-contained, meaning that they should not depend on their successor tests to pass in order for them to pass. We’ve seen many examples where teams build their tests to run in a sequential order while it's opposite to what the automated test suite is supposed to be: flexible, agile, etc. Data-driven: Always make sure your tests are data-driven in order to ensure the reusability of the work that has already been done. By using a data-driven approach, you can generate test cases just by changing the data stored in external files. Page Object Model: In some cases, when your application under test is prone to frequent UI changes, it is a good idea to implement POM for your automated tests. It not only ensures the reusability of the component but also helps in the readability and maintenance of the test code. Dedicated Automation EnvironmentThis might not be possible for each team or project. Still, since we’re talking about the best practices, I have to mention Dedicated Automation Environment, as having one will only increase the productivity and overall efficiency of your automation framework and hence the crucial time and resources of your team.It has been observed many times before, more than I hate to admit, that tests frequently fail due to unannounced changes deployed to the environment in which the tests were executed, especially when multiple teams are working with the same dev or test environment. Therefore, having a dedicated environment that is up to date with the latest changes that are supposed to go live in the next release is essential for non-flaky tests.Testing Cross-platform and On Real DevicesMore often than not, it is observed that testers design their tests to execute only on a single platform. It could be a platform like a desktop against mobile or in terms of a browser like Chrome vs. Firefox, while as a best practice, your tests should be designed in a way that they can be executed against multiple platforms, and they should.To have ample test coverage for a web application, you should ensure you execute your tests against all the popular browsers and on all the popular devices such as desktops, tablets, and mobile. Moreover, if you require more realistic results, your tests should run on real devices instead of emulators or simulators. This task can be humongous and expensive if done manually or even using local resources hence making use of cloud platforms such as Browserstack or SauceLabs can be beneficial.ExecutionThis is where all your preparation and work come to life. This is also the most critical step to automate, which most of the testers fail to do. It is often understood that integrating the E2E tests into the existing CI/CD pipelines is a DevOps job that is somewhat true but not always. I like to do it myself, as it gives you more control and ownership of your tests. Anyhow, whoever does it, ultimately, it should be done, and the following are a few points to remember while making doing so:Parallelize the automated test cases that are independent. For example, if a test suite contains 5 tests that are self-contained, none of them should wait for the previous test to finish before starting. Use multiple machines and servers to run your tests in parallel. It can decrease your execution time from ~25 to ~5 minutes. To increase the speed of your release while maintaining the quality, configure your tests to run on each pull request from a child branch to the main branch. This will ensure that none of the new changes are breaking any existing functionality and that the code is safe to merge. If your team doesn't push out frequent releases, it might be a good idea to execute your test cases based on a cron-job to ensure nothing unexpected breaks the application. For example: Run the tests every other day at noon or every first Monday of the month. ReportingFinally, in the last step, if there is no thought-out plan for effectively collecting and analyzing the test results, the automation effort can be all in vain. Therefore, a well-defined process will save teams from many conflicts and resources in refining the application.Automation should serve to reduce the amount of time QA teams have to spend verifying test results. Set up adequate reporting infrastructure using the tools that will generate high-quality test reports after each cycle. If possible, assign your tests different but relevant tags to group and filter them out easily in the reports. A good test summary report should be created after each cycle and shared with all the stakeholders via multiple mediums, such as Email & Slack.TakeawayEach application of any complexity will likely have its own combination of testing requirements, and none of the discussed points together will ever be in play.It is still essential to know the best practices for making the right decisions early on. Study these practices, and implement them in a way that best suits your software, business, and users.If you want to take your application of test automation a step further to practice better, faster QA, this 30-minute video will give you all you need!
Best Practices for Test Automation: Framework Selection, Test Coverage, and more
UX, UI and Product Designers work within a space of continuous learning. The very nature of what we do is iteration!In a highly competitive field, good designers look to expand their knowledge and keep up with the latest tools in order to best serve their users.So I asked designers from our team about how they stay sharp and what methods have worked best in their experience.Question 1What suggestions would you give to keep up with the latest knowledge and advancements in technology?Name: Adriano RenziTitle: UX ResearcherResponse:I try to check how technology may evolve by attending scientific conferences and reading scientific journals and being part of discussion panels related to interaction, information architecture and AI. Many presented researches anticipate by 2 to 5 years what will come in the future.Name: Rahul JacobTitle: Product DesignerResponse:As the tech industry evolves rapidly, attending conferences, tech events, and joining online or in-person communities can help with gaining knowledge and adapting to new technologies. It is also important to spend some time every day following news from technology focused publications and online tech news sites. Connect with tech Influencers, tech companies devoted to technology and emerging trends through social media.Name: Osama NadeemTitle: Product DesignerResponse:I have subscribed to many designers/content creators on youtube, blogs and I follow tech leaders on platforms like linkedin where I get many updates regarding what’s happening in the tech industry.Name: Umer FarooqTitle: Principal DesignerResponse:I used to look around articles especially on IDF and try to find if it’s timeless. Trends come and go but there are certain workflows, behaviors you can learn by participating in communities. I can relate to Figma as I lost the train because of my too much involvement with Adobe XD. A lesson well learnt and now catching up well with Figma, the best design tool.Name: Dana MitchellTitle: Sr. Product DesignerResponse:Step one would be to identify what technology you want to keep up with. Go with what you are passionate about or technologies that pique your interest. Step two is figuring out how you want to keep up to date. I leverage videos, audiobooks, and podcasts for my learning. Lastly, step three, use that knowledge. Write articles, design flows, edit a video, whatever format it takes! By using your new knowledge you will anchor it in your mind and create a personal design process that is dynamic and designed to grow with you.Name: Cordelia FongTitle: Sr. UX DesignerResponse:I tend to actively keep up and devote time to reading the tech news daily. Attending conferences and workshops, networking with designers are also a priority, following great leaders and participating in online communities.Question 2Are there design leaders or blogs you regularly follow?Name: Adriano RenziTitle: UX ResearcherResponse:I like to check what is happening in projects from Ben Schneiderman, Floridi, Resmini, Benyon, Agner, Nigel Cross and Resú. They are usually very active in continuous publications on technology. I do not look for design blogs because they usually present no reference of their facts.Name: Rahul JacobTitle: Product DesignerResponse:I consider Don Norman to be one of the most influential design leaders when it comes to user experience and usability research. In addition, I find John Maeda to be a true inspiration since he is the pioneering voice of simplicity that encourages people to use technology to simplify their daily lives rather than complicate them.Dribbble, UX Collective, Medium, Smashing Magazine are all excellent resources for finding great articles, stories, content, inspiration, research, as well as for thinking critically.Name: Osama NadeemTitle: Product DesignerResponse:Among many designers that I follow, I admire Don Norman, Anudeep Ayyagari, Eric Keneddy and Joe Natoli. I like how Don Norman has a very simple yet effective way of explaining things, Anudeep Ayyagari has a rather funny tone which is also pretty interesting. Eric Keneddy’s UI course that I took a couple of years ago was very impactful and is still very relevant and Joe Natoli has a very unfiltered and impactful way of explaining things.Name: Umer FarooqTitle: Principal DesignerResponse:My all time favorites are Normans, Nick Babich, Mark Hassenzaal, Frank Spillers. The guys are so expressive and communicate ideas in a seamless fashion that you don’t feel burdened.Name: Dana MitchellTitle: Sr. Product DesignerResponse:Not a blog but podcasts! While not specific to emerging technologies in design, there are a lot of podcasts, like The Way of Product Design, Design Better Podcast, 99% Invisible, etc. have helped me to build a better design practice. Hearing design leaders speak about their problems or projects and the solutions that came with them helped to expand my knowledge of what is possible in product design.Name: Cordelia FongTitle: Sr. UX DesignerResponse:My favorite design leaders are Luke Wroblewski (Mobile First), Steve Krug, Don Norman and Kim Goodwin. I followed a few blogs when I started as a UX designer: CareerFoundry, JustinMind, UX Mastery, UX Matters. They provided such great depth of information on UX careers.Question 3What are your main sources for continuous learning?Name: Adriano RenziTitle: UX ResearcherResponse:Conferences (HCII, AHFE, SPGD, FTC), Journals (Ergodesign Journal, Philosophy and technology, Design de Informação), Theses (Esdi, PUC-Rio), Books (Mihaly Csikszenntmihalyi, Resmini, Nielsen, Rosenfeld, Morville),Name: Rahul JacobResponse:Every once in a while, I check if there are any tech-related crash courses, webinars, online tutorials, and networking events occurring. In addition, I reach out to my friends, coworkers, and colleagues who have the most knowledge on a topic for recommendations: articles, books, lectures, or other resources.Name: Osama NadeemTitle: Product DesignerResponse:I consume lots of content on youtube and I’ve subscribed to many design specific channels there. I also read articles on medium and have followed people on linkedin who post useful design learning resources. Whenever I come across some content and I don’t have the time to skim over it right then, I save it in my Notion workspace and revisit it later. I also try reading books, but that doesn’t happen too often.Name: Umer FarooqTitle: Principal DesignerResponse:I prefer watching rather than reading. There are many channels out there to follow and everyone is doing some seriously amazing work.A recent addition was a guy from Canada called Faheem.Name: Dana MitchellTitle: Senior Product DesignerResponse:My source changes based on my intent. I find that, as a designer, any social media algorithm will eventually serve me design-focused content. You like one of Zander Whitehurst’s “Supafast” videos and the algorithm will serve you more like it. This is a fantastic way to take small “sips” of new knowledge, which makes learning more approachable. However, I do have a more targeted approach when it comes to learning specific skills and I will seek out anything that can help me achieve my goals. Articles, tutorials, videos, documentation, whatever!Name: Cordelia FongTitle: Sr. UX DesignerResponse:LinkedIn learning, courses, books, blogs and attending events.Question 4What design books do you recommend?Name: Adriano RenziTitle: UX ResearcherResponse:Information Architecture by Rosenfeld, Pervasive Information Architecture by Resmine and Rosatti, Arquitetura Pervasiva by Luiz Agner, Lean UX by Gohelf, Seiden, Flow by Csikszenntmihalyi, Les Technologies de l'intelligence by Lévy, Designing Interactions by Moggridge.Name: Rahul JacobTitle: Product DesignerResponse:The Laws of Simplicity by John Maeda, The Design of Everyday Things by Don Norman, 100 Things Every Designer Needs to Know About People by Susan Weinschenk, and Start With Why by Simon Sinek.Name: Osama NadeemTitle: Product DesignerResponse:The Design of Everyday Things by Don Norman, Just Enough Research by Erika Hall, Giving A Damn about Accessibility by Sheri Byrne Haber, Don't Make Me Think by Steve Krug, and Refactoring UI by Adam Wathan & Steve Schoger.Name: Umer FarooqTitle: Principal DesignerResponse:Don’t Make Me Think by Steve Krug and The Design of Everyday Things.Name: Dana MitchellTitle: Sr. Product DesignerResponse:Inspired by Marty Cagan, The Mom Test by Rob Fitzpatrick, Stories That Stick by Kindra Hall, Think Again by Adam Grant, Actionable Gamification by Yu-kai Chou, and Atomic Habits by James Clear.Name: Cordelia FongTitle: Sr. UX DesignerResponse:Many of you may have known these books, but these are still my top 3: 1) The Design of Everyday Things by Don Norman 2) Don’t Make Me Think: A Common Sense Approach to Web Usability by Steve Krug 3) The Elements of User Experience: User-Centered Design for the Web and Beyond by Jesse James GarrettQuestion 5Do you find your personal interests influence your professional space? What inspires you as a designer?Name: Adriano RenziTitle: UX ResearcherResponse:Not really. But I find my enthusiasm and learning interest to influence people around me.Name: Rahul JacobTitle: Product DesignerResponse:Looking back at my childhood, I’m glad I was exposed to Music and Art because it taught me valuable lessons that I don’t believe I would have learned otherwise. Through music and art, I learned how to think outside the box, discover better ways of doing things, boost confidence, productivity, self-esteem, and well-being.Name: Osama NadeemTitle: Product DesignerResponse:I’ve always been fascinated about how things work, that mindset has enabled me to look at problems keenly and that is always my approach when it comes to design. My personal interests such as music and gaming always keep my brain creative because both of them allow me to think outside the box and be creative. These two are among the attributes that keep me motivated as a designer.Name: Umer FarooqTitle: Principal DesignerResponse:I prefer to spend time watching seasons especially with the backdrop of WW2 and keenly observing the culture, the food, the language and how people use to behave in certain circumstances etc. A good designer can always get inspired from whatever content he digests every day.Name: Dana MitchellTitle: Sr. Product DesignerResponse:My inspiration as a designer is to creatively solve problems to improve the lives of people. With my personal interests being balanced between mentoring new designers and expanding my skills as a novice potter, I find that these interests feed directly into my professional work. Mentoring has improved my ability to speak about design and to convey my ideas more effectively while pottery has taught me to let go of my work and accept that not everything is going to make it out of the kiln how I want it to.Name: Cordelia FongTitle: Sr. UX DesignerResponse:Curiosity and finding your passion. I tried things that interested me. Find out what I am good at, and I experimented with Illustration, Sketching, Painting, Interior Design, Fashion Design, Jewellery Design, Advertising Design, Website Design, and which led me to where I am
Ask A Designer Round 3: Staying Curious
When was the last time you considered “team happiness” as a KPI for yourself?As a manager, it’s vital to be very aware and conscious of how your team feels at work. Are they happy with what they’re doing? Do they have enough opportunities to grow and develop their skill set?Having a culture of open and transparent communication within any team can have very positive effects on its function.Open communication helps foster trust among team members by creating a safe environment where they can share their thoughts and ideas.It can also boost the team’s productivity, creativity, and innovation. Above all, if practiced regularly, this can create a continuous feedback loop that can help the team to improve, leading to better decision-making and smoother achievement of their goals. Combined with values of honesty and support, this can enable managers to build hyper-successful teams.Then again, it is important to understand that communication is a two-way street, and sometimes, growing teams and managers fail to look at it that way. The team lead, or the manager would always be the one to provide feedback and consider it to be sufficient.Instead, managers should engage in open dialogue and actively seek feedback for communication with their team members. This will help them understand how they are perceived and identify any concerns their team members have.Not only will this activity make them more effective in their roles, but when each teammate feels that they have a say in how the team is run and that their opinions are valued, their motivation and commitment to the team will increase massively.Open collaboration is not just limited to the team and the manager, but it should also be encouraged amongst all stakeholders involved to ensure success of any project. To achieve this, be sure to check out this article on DesignOps.Focus Areas For ManagersFor managers, we can identify the following areas upon which they can measure their perception within their teams and seek improvement:Team Happiness: Do their team members enjoy working together under their management? Growth & Upskilling: Do managers provide enough opportunities for growth and development for their team members? Leading through an example: Do managers manifest the qualities in themself that they expect of their team members? Support: Do managers provide the right support to their team members in their work? Open to feedback: How open are the managers about their shortcomings or listening to their team members about their own mistakes? Transparency: Do the managers communicate proactively and transparently? Culture: Does the manager create an inclusive culture for everyone in their team to thrive? Recognition: Does the manager put an effort to recognize their team members when they do good work or just focus on the criticism? A lot of answers to the above questions can be found by simply maintaining open communication with your team, like in this candid conversation with designers.Feedback Form TemplateNow to put these in practical terms, here is how someone can use the following template to get feedback from their team members.Instructions for the respondents:Keep the survey confidential and anonymous, so people can comfortably respond. Illustrate the intent of the survey, which is to get feedback from people for self-improvement. Don't confine the responses to p options or a grading system. Keep the answers open and let people express their sentiments. Here is an example:The QuestionnaireFor the questions of the feedback survey, it is a good idea to give examples of positives and negatives that can help the team members understand better what they should be looking for in terms of support from their managers. It is also a good idea to enable the shuffle option in the questionnaire if possible to randomly sort questions to remove fatigue bias for some questions.Are you happy or proud to be a member of your squad/ team? Do you feel connected with your peers? Does <> provide you with enough opportunities for growth and advancement in your career? Examples can be discussion around opportunities or asking you to practice certain things that can improve you as a professional. If this has happened, highlight something that you disliked or liked about this. Does <> inspire or motivate you to do your best work? If yes, How? Example: Either by his example or sharing inspirations or talking with you or something else. How would you describe the level of support offered by <>? Example: Does <> support you in your well-being, work challenges, work-life balance challenges, growth challenges, etc.? You can also mention areas where you may have felt a lack of support from <> and would like that to improve. How comfortable do you feel in giving feedback to <> or sharing anything that you don't feel good about? What is <>'s general response when you have shared something difficult with him before? Do you feel that you are treated fairly without bias? How would you rate transparency in terms of messaging, policy sharing, and general communication from <>? What could be improved? Describe your squad's culture in fewer than 20 words. You can also mention if you don't see any culture or a culture you would like the team to strive for. Are you encouraged to lend others a helping hand if they are stuck with some task or take help from others and be comfortable about it? How strongly do you feel valued at work? Does <> recognize your good work or exclusively focus on criticizing? How likely would you recommend a close friend, family member, or someone you care about to work in the <>'s team? [BONUS Question] What is something <> can start doing, do more of, or stop doing to support you to continue doing great work? TakeawayConstructive criticism that doesn’t discourage, but in fact motivates team members effectively will always prove to be a successful method.And as great as achieving effective communication can prove to be, it’s vital to make conscious and tangible efforts in the way of doing so.With the feedback form template and questionnaire provided above, you should find it easier to actively contribute towards improving communication and collaboration within your team.For a more in-depth guide on how you can enhance the growth and performance of your design teams, read this detailed article on Designer Career Ladders.
Empowering Your Team through Feedback: A Manager’s Guide For Effective Communication in the Workplace
Many product development teams often face one very common yet very significant challenge; scaling unified experiences across multiple channels, and accomplishing that without significantly reinventing the software itself.The main culprit behind this problem? Usually, a lack of reusability maturity across the organization. In the initial phases, most startups are heavily focused on shipping out the first version of their digital product as early as possible and collecting feedback early on in the process. While this is understandable, given that startups often have tighter timelines (and budgets!) compared to an enterprise-level business, it does impact the product itself.Usually, as a direct result of focusing too much on a fast rollout, many important architectural decisions can (and do) take a lower priority than shipping out the product itself. And to make matters worse, these gaps continue to compound and become harder to deal with the longer they stay on the back burner.It is important to note that the engineering teams (in most cases) are not to blame for this situation or the problems that subsequently arise from it. Regardless of the extent of their competence, a product’s architecture is directly impacted by decisions taken very early in the development process, especially where there is very little foresight available into the future.However, before a product can truly enter its growth stage, it could prove prudent to take a step back and address the underlying issues that could be impeding the needs associated with the growth stage of the product’s lifecycle.What makes reusability so important? Accelerating time-to-market When leveraged correctly, reusing components can create space for faster development and save product development teams more time since many of the necessary building blocks will already be available to them, making the need to build everything from scratch redundant.Centralized maintenance Any software periodically requires updates to function optimally. With reusability in the mix, any time a piece of software needs updating, developers need only to update it in the centralized master component, instead of manually updating each section of the software. This allows not just for easier maintenance but also speeds up the process in parallel.Lower operational costs One of the most important long-term benefits of reusability is that it allows development teams to increase their efficiency, helping them save time both in terms of development work as well as eliminating redundancies and duplication.New business lines Reusability at the system level can help firms discover new business lines. For example, with the exposure of the existing system functionality to new customers, especially those that live outside of the eco-system e.g. API integrations, SDKs, etc.Reusability maturity frameworks From a product developer’s perspective, there can be several suitable reusability frameworks and models. However, choosing the right one is crucial to product success. Based on my professional experience, two such frameworks can prove very helpful in a broad range of situations.First, the reusability framework pioneered by Koltun and Hudson (1991). This framework evaluates reusability maturity on an organizational mindset level. It does so primarily by taking into account the various processes and internal practices that the development teams within the organization tend to follow.The second framework that I have found to be very effective solely focuses on the assessment of reusability maturity in the context of the software itself. These assessments can include:Level 0️ – No reusability: Cut and paste. Manual update at all instances.Level 1️ – Object & Function Reuse: Small-scale software components that implement a single well-defined object or function may be reused.Level 2️ – Component Reuse: Components of an application from sub-systems to single objects may be reused.Level 3️ – Application Reuse: An application may be reused either by incorporating it without change into others or by developing application families.Level 4️ – System Reuse: Complete systems, which may include several application programs.Software Reusability Maturity modelMoving up the reusability maturity curve The impact of leveraging the assessments of a reusability maturity framework is to determine where you stand on the reusability maturity curve. From that point, the next logical step is to try and move up the curve. One way to do this is to establish a roadmap and identify the next necessary steps along this roadmap. It is important to remember that, regardless of your position on the curve, the phrase “build once, reuse everywhere” should be a north start for development teams.For example, in the second framework examined above, moving between Level 0 and Level 2 would primarily require teams to consistently adopt and adhere to better programming practices. In this case, product managers could consider using a design system as a possible solution in promoting the reuse of components across different areas of the application.A design system can help to create a comprehensive library of components, allowing product teams to efficiently and consistently deliver a unique brand experience through a single application.Levels 3 and 4, however, may need more sophistication in key decisions, especially at the architectural level. Products may require you to make certain changes to the tech stack or move towards complex cross-platform options, based on what would best meet your requirements.The correct architecture would then allow product teams to collate various application components and then republish them separately as SDKs or separate platform services.Microservices architecture can enable product development teams to build specialized APIs which can also be reused outside of the core application. This, in turn, could help to create entirely new business channels, for example, by exposing the existing service to 3rd parties for integrations.Final Thoughts This blog does not mean to imply that achieving reusability maturity is the solution to all product problems. In fact, working with reusable software code can prove somewhat challenging for many developers. But there is the fact that with software components reused across different sections of the same app or multiple apps, you can create additional space to consider and test out different scenarios for each release, minimizing the impact on the rest of the modules in the software.Therefore, moving up the ladder of the reusability maturity curve is more likely to help with product sustainability through the growth and maturity stages in its life-cycle.
Understanding Reusability Maturity Assessments
If you’re someone who thinks of frameworks as a scam — you’re not alone.Many people have come to associate product frameworks with trouble rather than a solution to real world challenges — a conception that has formed over the past few years.Unfolding the Stigma Around Product FrameworksThe stigma around product frameworks stems from companies and individuals that offer “frameworks” only as a means to lure users towards their products — a mere stepping stone in their marketing efforts.As a result, what we’re left with is a wide variety of frameworks — for every topic, and in every industry — with little knowledge on how to actually use them.And today, it seems the situation has gotten quite out of hand; I recently came across a framework for relationship counseling that contained “metrics” to track all violations caused by your partner. And of course, these would be discussed in weekly review sessions.So, it was inevitable that frameworks, originally meant to provide a well-organized structure to any process, are now more often than not looked at in a negative light, especially in the product community.Most aspiring product managers have fallen prey to the facade of flashy frameworks that only cause more trouble than offer any viable solutions.But we’re not here to completely discredit product frameworks! After all, at its core, the true purpose of a product framework is to offer a template of practices you can follow to bring structure to any process.However, without certain measures of caution, using your product framework would always be a gamble.4 Things To Consider When Using Product FrameworksThe success of your product framework will always depend on certain factors. And being cautious about each of them will significantly improve your chances of success with your frameworks.1. Find the right framework for your productNot every framework you come across will be suitable for you and your product.As a standard practice, it is critical to perform your due diligence in seeking out the framework that best suits your needs and fits your product well.In your research for the right product framework, you’ll also likely come across two frameworks with contradicting guidance on how to use them.This doesn’t necessarily indicate that either one of the two is a scam; they just might not be relevant to your situation.A good way to identify the right frameworks is to watch out for non-credible sources and seek out frameworks that have actually been tried and tested by others with similar use cases.2. Get appropriate and authentic trainingProduct frameworks are far from being straight-forward and easy to adopt right away.Most frameworks you find will require some level of familiarizing and training.There are some complex product frameworks for which you’ll find dedicated training programs online as well. But even this is being exploited and turned into a scamming business, so it’s important to be wary of what you’re getting yourself into.I’d like to emphasize here that there is no need for any paid certification to get started with using a product framework. In today’s world, most of the information and guidance you’ll need is already available out there — free of cost.All you need to meticulously do is filter out the authentic ones from the rubble of unreliable ones.The best way to do this is to:Get a sound understanding of any framework you pick for your use-case. Understand the limitations of your chosen framework, where it is most effective, and where it fails Review all original material available about the concept wherever possible (look for the original author of the information to check its authenticity). Connect with other community members and learn from their experience of the product framework: what worked for them? What didn’t? 3. Avoid getting blindsidedNo framework you find out there will be 100% perfect!Product frameworks are meant to be an aid to your process — not a stringent rulebook that is rigid and impedes the progress of your product team.Anyone that considers product frameworks as the “absolute truth” would be a fool.For every product, every team, and every circumstance, there will be different and distinct requirements. There’s no guarantee that what worked for companies like Google, Facebook, or Amazon will work for you — so it’s best not to get too excited and try implementing it on your own use-case.It’s good to trust your gut and base your decision on what the framework actually brings with it, what it recommends, and whether or not it aligns with your fundamental principles.Before getting started with a product framework, make the effort to truly understand what you can modify about your process in accordance with the guidelines and what cannot be changed for your specific framework.4. Don’t over-rely on frameworksAlways remember: there’s no critical need to use frameworks — and using them just for the sake of having a framework in place is possibly one of the worst reasons to invest your time and effort into getting a framework.The end goal in product management is always to solve customer problems, and do so effectively. Whether or not you have any particular framework in place holds no importance to the customer.If, for example, you already have a prioritization method or product prioritization framework that works for you better than MoSCoW or RICE, it’s completely fine to continue using that.Product frameworks are very helpful when it comes to adding an overall clarity to your process and approach, and even more so for newcomers who need concrete and clear guidelines before handling any abstract task.TakeawayProduct managers want to solve customer problems, and product frameworks are tools that help you solve problems in pretty much every situation.For any process, and at any given point in time, there will always be multiple tools that you can choose from and use. But the important thing here is to identify and choose the right tool that works well with you and your product and actually helps you execute your processes more efficiently.As with any tool, it’s important to know how to use the framework effectively before getting started.Product frameworks are created to address pre-defined problems, but in the real-world, your situation will always be unique and the tools you use may require adjustment in accordance with your circumstance.Finally, there’s absolutely no obligation to continue with an existing tool. If you have another method that does the job better, don’t take the added burden of additional costs to use the tool.Learn what works, gauge your own limitations, and apply methods and product frameworks that actually align with your needs.
4 Things To Get Right When Using A Product Framework
To build and manage a product in the digital world today heavily relies on data and analytics.With data guiding important decisions, there remains no doubt that data-driven decision making will continue to dominate as the modus operandi for digital businesses.In this article, we’ll cover three techniques that can help you make data-driven decisions.But first, let’s understand why using data to make decisions is such an important part for successful product companies.Why are data-driven decisions important?While data has always been integral to any business in any form, never has it been more important to extract, collate, and analyze this data efficiently.With massive amounts of data now generated on a daily basis, there are several reasons successful organizations play such close attention to user data analytics:1. InnovationCompanies that recognize the importance of data-driven decisions don’t just look at it as a digital “trend”. They treat it as an essential asset for the business; with data guiding important decisions, digital insights are seen as an opportunity to seek more knowledge and drive a culture of innovation within the organization.2. CompetitivenessWith a higher dependency on data, you gain the ability to evolve your business in accordance to emerging trends.Not only does this increase the adaptability of your organization to keep up with the latest patterns in the industry, but such adeptness also helps you use data to make better decisions that ensure you stay ahead of your competition.3. Improved communicationWorking with key insights and user data analytics is a great way to keep your organization aligned with important KPIs that contribute towards business growth.By being on the same page, your organization can perform as one cohesive unit that never loses sight of what needs to be achieved and which kind of user data is needed for it.4. Consistent growth and opportunitiesOne of the prime benefits to businesses that leverage user data analytics is that of consistent growth and discovering fresh new opportunities.Being able to consistently make data-driven decisions empowers your organization to narrow down on key insights, performance metrics, and actionable benchmarks that keep you ahead of the curve by continually encouraging growth.What is an example of a data-driven decision?To understand how businesses have come to recognize the importance of data and user data analytics, let’s take the example of a business with a large base of millions of active users.Netflix, a giant in the content streaming industry, leverages user data to achieve consistent customer retention.Despite increasing competition in the industry, Netflix has found ways to prioritize the user experience by studying their behaviors, preferences, and watch patterns.With such deep insights, the streaming service is able to recommend relevant and accurate content suggestions to each user, significantly improving their experience on the platform, which keeps customers coming back for more.What are some key factors when making data driven decisions?With a good understanding of all the ways user data analytics can help in your business, it’s vital to keep the following factors in mind for data-driven decision making:Organize your data effectively Format your collected data properly, before jumping into the analysis. Remove outdated information or irrelevant data that is not aligned with your goals, and get rid of duplicated data to remove confusion and arrange the data systematically.Overcome your biases Keeping yourself aware of factual data will limit your bias and help you make more informed decisions that aren’t based on guesswork. It is one of the best practices in data-driven decision making to collaborate with colleagues and consider other perspectives by seeking out more conflicting information.Ensure data literacy Only educating yourself on data won’t suffice; you should ensure that every stakeholder that contributes towards important business decisions is well-versed in working with data. This can be done by making your data more user-friendly and easily understandable, and also by analyzing who in your organization has the capability of working with data.For more tips on how you can effectively manage data, read about our 3-tiered approach in this article on The What, Why, and How of Data Management In the Age of Digital.3 Techniques To Make Data-driven DecisionsNow, let’s walk through some of the most useful techniques that are great for making data-driven decisions.There are several other techniques to drive better decision-making with user data analytics, but the following are found to be most effective:Funnel analysis Cohort analysis Event-based tracking Funnel analysisA funnel analysis is a technique for figuring out how many people proceed through each phase in order to reach a particular outcome on a product.The series of phases is known as a "funnel" because the standard shape used to visualize the movement of users resembles a kitchen or garage funnel.There are some great benefits of using a funnel analysis:Identify the exit pages with a significant volume of visitors Figure out the sources of your best visitors Aid in decision-making for team members and stakeholders Let me share an example of a funnel analysis done where we are looking how many customers started the session on the app, then viewed the ingredients and submitted the review.In the image above, we can see that around four people started the session and 85% of the customers viewed the ingredients.This further dropped until the write review step, showing that there’s an overall 62% conversion rate from session start until the “write review” step.This is how funnels help to understand the drop, on which you can further do different analyses to understand what’s causing the drop and how you can mitigate that.Cohort analysisHow do you analyze your user base to study various types of users when not every user feels the same way about your account?Cohorts are a means to divide up users into subsets based on a shared trait.Let's take the simple example of a blog website.To begin with, imagine a scenario where every person who visited the account for the first time is compared to how they behaved the next time they visited and interacted with it.Then, we evaluate this user behavior at two distinct times, testing for the same characteristic, which is first-time users, as you continuously push out updates, and analyze how various update types affect first-time user behavior.Now, let's say you published some extremely interesting material in January and February and noticed a rise in interaction.But in March, you added some pretty awful, enormous graphics to the landing page, and noticed a decline in retention.First-time user study is useful in such situations because it helps eliminate the prejudices of customers who have some brand loyalty and will continue to use the product despite poor UX.The goal here is to compare behavior and responses to various activities performed using the first-time experience.Cohorts become important in this situation.You need to compare KPIs as you push out new features, factoring in the KPIs you are measuring first. Conversions and retention are the KPIs that individuals most frequently consider when discussing cohorts.In other words, are your users turning into paying clients or are they only returning to your product itself? You can determine what features and KPIs are driving behavior in this way.Event-based trackingLet's look at event-based tracking now.This is the technique I would emphasize the most on because it’s the one that businesses do the least.By examining each and every interaction a user has with your product or your screen without clicking, you can now get into the mind of the user. And using instrumentation is the only way you'll be able to gather this data.Instrumentation should be a consideration for your engineers when they design your product. It's a difficult process because you're adding a lot of instrumentation in addition to writing code and releasing a new feature.But bear in mind: you're not being an effective product manager if you have to do this after launch. The post-launch process is highly expensive, and you've already lost a lot of really important data.Event-based tracking enables you to dissect particular elements of the user experience. How many times, for instance, was a picture loaded? How many times did the same error message appear, or how many characters were entered?This type of analysis enables you to guarantee that your customer experience is excellent. Consider what the client is doing when you create your instrumentation and how you want to instrument it. And some of these are actually rather difficult; it's not always simple to perform.I collaborate with a few of our customization teams, and a lot of these initiatives involve technology hurdles you need to consider right away. Consider your technology choices before moving on with the project.TakeawayIn this article, we explored the many ways data-driven decision making can drive incredible benefits for your business, both externally and internally.On the external front, user data is definitely something to be leveraged if you want to remain competitive in your industry and become a giant, like in the example we saw.Within the organization, staying on top of user data analytics will help keep your teams aligned and focused towards clear, common business goals and place benchmarks and KPIs that all contribute towards your journey to success.If you want to learn more about data-driven product management and which stages of the product life cycle can benefit from the use of user data analytics, don’t skip this article here: Do Products Drive Data or Does Data Drive the Product?Meta description:Data is vital, but useless if you don’t know how to use it right. These 3 effective techniques improve how data is used to drive better decisions for your business.Excerpt:In a world of cut-throat competition in every industry where businesses operate in the digital space, missing out on key user insights and relying on guesswork can prove to be fatal for your business. Leaving things to chance when it comes to understanding what your users really want from their experience with your product can be detrimental. On the other hand, carefully studying your users, their behaviors, and their interactions has endless benefits — both on the outside and within your organization. In this article, Associate Product Manager Ali Ayub Khan reveals three of the most effective techniques that help you drive better data-driven decisions every single day.
3 Techniques To Make Data-Driven Decisions with User Data Analytics
For any organization where design is a core part of the service portfolio, building a design team is a gargantuan task.While filling in the designer roles and laying down designer responsibilities is one big part of it, there lies another immense responsibility on the organization: to provide promising designer career paths for its people.This means building something strong from the ground up, but at the same time, creating a solution that is flexible to your organization’s distinct needs and goals.What is the Career Path for a Designer?When deciding the career path for a designer, the first step is to start by building a career ladder for all designer roles within the organization.At this stage, your objective should be to make sure that the ladder answers the following questions:How do we offer our designers opportunities to grow as individual contributors and people managers? How do we make sure that our titles are standard with the best practices in the industry? How do we ensure consistent titles across all designer roles? In the search for answers to these questions, we came up with the following career ladder:The Dual-Lane Designer Career LadderAlong the product and design career ladder, there are multiple elements and steps that the designers go through.And understanding the hierarchy of these designer roles and their impact is step one.What is the hierarchy in designer roles?Every designer starts as an associate, learning and growing their way to mid and senior levels.After the senior level, designers can decide to pursue either the individual contributor or the leadership path as both offer very different levels of designer responsibilities and role expectations.Of course, designers can opt to switch their paths: An individual contributor can always become a manager and a manager can go on to become an individual contributor at any time of their career.But, with that being said, this usually comes with a big change in their day-to-day responsibilities — and that’s what’s important for designers to understand.Transparent role expectations and designer responsibilitiesNext, it’s important to give designers the tools they would need to succeed in their current roles and help managers have better conversations with designers around the subject of growth and development.In our quest to learn from some of the best standards and examples from the design community and defined clear role expectations for every role.For each designer role’s expectation, we tried to include a good mix of hard and people skills.Below is an example of our product design role expectations.Annual assessmentsNow to add accountability and dialogue to the mix, we’ve seen that running skill assessments once a year is a good way to take first-hand input from every designer, key designated client-side stakeholders, and the team leads. This feedback is then assessed against the designers’ role expectations, and key designer KPIs.We also have the designers fill out the same skill assessment for their own selves when they join the team.In these assessments, we urge designers to be as honest as possible:Ranking as ”Improving” (low) anywhere is seen as an opportunity for growth and not as a sign of weakness.This is the same principle you can follow when discussing the results of yearly assessments that are done from clients and other stakeholders.For the rating of each skill, we use the following grading system that we learned from our amazing friends at Salesforce.The word scale makes a lot more sense than the number scale and reduces subjectivity when it comes to rating designer skills.The assessment results from different stakeholders are received, aggregated, and shared with each designer and their design leads. This process end-to-end is duly managed expertly by our Design Ops team.The leads then engage in conversations with designers to help them better understand the expectations of their designer roles.It helps to break the expectations into achievable goals and help the team members get better at what they do in their designer roles.The leads are also responsible for creating opportunities for designers to help them polish their skills in areas where client-side opportunities are not sufficient.This conversation is carried over to the weekly one-on-ones between the designer and their lead and they both work together for the designer’s career growth.Above is a hypothetical example of what a designer assessment result looks like. Different colors represent scores from different stakeholders.The further a point is from the center of the radar determines the level of proficiency in each skill. 0 in the graph is mapped to Non-Scoring, 1 is improving, 2 is achieving and 3 on the graph is excelling.In the above chart, the designer is perceived to be excelling in visual design by one stakeholder and perceived to be achieving in visual design by their other stakeholders.Looking at the assessments, we commend the designers for being great at so many things.Then, for the following year’s learning and development program, we start by focusing on areas where we see our designers not excelling or fully achieving expectations and define priorities from there.Seeing designers excelling at multiple ends also gives us an indication that the designer has started to outgrow their current role.For example, in the above evaluation, we would want to work with the designer on their Product Thinking, Mentorship, Culture, Design Thinking and Facilitation skills as starters and then move on to strengthening their other skills.One of our key learnings when we implemented this at our organization in recent years has been that transparency and good communication are the key to any initiative’s success. With these assessments, we also try to create an opportunity for open and clear conversations.Case in point: Here is a recent communication that I shared with my team on how they can make the best of their assessments in a very positive way and use this information to their advantage.TakeawayDeveloping such processes within the organization is an achievement in itself:It feels amazing to look back at all that is achieved along the way, and while this is by no means complete, it’s an opportunity to always take feedback from people all over the organization and use it to improve our processes.For us, the next step is replicating the same model for other roles of Designer, Research, and many more across the Product-Design career ladder. This is also an opportunity to build a model where designers can give feedback on the leaders of their team, thus maintaining a healthy atmosphere built on constructive criticism and focused towards design success.
Designer Career Ladders: Enhancing the Performance and Growth of Design Teams
For frontend developers, the ease and simplicity of the development process has a significant impact on the quality of their output.And, while in a simple UI layout, either vertical or horizontal arrangements of cells are easily created with the UICollectionViewFlowLayout, which is the default layout for UICollectionView, there remains one very pressing challenge for frontend development:As times passes, the layouts are getting much more difficult to implement as the UICollectionViewFlowLayout lacks some important functionality, thus forcing developers to create custom layouts to achieve those functionalities in an enhanced way.What can developers do to keep up with the growing heterogeneity of layouts and build custom layouts with ease?Fortunately, we do have a successor to the old UICollectionViewFlowLayout, as introduced by Apple in WWDC 2019!Here’s a solution that moves away from the delegate approach and uses a much simpler closure-based approach.But before that, let’s try to wrap our heads around the UICollectionViewFlowLayout.What is UICollectionViewFlowLayout?Look at UICollectionView as the foundation of a lot of apps.The UICollectionView is used to arrange cells/ UI views in Scrollable Content inside it, and the given layout determines how the elements will be arranged. By default, the UICollectionView Flow Layout is either in horizontal or vertical scrolling, which is the most widely used layout.The UICollectionViewFlowLayout object organizes items from a simple layout into a grid, and for each section, it accounts for optional header and foot views. You are required to provide an estimated height and width for the cell, otherwise the cells will use auto-layout and the end result is not the best, in most cases.The biggest plus point of the UICollectionView over the UITableView is the capability of horizontal scrolling, as well how multiple cells can be arranged in a column, unlike UITableView.But, like we saw earlier, it comes with a catch: The UICollectionFlowLayout works quite well with simple layouts, but with designs becoming more heterogeneous, there needs to be a solution that supports building custom layouts — without the challenges that come with it.Boilerplate code and problems of self-sizing cells are just a couple of issues that make building complex and advanced designs an uphill task.UICollectionViewFlowLayout and its DrawbacksThe default layout for UICollectionView is sufficient for designing a simple layout, but it has the following drawbacks:The Layout Scroll can either be Horizontal or Vertical The UI Flow uses Delegates as a datasource, which is an outdated method of implementation It only provides animation for Inserting or Deleting a single item at a time The object has to be fetched using IndexPath, which can cause the application to crash in case of out-of-bound access The selected object has to be casted It is only suitable for simple UI Designs Switching layouts with animation is not possible Cells cannot encapsulate their layout variations programmatically One of the most pressing challenges of the Default UI Flow is that it is only limited to Continuous Scroll or Paging Scroll behaviour.In the UICollectionView, if you want to achieve paged central scrolling behaviour or scroll with cell hugging behaviour, a third-party custom layout has to be used. Either this, or you would have to implement your own solution, which, in most cases, might crash if not written properly.If you want to achieve Horizontal Scroll View within a Vertical Scroll View, there is a way to embed UICollectionView in UI Table Cell and use UITableView. We’ll learn exactly how a little further into the article, but before we get into that, let’s quickly refresh our knowledge of the main difference between UICollectionViewFlowLayout and UICollectionViewCompositionalLayout.What is the Difference Between UICollectionViewFlowLayout and UICollectionViewCompositionalLayout?The UICollectionViewCompositionalLayout is essentially a more advanced and enhanced version of its predecessor, the UICollectionViewFlowLayout.In a UICollectionViewCompositionalLayout, data is presented in a static manner in multiple rows arranged in a single group, up and down, and the sections are composed of single and multiple groups.Depending on the user interface you want to achieve, you can see for yourself and decide which layout of the two suits you more: for simple layouts, for example a list of items that don’t need any designs, a UITableView would work just fine.However, if you’re looking for more ability to customize within each cell, the UICollectionView is what you should use.To put it in the simplest way possible, the UICollectionView organizes the collection of data/ views, presenting it in layouts that come with the customizability that is missing in UITableView.In the UICollectionViewFlowLayout, embedding the UICollectionView in the UI Table Cell and using the UITableView as the top most view will help achieve the Horizontal Scroll View within a Vertical Scroll View.And while this may sound easy to accomplish, there are certain things you must look out for when doing so:You have to conform to Delegates for both UITableView and the UICollectionView, which will make the code very messy It is memory-intensive and might cause memory leaks It’s hard to contain the complete code in a Controller The MVVM paradigm may need to be modified Gladly, during WWDC 2019, Apple introduced compositional layouts with the goal to simplify the development process of complex layouts in our applications.Here’s where the solution from Apple comes in and saves the day. Meet the Compositional Layout.What is the UICollectionViewCompositionalLayout?During WWDC 2019, Apple introduced a new, more modern and advanced way to implement more complex layout in simpler ways.This was known as the Compositional Layout, which was released with the purpose of simplifying the development process of complex layouts in the UI for iOS applications.Compositional layouts are a type of collection view layout that are designed to be flexible, composable, and significantly faster so as to allow you to build whatever visual arrangement you want.It lets you combine, or composite, each of the smaller components of the design into a full layout.The Compositional Layout comprises one or more sections that compartmentalize your layout into distinct visual groups. Each section is composed of groups of individual items, the smallest unit of data you want to present. A group might lay out its items in a horizontal row, a vertical column, or a custom arrangement.How Compositional View Solves the Challenges of the Default UI FlowThe Compositional Layout for iOS design comes as a solution owing to the multitude of advantages that it boasts over the older UICollectionView for iOS layoutIt is declarative in nature It supports both Delegates (older approach) as well as the new Diffable Datasource New Complex animations come out of the box using Diffable Datasource Snapshots Item can safely be retrieved directly for diffable datasource and doesn't need to be casted Layout variations can be encapsulated within the cell programmatically Easier on device hardware performance and supported by Apple itself Support layout switch with animations TakeawaySome of the biggest enhancements that the Compositional View offers for frontend UI development, particularly to overcome the challenges of UICollectionView for iOS, include:It supports both Horizontal as well as a Vertical scroll at the same time It achieves this by adding new Groups in each section, making code much more readable, easier to understand, and cleaner It supports 6 types of scrolling behaviours out of box, including Central Scroll (suitable for Carousel View). Considering the importance of breaking down the frontend UI development process to make it simpler, more efficient, and most importantly, flexible and adaptive to increasingly advanced iOS designs, there remains no doubt that the Compositional View offers benefits that can go a long way to improve life for frontend developers. Want to learn more about MicroFrontends and Microservices? Read this article here!
How Compositional Layouts for iOS Design Revolutionized Frontend Development
What do small businesses with small teams need during their product’s life cycle?Scaling their teams and operations is undoubtedly one of the biggest aims for startups. But scaling using the legacy Agile Scrum frameworks that these small-sized teams are so used to can make scaling an uphill battle.When it comes to a small business scaling up, the Agile Scrum framework will fail as the business endeavors to add more resources to the team. The Agile Scrum framework is designed for small teams, and a lot of startups end up making the mistake of adding more resources while using the same Scrum framework, resulting in agility loss.What, then, can startups do to bring about agile and digital transformation without slowing down their operations?How Do You Transition From Company to Agile?Often at the growth stage, one of the biggest problems teams run into is organizing and structuring their processes. This leads to a number of problems that end up significantly slowing a team down.As they consider business agility transformation, it is crucial for product owners and managers to try out different agile frameworks meant for larger teams so they can ensure the same agility when they are scaling.Scaled Agile frameworks aim to solve these structuring and organization problems by introducing processes meant for multiple small teams working towards a common goal. This helps teams manage their backlog and also stay agile enough to adapt to changing customer needs.At times, however, in hopes to adopt agile transformation, teams slow themselves down by jumping on one scaled agile framework without knowing what works for their team and what doesn’t. This leads to a lot of frustration that, in my opinion, can easily be avoided.If you’re a small business scaling up that is used to the legacy Scrum framework then what exactly are the best options out there for agile and digital transformation?The answer to this question largely depends on what works best for your team, but it helps to look at the best, most popular, and well-reviewed Scaled Agile frameworks.In this article, let’s explore our top three popular frameworks and what teams should focus on while scaling Agile transformation.3 Popular Scaled Agile Frameworks For Digital TransformationSAFe FrameworkThe Scaled Agile Framework® (SAFe®) is a framework for implementing AGILE operations at an enterprise level.SAFe lays out a set of organizational and workflow patterns that are needed to implement agile practices at an enterprise scale. The SAFe framework offers structured guidelines on how to plan and manage work, the roles and responsibilities of everyone involved, and the values that must be upheld.The framework is based on creating structure, roles, and processes around multiple scrum teams working together. These teams are called Agile Release Trains (ART) and come with extra roles and responsibilities that need to be filled. The teams coordinate on delegating epics, features, and user stories among themselves and complete work in time-boxed sprints along with all required sprint ceremonies. These sprint ceremonies are quite the same as the legacy scrum framework e.g. Sprint Planning, Retrospective, Backlog Grooming, etc. Teams are also free to use either Kanban or Scrum to finish work in their sprints and every team maintains its own backlog of work.SAFe is also the most widely used framework for big corporations' developing and shipping software.If you are looking to scale your organizational processes and structure, SAFe really is the best option. It doesn’t only focus on building multiple teams working on a single product, but rather on building the key organizational principles, values, and roles that help companies scale and build lean product portfolios. Read this article to learn how to implement SAFe as a Scalable Agile Framework.LeSS Agile FrameworkAnother popular framework is LeSS, also known as Large Scale Scrum, which follows the “do more with less” philosophy.The LeSS framework focuses on scaling existing scrum procedures, always starting with one small scrum team and then scaling to multiple teams working on the same product backlog.Less is considered much more lightweight than SAFe because it comes with fewer roles, which makes it a much easier process to adopt for agile transformation than SAFe.The LeSS framework focuses on legacy scrum practices but applies it to multiple teams working on the same product.Unlike SAFe, in LeSS there is a singular product backlog rather than a team backlog and just one Product Owner. All teams plan and pick their items in Sprint Planning 1 where work is presented to the dev teams. This is followed by Sprint Planning 2, where dev teams discuss development strategies for features and integration of their development into a single product during release time.Contrary to SAFe, in the LeSS Agile framework, inter-team coordination is also a responsibility of the teams rather than a role dedicated solely to it. This framework is often referred to as barely sufficient by some while it works great for some teams.LeSS really is a great choice when it comes to affordability due to its lower implementation cost. For some teams, a single Product Owner really proves to be an efficient link between business and tech because they understand the framework, challenges, and product vision and strategy.Scrum of ScrumsScrum of Scrums is a framework that focuses on divide and conquer.It helps organizations take a large workforce and create teams of four to five people working towards a common goal. The framework believes in smaller teams as it helps to build practical and personal relationships, which in turn results in velocity.Scrum of Scrums also has separate product owners for each team and the ceremonies are exactly the same as Scrum. The framework also comes with extra roles to ensure coordination between different teams. These roles are Chief Product Owner and Scrum of Scrum Master, who solely works on coordination in daily 15 minutes scrum meetings.The Scrum of Scrums framework is quite lean but does not undermine the importance of coordination roles.The framework is also quite flexible, allowing you to quickly discuss and switch up processes for greater efficiency. The coordination roles constantly discuss impediments daily and try to resolve and change processes, which allows for open discussion on problems and their solutions, unlike in other frameworks.Which Agile Frameworks Are Right For My Team?There really is no one size fits all solution here: what works for one company will not work for other companies, simply because of different cultures and mindsets.Usually, a lot of research, trial, and testing is required to create and pick the processes that work for you. Sticking with one framework religiously will slow you down. These frameworks focus on delivery & outcomes, which is great for larger enterprises and teams and help establish predictable outcomes and clear goals.However, for small businesses scaling up, the recipe for success is delivering value to customers as fast as possible.Most popular frameworks also come with their own set of frustrations and problems, which people openly talk about.For example, we talked about the SAFe framework and how it works but one of the biggest problems that people report with the framework is the unnecessary roles and complex processes. People who have worked in this framework often report that it is a waterfall method in disguise and the delivery of actual work and value to customers is quite low. The jargon and processes are so complex that teams spend more time organizing themselves rather than actually working, let alone being able to work with agility.On the contrary, the LeSS framework is regarded as barely sufficient as it is a simple and lean framework and relies on fewer roles. If the team dev leads are not used to communicating and coordinating, then the absence of a team's product owner can be very difficult on everyone.LeSS is also quite notorious for not solving developer problems as it often is not the focus of the single product owner; sometimes product owners are just swamped with a lot of challenges that demand their attention, and the developer challenges are neglected.Scrum of Scrums focuses on building smaller teams but it often leads to an imbalance of resources. Finding the perfect performing squads can also be quite a hit-and-miss.This just proves that no digital transformation framework alone is perfect and in order to achieve efficiency, teams need to invest time into discussion and trial.What are the Steps to Scale a Small Business?For all the challenges that come with the aforementioned Scaled Agile Frameworks, what exactly should then growing startups focus on instead of jumping to adopt a Scaled Agile framework?I really believe that before scaling, product teams really need to look at themselves and ask whether they are agile enough to scale.Scaling with what is right for your squadWorking on your single scrum team is very important as the first team really is a brand ambassador of your values and culture.Agility is a mindset and your team is like a car engine. Every person is a crucial component of this engine and to perform well, the team needs to have the right mindset. After that, the team will scale efficiently no matter what.It is also quite important to develop practices that help to build the foundational work ethic for your squad.For example, how efficient are devs in communicating on tickets? Does your team document a lot of stuff? What onboarding processes do you have for new team members? These processes are important baselines for establishing success in adopting frameworks.Every team has its own pace and comfort in which they perform best, so it is important for teams to continuously discuss and monitor their performance to make adjustments. Continuous discussion around process improvement is key and all stakeholders should be able to voice their opinions.If you’re still not sure if you’re ready for Agile transformation with Scaled Agile Frameworks, use this Agile Culture Checklist to keep your team grounded and efficient as you scale up.Agile Culture Checklist1. Customer/User obsessionWe’re all here for our users and customers, developing products for them.Successful companies are always obsessed with making their customer/user experience the best there can be.Customer obsession is a valuable principle that is quite common in all frameworks and it helps squads develop better features, which in turn helps the product grow.2. Looking out for each otherA great agile squad is like a well-oiled engine and in order to keep this engine running smoothly, one thing teams need to do is always look out for each other.Building personal relationships within your squad will help keep your velocity always high.A great example of this is sick or parental leaves. Whenever a squad member has to take time off work, unprepared teams will struggle.However, a team with the right agile mindset will share the work and responsibility of the team member not present. This helps the team to always move forward and take charge when they need to.3. Always flexibleFlexibility is one of the core principles agile teams need to practice.Today’s markets have gotten increasingly competitive and startups always need to be delivering value fast, as changing market scenarios can make or break businesses.Teams who are flexible constantly innovate and stay ahead of the curve by adapting to any situation they are faced with.I believe teams that fully practice these principles should consider adopting frameworks while always staying true to these principles.All said and done, let's see how mixing and matching processes from different frameworks can really be the best way to scale for companies.Takeaway: Mix, Match, and Perfect!There is no hard and fast rule to adopt a single framework and stick to it. Studying the different processes and roles within different agile and digital frameworks can give you the best insights into what can work for you.If you’re a small business looking to scale, you can start with small teams like in Scrum of Scrums as then the teams can focus on building better personal and practical relationships. Having product owners for every squad might not be feasible for you, so try splitting the responsibilities to two or three central product owners.There are several principles that are common within these frameworks, and hence mixing and matching processes works as many things are interchangeable.Teams can also experiment to empower their developers so they can also be better at managing work, allowing for less reliance on coordination roles as your tech leads will then be comfortable communicating with stakeholders.All these experiments and trials can happen within the sprint cycle and you can even monitor two squads simultaneously with different processes to see what is working better for you. You can also add or remove meetings you think the squads need. Creating a feedback channel for all processes and meetings will help validate your experiments.Finally, it really does not matter if you adopt a popular framework because, with the knowledge and resources available, teams can literally invent their own new frameworks.Using this approach of mixing and matching while staying true to your agile values is the foundation of success when scaling your operations, structures and processes. Agility is a mindset and that is what you should be preaching across your business. To learn more about how an Agile culture can be adopted across the organization, you’ll find a lot of value in this article that discusses the role of People in Agile.
Scaling Agile Operations: 3 Agile Frameworks for Digital Transformation
In the product development life cycle, user experience research plays a significant role.But knowing how and when it should be implemented for the best results in any new product development process is a very critical piece of the puzzle.How Does UX Research Fit into the Development of a Product?User experience is central to every brand.Even more so than your product itself, the experience that your users have at every touchpoint and interaction will define whether or not you can not only capture their attention but also convert and retain them as happy customers.To deliver the highest level of customer satisfaction, it’s vital to know who your customers are, what are their preferences, their wants and needs, and what could potentially convert them (or drive them away). This is where the UX research process kicks in.When Should User Research Be Included in Product Development?Ideally, user experience research (or UX research) should be utilized very early on in product development.With that said, it can also be useful at later stages of the project and for upcoming releases.To best understand the true impact of user experience research in the product life cycle, it helps to consider each of the four stages of the product life cycle separately and learn the exact UX research methods that can be used at each stage, along with the outcome that the UX researcher will achieve by implementing these methods.What are the Stages of a New Product Development Process?Whenever you’re faced with any new product development process, it will comprise four main stages:Research and Discover Ideate and Design Build and Test Deploy and Measure At each stage of the process, it’s important to really focus on your end users and adopt a customer-centric approach to product development. Here are 4 Customer Research Methods for Customer-Focused Product Development.We will be looking at each stage of the product life cycle in terms of how UX contributes towards them and what methods should be used to achieve certain outcomes at each of these stages, both pre and post-release of your product.UX Contribution in the Pre-Release StagesStage 1: Research and DiscoverResearch is always the first step in discovering and comprehending the End User.Whenever you’re looking at a new product development process, the first step is to establish a core team, learn and align on the company's objectives, and always empathize with the customers. This will assist teams in comprehending the customer's perspective, pain points, and actions.The following are a few approaches taken by professionals in user experience design to help businesses avoid mistakes in the early stages of the product life cycle:UX research This includes things like in-depth interviews, surveys, focus groups, and field studies. After conducting research, designers create personas, experience maps, user scenarios, etc. through a data modeling approach. These procedures can aid businesses in better comprehending their target audience of users and the issues they face. This will make it simple to address them in the final product.Workshops for brainstorming The attendees of these workshops come from various departments.The confrontation of various points of view encourages discussion and provides the ideal setting for challenging existing concepts and coming up with new ones. This, in turn, may help in aligning everyone on the vision for the project.Competitive research When you examine the products of your rivals in terms of user experience, you could seek out potential areas for enhancement. Stakeholders will be able to gain an advantage in solving customers' problems and ensure that their product will not be a duplicate of an existing app by conducting competitive research in the product design process.Stage 2: Ideate and DesignIn this stage, the entire product team works together to make sure the product is of the highest possible quality at the best possible time for marketing.Designs are developed that represent a solution that UX researchers and teams have arrived at after brainstorming and ideation.Some of the practices that UX designers can follow to help deliver the highest quality of experience include:Map out the user flows and information architecture It is necessary to develop a well-thought-out structure, navigation, and user flows (a user's path to completing a task). By doing this in the product design process, designers can ensure that end users can easily navigate the features and contents of the product.Create wireframes Before any visual design is applied, the interface of the app is laid out in wireframes as a skeleton. Wireframes are a quick way for UX designers to convey their thoughts and ideas in the form of an interface because they require less time to produce (especially low-fidelity sketch versions).Create high-fidelity and test prototypes Additionally, wireframes can be transformed into functional prototypes, allowing designers to evaluate their concepts prior to beginning UI design work. By doing so, businesses can avoid wasting time on bad ideas, and designers can begin working on high-fidelity designs as soon as the results of the prototypes are clear.In the next step, we’ll come to actually delivering the product to your customers after the strategy has been established and everyone has a clear understanding of what the product will be and who it will serve.Stage 3: Build and TestAt this stage, designers are expected to collaborate closely with the development team to release a customer-focused solution that drives value and creates an unforgettable experience.This is when the high-fidelity designs are in place and the designers are prepared to “hand off”:Handoff between Designer and Developer! The Developer, Designer & Tester huddle together where the designer walks everyone through the Design Features and Their Planned Functionalities from the Element Level to the Component Level, describing Their Expected Behavior.Sharing the designs through UI slicings is one of the suggested practices to accomplish this, which essentially entails annotating each component and element to convey it smoothly to other stakeholders involved.For more insights into how Designers and Developers collaborate, check out our latest Ask A Designer segment, where developers question designers on things they’ve always wanted to know!UX Contribution in the Post-Release StagesStage 4: Deploy and MeasureDuring this phase, the entire team works together to implement a user-focused solution where they learn, iterate and deploy in a sprint cycle to achieve a customer-centric product that delivers value and satisfaction.After the product has been introduced to the market is when you come to the final stage of the product development life cycle, where you will Observe & Validate whether the product is a good fit for the market.The following are some of the actions that can be taken to enhance the user experience of a product:Perform a UX audit Performing a product audit can prove to be the best course of action for a business that wants to go a step further to enhance their product experience. By doing this, designers can offer advice on a product's accessibility and usability issues, which could boost conversion rates and other crucial business metrics.Build new features with feedback from actual users Another great part at this stage is that you can use the information and data you have gathered about a product's performance whenever you want to improve on it to meet your users’ needs.Even if a product is just an MVP, it may be easier to identify usability issues. You can get real-world feedback on your product through user testing, which is sometimes impossible to get through even past mockup testing.Additionally, you can also gain insights that will assist you in designing additional product features.TakeawayUser experience research can be advantageous for businesses of all sizes, digital and physical products, and at all phases of a product life cycle.The earlier in the product development life cycle you can introduce UX research, the more benefits you will reap.“UX comes into play as a Prerequisite & Not as a Peripheral when you speak about Focusing on End Users!”With that said, this action can always be taken at any time; it’s never too late!If you’re still considering the value of incorporating the expertise of a design specialist for your next product development process, here’s a detailed introduction to Enhancing the Product Life Cycle With Great User Experience.
Implementing User Experience Research in the Product Life Cycle
mobileLIVE is proud to celebrate another solid win this year! Our company has been officially included in the 2022 Best Workplaces in Ontario. mobileLIVE received this honor after a thorough and independent analysis conducted by Great Place to Work®.The list is based on direct feedback from employees of the hundreds of organizations that were surveyed by Great Place to Work®. To be eligible for this list, organizations must be Great Place to Work-Certified™ and have exceptionally high scores from employees on the Trust Index survey.mobileLIVE is humbled and honored to have scored high on the Trust Index. This score proves our approach is in the right direction: empowering employees, valuing diversity, and nurturing innovation throughout our workforce. At mobileLIVE, our people are the real drivers behind success, and this is a win for each of our valued employees.About Great Place to Work® Great Place to Work® is the global authority on high-trust, high-performance workplace cultures. A global research and consulting firm, Great Place to Work® provides the benchmarks and expertise needed to create, sustain, and recognize outstanding workplace cultures. In Canada, Great Place to Work® produces both industry and demographic-specific Best Workplace™ lists and represents the voices of 500,000 employees across industries. This is part of the world’s largest annual workplace study, recognizing the world’s Best Workplaces in a series of national lists including those published by The Globe and Mail (Canada) and Fortune Magazine (USA). Visit us at www.greatplacetowork.ca
mobileLIVE recognized among the Best Workplaces in Ontario® 2022
A big part of any organization’s growth now relies on providing a great user experience.And hence, some of the biggest product development companies now focus a great deal on user experience design. In the product life cycle, UX design provides multiple benefits, making it easy to see why and how good user experience design should be an integral part of the process.What is a Product Life Cycle?The product life cycle refers to the set of strategies that are used at different stages during the product’s development for purposes of sales and marketing.If we look at the product life cycle through an abstract lens, we find five distinct stages in the product life cycle:Conceptualization and Technology Development Product Development Market Introduction Market Acceptance Growth Normal Market Cycle Apart from this, the product life cycle can also otherwise be defined as amount of time that a product spends from being introduced into the market until the time it is taken off the shelves.The product life cycle assumes that most products have different life spans, and therefore, at each stage of the cycle, the business faces a different set of advantages and disadvantages.This is why, when working on any product, it’s crucial to be a critical thinker to identify all edge cases that you could face during future processes of new product development.There needs to be a lot of focus on the current and future consequences of the product, and to overcome such situations, businesses can adopt different processes and strategies to approach design, development, and marketing.What is User Experience Design?User experience design (or UX design) is simply the experience of your customers during their interaction with your product or service. How easily users can navigate through this experience and how simple (or complicated) it is for them to achieve what they want is all a part of user experience design.The user experience can make or break your customer experience — and hence be a deciding factor when the question arises of whether your customer will want to stick with your product or move on to the next best option.So, when it comes to achieving great user experience design, it's important to make this consideration throughout the product life cycle to improve product growth and increase revenue streams.How the Best Design Performers Increase Revenues in the Product Life CycleIf we refer to extensive research from McKinsey on the subject, companies with top-quartile design scores outperformed the industry-benchmark growth by a ratio of as much as two to one.You will be amazed after looking at the graph below about how shareholders got the revenue after implementing the design into their businesses.(Image reference taken from McKinsey)Why is UI/UX Design Important in the Product Life Cycle?The potential for design-driven growth is enormous in both product and service-based organizations.Customers can feed opinions back to the companies in real-time, allowing the design to be measured by customers themselves — whether or not companies want to listen.(Image reference taken from McKinsey)Although UX requires a lot of work and time, there are several techniques that play an important role in the product life cycle.Without UX, most teams end up facing several issues and difficulties after the product is released. This is where UX comes in, helping with not only avoiding any issues but also ensuring you identify them during the product life cycle and not after release.What Does the User Experience Design Thinking Process Look Like?As UX affects every aspect of the product being built, the UX or Experience Designer is involved throughout the product life cycle.Essentially, the UX designer designs the end-to-end experience of the product, which includes the complete design thinking process depending on the nature of the product.The design thinking process always varies from product to product. Your process could change if you are working on an existing product and you want to add some new features.The UX designer’s task is to be aware of all the problems and pain points with viable solutions, and convey those results to any and every element with which it is associated within the product life cycle.Working as a UX designer is really important in the product lifecycle to understand why we need to define the problem, discover pain points, ask important questions like the whys and whats, and conduct user research before getting into the design phase.Sometimes, there might also be the need to convince other stakeholders about why conducting user research is important for your product. The trickiest part is how you do it; there could be a lot of challenges such as:How to discover and understand the actual problem statement? How can we manage time constraints? What if people find the experience difficult or useless? What will the consequences be?User experience design starts by conducting user research. Moderated and unmoderated usability tests are conducted to validate design assumptions specified in the requirements defined at project kick off.User research helps the team identify their target audience and create a user persona to represent their users in order to validate design decisions throughout the product life cycle.Next, they design the user journeys in order to gather feedback and optimize their designs around that feedback. They keep refining their designs even after product delivery to make sure that the product is user-friendly.How To Practice Good User Experience Design in the Product Life CycleGood user experience design means that from UX research to launch and till the time the product life cycle ends, the customer is kept at the center and considered at every stage for the product design.If we consider the problem above, user experience design contributes largely towards overcoming it.UX research helps in identifying the pain points earlier so that you can solve any problem you may run into.In turn, designers can start at the beginning of the product life cycle with research and can focus on the end user of the product. It helps designers to reveal customer issues and narrow down on how the product solves them.By conducting UX research with your target audience through a set of interviews and more, you can design user personas, journey maps, user scenarios, prototypes, and simplify the overall user experience process. Here’s another article that will give you a clearer understanding of Why Ignoring UX Research Is A Mistake.Good user experience design plays an important role in the product life cycle when designers approach it with the actual pain points of the users, support it with user research, brainstorm the ideas together as a team, and create seamless flows.Takeaway: Key Benefits of Integrating UX in Product Life cycleIncorporating user experience design into your process is always beneficial, whether you're launching a startup, developing a product, or questioning the performance of an existing one.In most cases, the sooner you incorporate the expertise of a design specialist into your project, the better your chances will be of achieving effective and agile UX product development.“UX comes into play as a Prerequisite & Not as a Peripheral when you speak about Focusing on End Users!”Using the right UX methods and principles at all stages of a product's life cycle can reap many benefits for product development companies. Some of the most valuable benefits evident to us across the product development life cycle include:UX research helps stakeholders to understand their target audience and problems, making it easier to address them in the finished product. User experience design allows much better work engagement and lets product managers plan ahead of time. The UX designer gets a better perspective of how the product is, enabling them to stay consistent across the product features. Implementing the UX process into products increases the sales, user engagement, and adoption rates. It also increases the customer’s emotional bond to a company or a product. It will always be beneficial for a product development company to bring in a UX expert into the product development life cycle so that UX design can be involved into the product process early on. Take your User Experience Design a step further by designing cross-channel experiences for your customers with these 9 UX Design Heuristics!
Enhancing the Product Life Cycle With Great User Experience Design
Designers and developers have very different roles, but in most cases, their paths cross while working on the same project.For example, if a website needs to be created, a designer is tasked with delivering the visual impact that the user interface will have on the viewers. On the other side, the developer will be managing all the backend capabilities and ensuring that the designs and wireframes are materialized.And while this shows us that the relationship between designers and developers is key, oftentimes their processes aren’t as collaborative as should be.So, in an effort to improve communication and bring down some of the barriers that hinder collaboration between design and development teams, in this Ask A Designer feature, we invited our developers to put our designers on the hot seat — asking them things that developers want to know about designers!Question 1How is your experience communicating with developers?Response 1:Most of the projects I work on will involve developers in some way – either in setting up a new page in Contentful, or creating/modifying components.— Véronique Janosy, Senior Product DesignerResponse 2:In my experience, collaboration, open-mindedness, inclusion, proactiveness, and a healthy relationship between design and development are key essential characteristics required to ensure that the design details are correctly translated into the code for a successful, visually appealing, and functional product development.For a pixel perfect design, every detail including measurements of canvas sizes, margins, paddings, hover and focus states, no-data or error states needs to be communicated or annotated to the development team for smooth coding.At the end of the day, both designers and developers are working towards achieving the same goal.— Rahul Jacob, Product DesignerResponse 3:Given that the design requires a collaborative environment within multidisciplinary teams, and in particular, an efficient communication between Product designers and Developers, it is always considered that communication is the key value in succeeding in any product’s project. In my collaborative design projects from the beginning, I always tended to build a good relationship with my team members, especially the developers, to improve the process of the design of the product. In my experiences throughout the years, I tried to learn the basic programming terms and on top of that learned the developers’ concerns regarding creating a new design to be able to provide feasible ideas that they could also be able to create.— Ella Rabiei, Senior Product DesignerResponse 4:Developers and I work closely with each other; often communicating on Slack or via daily standups and scrum meetings. We discuss ux issues such as error handlings or variants of a component like having different states of buttons and behaviors in the Design System and talk about problems and solutions. I deliver ux clarifications includings scenarios in the form of design patterns and guidelines and support developers where needed. It’s all about collaboration and teamwork.— Rebecca Kim, Lead Product DesignerQuestion 2When tasked with a new feature, do you research the feature(s) while keeping the developer and standard libraries like Angular / React Material, PrimeNG/PrimeReact, Angular/React Bootstrap, etc. in mind?Response 1:I don’t research features available in any libraries, but I always engage developers whenever developing something that I’m not certain about, since they are the experts in the field. A lot of actions and features we use have already been used on the site, so I don’t need to verify those, but I will set up a call or start a conversation about a feature if I’m not sure. I also want to make sure that when I write tech specs I include any information that will be essential for the dev team. After handing over a new or modified component spec sheet, there is always a walkthrough with developers, at which point we can discuss any questions or concerns that have not already been addressed prior.— Véronique Janosy, Senior Product DesignerResponse 2:When it comes to new features, I always take the less is more approach. As a product designer, I am always empathic with the users, as they rely on their products to be intuitive, functional, reliable, and usable for their needs to be met quickly and effortlessly.Any time I start working on a new feature, I use existing libraries as a starting point. It is always important for me to design components that are practical and long-lasting so that they can be reused or managed in the future. As a result, the design is easier to maintain on the backend while looking good on the frontend, which increases its longevity.It’s a win-win for everyone!— Rahul Jacob, Product DesignerResponse 3:Yes, but not the library source in particular. When it comes to design of a new feature, I would talk to the developers to learn about developing and coding restrictions and the alternatives that I can use of; there are always multiple new features that need to be created, and it would be hard for the designer to go through all the libraries in person to see if it would work for them, that is why the direct collaboration with developers can help the designer to process the feature and make the decisions correctly regarding that.— Ella Rabiei, Senior Product DesignerResponse 4:Yes, I do research the features, prioritizations, IA, design approaches, and page layouts focusing on synthesizing and communicating high-level strategic insights. Developers are highly-skilled and knowledgeable and may offer new and different perspectives that could unlock unrealized potential. For example I ask developers' thoughts how the sophisticated backend system works that is captured in information architecture and workflow in ux.With the collected information, I adopt user research methods and conduct Usability Testing, User Interviews, or Focus Group for ux findings. With the ux findings, I look into the Design System in terms of the UI component quality like a complex form and table, so it allows me to reduce the risk of any variation between products.— Rebecca Kim, Lead Product DesignerQuestion 3Is the web app multi-themed, responsive, and/or multilingual? How many layouts do you share with the developers, and what format (XD, Figma, etc.)?Response 1:The work I do is aimed at one theme (brand). All the components I build are designed to be responsive, and I provide mockups and tech specs for 4 breakpoints, which handles the majority of the scenarios. My client works with Sketch for design, and Abstract for version control, so I share out Abstract links to the developers, which allows them to inspect each component fully.— Véronique Janosy, Senior Product DesignerResponse 2:Each project I work on incorporates responsive design to adapt to the viewports of these devices: desktops, tablets, and mobile.I work primarily with an app called Abstract that lets me import Sketch files and create a centralized repository for the team's most up-to-date design work and supporting documentation. Developers can seamlessly transition from design to development with this app. It allows developers to compare changes, view measurements, specs, inspect, and download assets through a link or directly from the app.— Rahul Jacob, Product DesignerResponse 3:Yes, the process of the designing a product requires designers to create/build responsive designs which would follow the guidelines and rules; it is very important to evaluate and test the design on different platforms to see if the message of that specific product is clear without any distortion, failing to do so, would cause a confusion and difficulty to the developing team and as a result the product would fail. The number of layouts highly depends on the number of scenarios in a particular product project, in general we as designers would provide all the potential scenarios for developers. In each design team, there are a variety of applications that product teams could use to work together. In my current work, we use the Abstract and the Sketch apps to communicate our ideation and designs; there have been some situations where we had lost our access to either of these apps and I would provide developers with exported versions of the design, such as SVG, PDF and PNG to move forward with our process.— Ella Rabiei, Senior Product DesignerResponse 4:Yes or No, it depends on the context, constraints, business requirements, and user needs. If yes,define the layouts based on responsive breakpoints (Desktop, Tablet, Mobile) in Design System including with light and dark themes in Figma. share with the developers UI elements and functions including documentations around the look and usage of each component in the component library. support multilingual languages. For example, when translating English into French or Spanish, the translation will generally be about 15 % to 30 % longer than the original. I will make sure if it suits the copy in different languages. If the translations break the design layout, that requires discussions to come up with solutions such as adjusting font sizes, text truncate ellipsis, or alignment and text placement. — Rebecca Kim, Lead Product DesignerQuestion 4Do you keep accessibility in mind before designing features?Response 1:Yes, it’s a must! I do think that more education is needed around accessibility, across other areas of the business. It does seem that while design and dev are aligned on the importance of accessibility, stakeholders need to be educated so that they value it as well. At the end of the day, with certain businesses, design can always be overridden by stakeholders.— Véronique Janosy, Senior Product DesignerResponse 2:I always incorporate accessibility into my design process so that every user has access to the same information, regardless of their visual, auditory, cognitive, or motor impairments. As a result of keeping accessibility in mind, it expands the potential audience, improves usability, and meets accessibility standards. “Always build with accessibility in mind.”— Rahul Jacob, Product DesignerResponse 3:Yes! Absolutely. The accessibility is one of the most important features in product design and it is considered as one of the main reasons why a product would reach the public approval or it would easily fail.— Ella Rabiei, Senior Product DesignerResponse 4:Yes, I keep accessibility considerations in mind before designing and while designing features. The accessibility considerations are:Design simple experiences Interaction models (eg. is the functionality a link or a button) Color contrast and complementary colors and visual cues Create distinct and recognizable UI elements with familiar design patterns Work with content team and developers to communicate my design decisions and intent Test with real people early and often — Rebecca Kim, Lead Product DesignerQuestion 5What is your approach in dealing with accessibility?Response 1:Luckily, in design system work, our base components, colours, typography and interactions have already passed accessibility tests and been approved by our Accessibility team lead, so design needs to focus mostly on experiences and making sure those meet accessibility standards. By reusing existing components and sticking to the design system as much as possible, you can ensure a good base.— Véronique Janosy, Senior Product DesignerResponse 2:Often, accessibility is misunderstood as being primarily for those with disabilities. However, this is not the case. To benefit all users, designers must approach all projects with an accessibility-first mindset.Here are a few key areas to consider before designing your next feature or project:Color Selection and contrast Typography, font size and spacing Page structure, layout, and hierarchy Focus states, hover states and indicators Use of labels or instructions with form fields and inputs Descriptive text alternatives for images and videos to understand context — Rahul Jacob, Product DesignerResponse 3:If I would be asked to work on a completely new product without any developed design system, I would personally check the colors, typographies, layouts, and different component dimensions on different platforms by using accessibility tests tools and other design methodologies, such as observation method and secondary research method to create a feasible product for the product’s intended customers. In my current design team, we work with a design system platform which has already been developed and is also going through the accessibility checking processes over and over to make sure all the components are following the most recent guidelines and could result in products’ success.— Ella Rabiei, Senior Product DesignerResponse 4:Accessibility should be considered before the project starts. When I do my design research, here are some practices I use:consider accessibility, annotate, and document my decisions for an inclusive experience. Work with developers to refine decisions further. Ask myself: Are the needs of people with disabilities being met in the business requirements? Have I annotated my designs to clearly communicate to developers how a design is to be built? — Rebecca Kim, Lead Product DesignerSpecial thanks to developers Kashif Ullah, Waqas Niazi, Shahid Ullah Khan, and Talha Gillani for participating with these questions to ask our design team.TakeawayIn an age where so much relies on the interconnectivity of multiple functions working in unison to ultimately create the best user experiences, smooth communication and collaboration proves to be a valuable asset.Recognizing the importance of bridging the gap in communication between designers and developers, we brought both together and let the conversation flow, not only till our developers got all the answers they wanted, but going beyond and deepening this vital relationship!
Ask A Designer Round 2: Questions For Designers, From Developers
If there’s one big transformation we’ve witnessed with the onset of the digital era, it’s that user experience design has come a long way.The late 80s and early 90s saw a much-needed shift from the traditional way of designing objects to the idea of interfaces as design artifacts, accredited to acclaimed designer Gui Bonsiepe (1995). One of the first to see interfaces as a communication object, Bonsiepe saw interfaces as a “bridge” between humans, the product, and the objective.Ten years later, with faster microcomputers integrated into cell phones, cars, cameras, houses, appliances, etc., capable of communicating with other devices through not just one touchpoint of the customer journey but diverse technological possibilities of connections, it became clear that the “bridge” in user experience design goes far beyond interfaces.What are Cross-Channel Journeys?Cross-channel journeys refer to the experience a customer has on not just a single customer touchpoint but across a combination of multiple different channels.The human-computer interaction evolved into human-information interaction, and multi-channel interactions are increasingly transforming into a cross-channel customer journey.In the cross-channel customer experience, the user experience as a narrative journey involves actions in the physical and digital worlds pervading through many different digital devices, all linked together by a dynamic system ecology.In the digital customer journey experience, these connections, across multiple touchpoints in the customer journey, bring together short narratives into one whole story.What is Cross-Channel Journey Optimization?Cross-channel journey optimization is where the customer journey map is integrated with various techniques and tactics to cater to the customer via every channel on which they’re present.Usually, usability evaluations concentrate on the experience of users interacting with a single system.However, the whole experience, especially now in today’s digital world, trespasses the system and includes moments that are both precedent and subsequent to the direct interaction with the system.To truly optimize this cross-channel journey, user experience research and design will determine the quality of the customer experience you can offer to your users.What is Cross-Channel User Experience (UX) Design?From a UX design perspective, products and services must go beyond the good usability of systems and understand the users’ complete journey.Storyboarding is one method in UX design that helps UX teams align on the vision for the design they work on, with the single end goal of mapping out the user journey. To learn all about the What, When, Why, and How of Storyboarding in UX Design, read this article here.Businesses must understand the whole journey to plan customer touchpoints and create a service/product experience that is available on various different channels — a pervasive cross-channel experience.To achieve this cross-channel customer experience, user experience research plays a critical role in the UX design process.How to Conduct UX/UI Research and Why It Matters?UI/UX research is conducted through a variety of both quantitative and qualitative user experience research methods.When it comes to digital customer journeys, user experience research is one of the most integral parts of the UX process because it helps extract critical insights from early adopters. Understanding how users will perceive your product/service can be an aid when it comes to decreasing the learning curve for users and identifying opportunities for improvement. Learn more about why User Experience Research matters and what it can do for you here.Why UX Research is Essential To Plan Better Cross-Channel ExperiencesDifferent from a multi-channel experience, where users can connect to a product/service through different channels, the cross-channel experience will pervasively cross through channels and physical ambiance to create one experience.Connecting the dotsCross-channel contexts take the user experience to new amplitudes, and it is imperative to adapt usability principles to a narrative experience scenario, as the technological evolution and users’ interaction with devices and environment have been gradually changing and are expected to evolve to an even more integrated cross-channel experience with the metaverse.Although usability tests and measurements focus primarily on interactions with isolated devices, several user experience research methods can surface information about users that help understand their mental model, interaction needs, and cultural-interaction references to better comprehend their journey of experience and map contexts that could take users to interact with your product. Through generative research methods, UX strategists can connect these dots, build experience mapping and prepare a project with multiple possibilities of interaction.Planning for a better user experienceThe journey of your user will start well before they connect with your product/service, with several contextual scenarios leading them to your product.Mapping possible precedent actions and contexts of use can help understand what triggers the full journey of users and direct your product/service to be there when they most need it.Vague assumptions based on intuition have little role in the digital industry and can lead you down the wrong path, wasting precious time and resources.This is where user experience research comes in as the key to gathering the knowledge necessary to project a cross-channel experience:Knowing who the audience is for your product. Many times, this varies far from the initial assumptions. Thinking outside digital devices. Interaction with isolated devices is a very narrow perspective of the full scenario. Understanding the context of use that led people to use your product. What are the precedents that ignite interest? Planning how your product can help and create value for users. The product needs to be an ally, not a difficult step to reach a goal. Continuing the UX journey beyond your product. Plan for a smooth transition and a positive impact on the customer. Users will reach your system through diverse devices, and so the interaction must be planned in a way that offers a smooth journey with many touch points, blending the spaces in one full experience.Understanding this full experience will enable a better connection with not just service design strategies, but business strategies as well.UX Design Heuristics: Guidelines for Cross-Channel Customer ExperienceHeuristic evaluation in UX has always been centered around the user; how easy and user-friendly your interface is for the target customer contributes largely toward customer satisfaction, and even more so in the presence of cross-channel experiences.Therefore, the following set of UX heuristics (or guidelines) aims to help guide products for a better integrated, UX-pervasive experience. These are guidelines that can, and should, be applied to any service system that intends to provide an experience with interoperability between devices and systems:1. Place-makingPlace-making refers to the self-localization of users when using the system during their customer experience journey. Visual interaction, hierarchical layout, and structure, as well as physical environment, should facilitate the users’ understanding of where they are in the journey and where they can go from there.2. ConsistencyThe system must present visual, typographic, information, action, and interaction consistency. If a user utilizes different devices to execute partial actions of the whole experience, each touchpoint access must present the same rules and responses to the actions.3. ResilienceThe flexibility of the interaction flow and touchpoints should be adequate for different users and different journey and search strategies from different contexts of use.4. ReductionEven if the back end of the system is complex in its structure, the options and the contents must be presented to the users in an objective way and with simple usage, providing reduced interactive actions and minimum cognitive workload in their journey.5. CorrelationThe system must help users find information and content naturally. There must be a correlation of data between distinct points of interaction and devices. When users move from one device to another, the interoperability of information must be in unison.6. Equivalency to cultural conventionsIt is important to understand users’ references regarding technology, processes, functionality comprehension, and interactions — all of these can be used as a base in the development of a new system.7. Visual intuitive contentUsers must recognize functionalities, steps, hierarchy, pathways, and information with minimal memory load – this means making objects, actions, and options easy to recognize and understand by the user.8. Natural, intuitive, and direct interactionsAny touch point of interaction with the system should be as intuitive as possible, by direct gestural manipulation or simple vocal commands.9. Contextual ergonomicsPhysical environments, contexts of use within the journey of experience, and human physical limitations should be considered while projecting touchpoints of interaction with the system.TakeawayUndoubtedly, the digital landscape has evolved to a point where we can all safely agree that user experience has taken center stage in nearly every industry.But does the evolution of digital experiences stop here?From what we can witness today, business interactions in the digital world are constantly evolving, pushing the boundaries of user experience design.The onus is on UX researchers and strategists to respond and adapt well to these changes, integrating user experience research methods and journey optimization techniques to offer a complete cross-channel experience — meeting the customer where they are.
9 UX Heuristics For Cross-Channel User Experience Design
Across any industry in today’s digital age, what is the single most important differentiator that sets apart market leaders from other businesses?Many will answer that with “customer experience”. And they would be right.Experience has always been the key differentiator in identifying the right market leader in any industry. An enhanced customer experience has a proven potential of a 200% increase in conversion rate for the business. Concurrently, 89% of customers switch to a rival business as a result of a poor experience with a brand.But how do enterprises account for the customer experience in the product development cycle?Before we dive into that, it’s important to know what stages constitute the entire Product Development Cycle.What are the Stages of the Product Development Cycle?From ideation to commercialization, the product development cycle goes through multiple stages as the product matures. Broadly, the product development cycle constitutes 5 major stages:Ideation Research Planning Prototyping Release In this article, we aim to understand how a customer-focused product development approach can be integrated at each stage of the cycle, what customer data analytics need to be tracked, and what research techniques can be adopted to gather these customer insights.What is Customer-Focused Product Development?In the product life cycle, there’s a common perception that as soon as a product is launched, the product development cycle is concluded.But the lifecycle of product development doesn’t culminate with the release.Instead, there's a lot of work that needs to be conducted in the way of customer journey research to ensure that a customer-focused strategy is truly implemented in the process.What Factors are Important in Customer-focused Product Development?“Research is creating new knowledge.” - Neil ArmstrongCustomer wants and needs are prone to constantly changing as the market gains maturity.A great product is one that continues to evolve as it matures with the customer at the center.To implement a customer-focused product development approach, the co-existence of product delivery and customer research is vital — something that should be leveraged not just during but also after the shipment of the product or feature.Designing for the Right Reasons with Customer ResearchDuring a flight, before the plane takes off, an airline assistant always demonstrates the security protocols, even though every passenger receives a separate leaflet with the same details.This is a classic example of how observing an aware frequent flyer using the safety jacket reveals undocumented context.The same concept applies in product development that uses customer journey research to follow a truly customer-focused strategy.How Many Types of Consumer Research Are There?There are four different types of research that can be leveraged in the product development cycle.Each type of customer research comes with its own set of distinct benefits at specific stages of the cycle:1. Exploratory researchWith varying customer needs that evolve with each passing day, oftentimes product teams can end up with unique problems — as well as the demand to understand the problem first to decide on the best approach with which to move forward.Such situations require a very quick response and a clear segregation of what is known and what still needs to be learned.Why do it:Exploratory customer research helps product teams be aware and mindful of the challenges that a particular industry faces.Learning from both successful and failed launches can be identified early via exploratory research, which helps build empathy with clients and customers alike. With empathy at the core, a product can be designed well for an organization as well as customers.When to do it:Exploratory customer research is done at the very beginning, before the product life cycle is kicked off. This can be when there’s aNew domain (a new market/customer group sharing a common problem or need that needs to be addressed) New client (with unique business objectives along with new ideas to mature) Pre-sales initiative (by reaching out to an existing client base with ideas to implement with respect to emerging customer needs or technological advancements) Exploratory research may kick off the following activities:Industry benchmarking Competitive analysis Portfolio overview (Internal Competitive analysis) Evaluate across a 2×2 diagram to define maturity and usefulness Stakeholder interviews 2. Generative researchAn important contributor in the product life cycle, generative customer research paves the way for new ideas, new understandings, and new directions.For teams focusing on a single problem, particularly while designing UX, generative research helps explore many possible solutions and approaches. This mindset can lead product teams into many different directions, as it should. That being said, it is essential to have defined KPIs for success where each activity is tracked for its progress with clear success metrics.Why do it:Generative customer research helps strengthen relationships with clients by engaging in their core business problems.Two prime benefits include defining the success of the product and relationship building activities, as well as collaborating in upfront research.When to do it:Post major release Following a workshop It may kick off the following activities:Design sprint Suitable when there is flexibility in budget, scope, and timeline Often involves process changes outside the software Facilitated workshops Focussed collaborations involving the entire team Frequency should be once a month to collaborate on product direction and function Contextual inquiry Context is critical Requires a seasoned practitioner Ideally should be an environment where the product is going to be used/ or being used Output should be a well-defined service blueprint Card sorting Helps in discovering how people categorize information 3. Formative researchFormative research brings a bigger perspective to the table by analyzing both the qualitative and quantitative aspects together.Why do it:Formative research in UX helps evaluate the current state of the product, finding areas for improvement (e.g., building a new product or feature enhancement) and designing actionable output in the form of roadmap planning.Formative research can be carried out in the form of following activities:Heuristic evaluation: Expert reviews to check how the usability of an existing product holds up against a set of predetermined design principles. User interviews: Under expert guidance, you can conduct one-on-one user interviews to garner direct insights of the customer, highlighting the existing pain points or ideal journeys. Prototyping: An early sample, model, or release of a product built to test a concept or process, following the Rules of Prototyping: Build quickly, make many, and provide only essential details. Prototyping can be done in the form of Storyboards, Role-plays, Walk-Throughs, and Touchpoints. Some synthesis techniques for formative customer research can include:Personas Journey Maps Diagrams 2 x 2 Matrix Finally, the outcome analysis is done by asking six questions:Is it useful? Why/why not? Is it desirable? Why/why not? Is it easy to use? Why/why not? Is it functional? Why/why not? Is it sustainable? Why/why not? How does it compare to the alternatives? 4. Summative researchSummative customer research focuses on the evolution of the market and the respective product offerings. It helps identify failures at the early stages as well the adaptation measures needed, both with clients and users.Summative research comprises the following techniques:Usability testing Testing in development environments (anything before the production) Analytics review (to review metrics that can help in the evaluation of the forecast vs the performance of the product Out of all 4 of the customer research techniques discussed above, Formative and Summative research are the two types that are most crucial for designing a successful product from both a delivery and inception perspective — how the product works and whether it works as intended.With all said and done, for a deeper understanding of how user research contributes to the success of a product, read this article on Why Ignoring User Experience Research is a Mistake.Takeaway: Building Better Products with the Right Customer ResearchProduct experience plays a critical role in helping brands retain customers and build strong relationships, leading to a loyal customer base. Knowing how to incorporate customer feedback into the product development phases can be critical in helping product teams come together on the right unified goal of having a real impact in the life of their targeted customers.Being able to feel and realize this impact will prove integral to creating a better product experience for all stakeholders involved.To learn more about how you can take personalization a step further in your customer experience strategy, read this article on 6 Hyper-Personalization techniques that large enterprises can adopt for a much more enhanced customer experience!
Customer-Focused Product Development – with 4 Types of Customer Research
Toronto, ON—October 4th, 2022 – mobileLIVE, one of Canada’s fastest-growing IT services companies has been awarded the prestigious Great Place to Work – Canada® 2022 certification. This is the second consecutive year the company has been recognized for its people-driven culture.“Our second consecutive GPTW certification reflects our continued commitment to prioritizing employee physical and mental well-being, collaboration, and development. It is an honor to be recognized as a great workplace, by both the good people at GPTW as well as our talented and diverse workforce” said Jahan Ali, CEO & Founder.Each year, the Great Place to Work Institute® Canada conducts a thorough, independent, and extensive analysis, including anonymous surveys and employee feedback. The certification is awarded to Canadian businesses that are considered fair, unbiased, and inclusive among the majority of their workforce.Since its inception in 2010, the culture at mobileLIVE has nurtured talent and inclusivity across its workforce. With 400+ employees from varying ethnicities, cultures, and social backgrounds, the company continues to empower its workforce through empathy, collaboration, and development.About Great Place to Work®: Great Place to Work is the global authority on high-trust, high-performance workplace cultures.Through proprietary assessment tools, advisory services, and certification programs, Great Place to Work recognizes Canada’s Best Workplaces in a series of national lists including those published by The Globe & Mail (Canada) and Fortune magazine (USA). It offers benchmarks, frameworks, and expertise needed to create, sustain, and recognize outstanding workplace cultures. Visit at www.greatplacetowork.ca or find them on Twitter at @GPTW_Canada.
mobileLIVE gets its second consecutive Great Place To Work® certification
The buzz about data-driven product management is something we’re very familiar with.Long gone are the days when you could only rely on intuition — data is now everyone’s friend, and product managers have spent a considerable amount of time operating in a data-driven manner.This has also contributed towards the rise of big data analytics tools and other product data management systems and strategies.Having said that, while the importance of data and how it is used in product management is not lost on us, there is one big question to be answered:Where does this data come from?What Drives Product Data Management?What specific data do you need to drive your product?Product and data are both drivers in product management; we get data from the product and the same data helps product managers make the right decisions for that product.Understanding that the relation between product and data runs two ways is the first step in product data management.We know where data comes from and how it plays a key role for your product, but what are the most significant stages where data is the key for decision-making?Data-Important Stages in the Product Life CycleData plays a part at nearly every stage of product management.Specifically, there are three main stages in the product management life cycle where data should be central to making key decisions for the product:Acquisition Retention Expansion 1. AcquisitionAcquisition is one of the first few steps in product management.In order to achieve better acquisition for your product, it’s essential to track the right metrics.There are several acquisition metrics that are useful in data management for products, but some of the most important ones you’ll need are listed below:Customer Acquisition Cost (CAC)We are all aware that acquiring new customers is much more expensive than keeping the ones you already have.So, how much does it cost you to bring in a new customer? And what is a reasonable cost per new customer for your company?Calculating client acquisition cost, a crucial parameter that enables you to enhance your marketing initiatives and increase overall sales, will help you find the answers to these concerns.The formula to calculate this metric of customer acquisition data is as follows:(Sales Cost + Marketing Cost) / Total new customersCustomer Lifetime Value (CLTV)At times, a customer may stick around for a few years, buying more of your product until they no longer require it or they switch to a different business.How much revenue will a customer generate while they remain a client?The formula to find this metric is as follows:Average total sale x Sales over time x Average customer lifespanConversion RateNot every visitor to your business will make a purchase.They might look around your website or app, but they may not buy anything.You want to make it more likely for people to buy something, sign up for your mailing list, or do anything else you want them to do.The formula to use to gauge how many of your visitors actually convert into buyers is as follows:Number of conversions / Total visitors2. RetentionThe first step in developing long-term consumer loyalty is typically customer retention.Customer retention refers to the ability of a company to convert new consumers into recurring customers.Retention is critical to the success of your product; and to achieve it, cultivating loyalty amongst your consumers will always cost less than acquiring new customers.According to Harvard Business Review, acquiring new customers will cost your business 5 to 25 times more than retaining current customers.Therefore, for the best chance at achieving and improving retention, the top metrics you should consider are:Customer Retention RateThis is the easiest metric to use to gauge customer loyalty and the volume of recurring business you are bringing in.There are a number of additional indicators alongside customer retention rate that you can track to get a more comprehensive picture of how your product is doing.Customer retention is calculated for a specific period of time. Depending on your specific needs, it can be calculated for weeks, months, or years.It is usually reflected as a percentage, and the formula for calculating customer retention is as follows:(Customers at end of defined period - Total new customers during defined period) / Customers at start of defined period * 100Customer churnThe rate at which customers discontinue using your products or services is known as your customer churn rate.This can typically happen when a customer decides not to renew their subscription, stops doing business with you, or ends up doing something else instead of continuing with your product.Much like customer retention rate, customer churn is also calculated for a specific period of time, again, totally dependent on your needs.The formula for it is as follows:(Existing customers at start of defined period - Existing customers at end of defined period) / Existing customers at start of defined periodNet Promoter Score(NPS)A measure of customer satisfaction called Net Promoter Score (NPS) quantifies how probable it is for customers to recommend your product to others.This value shows how satisfied and devoted a customer is to your brand.Although a high NPS can't ensure customer growth and retention, it can help identify product advocates that are most likely to generate referrals.Low customer satisfaction is reflected in a low NPS, which may suggest that some sort of intervention is required to improve things. Here’s the formula for NPS:Percentage of Promoters - Percentage of Detractors3. ExpansionAs with any product manager, your aim will also be to keep clients loyal and coming back for more business throughout the growth stage of the customer lifecycle.To keep your customers satisfied and engaged with you — and in turn making them a profitable customer — you would upsell, cross-sell, and offer expert services, training, and whatever else you can think of.In an ideal world, you'd also like to see them become brand ambassadors for your product. If they are incredibly pleased with your product, they will assist in spreading the news and provide you with some priceless word-of-mouth advertising.Expansion is majorly based on the results that you get from retention metrics.But there are a couple of key metrics that help with the expansion phase:Monthly Recurring Revenue (MRR)Undoubtedly one of the most crucial levers for long-term growth is expansion revenue.This indicator, also known as expansion monthly recurring revenue (MRR), counts the money gained from repeat business through upsells, add-ons, and cross-sells.ProfitWell recommends that at least 30% of your revenue should come from expansion for a healthy product.According to the Product-Led Growth Flywheel framework, expansion revenue corresponds to the user journey's adoration stage, when your loyal customers start exploring new applications for your product.Net Revenue ChurnSimply put, some of your customers will stop using your product.And so, you will regrettably almost always need to disclose some churn.When you do that, the net churn is a better indicator of your product's health than gross churn rate, since it provides a more holistic picture.Customer churn is generally a less effective metric for growth than revenue churn. This is because, as much as we try denying it, losing your most profitable customer hurts much more than losing your favorite one.Owing to this, if you only use one churn figure, it should be the net revenue churn, which represents the sum of money lost after deducting any new, expanded, or reactivated revenue. The formula to calculate this is:(Revenue lost in defined period - New & Expansion Revenue) / Revenue at the beginning of defined periodTakeawayData management is a huge part of product management that can be approached in multiple ways and requires a deep understanding of the what, why, and how of data, data collection, data management, and big data.Across the entire product life cycle, and particularly at the most vital stages, product data management will define the quality of decisions made pertaining to your product.In doing so, to avoid getting stuck in the web of massive amounts of data, it’s best to know exactly which data you need that will have the most impact on your product.FAQsHow do you acquire customer data?There are several ways to collect data for product data management. Direct observation of customer behavior and preferences, focus groups, surveys, interviews are all the most commonly used methods. Once the customer data is collected, there are multiple ways to assess that data; exploratory data analysis, qualitative data analysis, predictive analysis, etc. are all methods that can be used to further analyze the data.What is the main focus of product management?The core focus in product management is to provide the most value to the customers while also generating value for the business. It combines product vision, strategy, and a deep understanding of the target market and customers to align on a product roadmap. Product management is a function that provides direction throughout the product life cycle — from developing the product to customer acquisition, retention, and expansion.What is the difference between qualitative and quantitative data?Qualitative data is information that is not represented in numeric values, but rather in descriptive terms. Quantitative data, on the other hand, is quantifiable information that can be measured and represented in numeric terms. While qualitative data is more about gauging factors like customer motivation and intent, quantitative data collects vast amounts of information through questionnaires, polls, surveys, etc.What are the three components of CLV?The Customer Lifetime Value (CLV) measures a customer’s average revenue over the period of their relationship with a company. The three components that make up the CLV are: 1) continuous margin after deduction of costs like retention spending, 2) constant probability of retention per time period, and 3) discount rate.
Do Products Drive Data or Does Data Drive the Product?
DISCOVERY & SOLUTIONING WORKSHOP FOR USER-CENTRIC TRAINING DOCUMENTATION An eGuide OVERVIEW In product development, ignoring the digital customer onboarding process can become one of the biggest reasons that drives your customers away. After all, no matter how great your product is, if your customers don’t know how to use it — or find it difficult to — they’ll quickly move on to the next best option. Despite that, while most companies will claim they spend time to create training documentation and support for the digital customer onboarding process, they often go at it with a waterfall approach that can have significant costs involved, including time lost due to delays that can otherwise be avoided. So, as a digital onboarding solution, we realized that an agile approach is necessary to create user-centric training documentation that is accessible, easy to understand, and best serves the customers’ needs. Training Documentation eGUIDE 1In this eGuide, we‘ve revealed a way to kickstart the process of creating user-centric training documentation alongside product development rather than after it. You’ll learn a 10-step process of how to approach the initial stages of discovery and solutioning in a more structured manner and in a way that aligns all stakeholders on how the training documentation should be created. Training Documentation eGUIDE 2 WHAT’S IN THIS EGUIDE HOW TO USE THIS TRAINING DOCUMENTATION GUIDE This eGuide can be used to organize a three to three-and-a-half hour workshop for the initial stages of Discovery and Solutioning to create Training Documentation. The workshop can be conducted in person or remotely on Miro. To synthesize insights from the workshop, we have also provided a template for an Output Report that you can use to present as a summary report to the audience after the workshop. The template for this report as well as other resources are provided at the end of this eGuide. Training Documentation eGUIDE 3TRAINING DOCUMENTATION WORKSHOP FROM PLANNING TO EXECUTION IN 10 STEPS Step 1: Finding the Right Team Step 2: Preparing for the Workshop Step 3: Orientation and Introductions Step 4: Familiarizing with Miro Step 5: Workshop Questions and Goals Step 6: Identifying Stakeholders and Steps in Training Journey Step 7: Mapping Stakeholder Needs in Training Journey Step 8: Lightning Demos, Presentation and Voting Step 9: Overview, Suggestions and Voting for Documentation Formats Step 10: Conclusion and Discussion Training Documentation eGUIDE 4 STEP 1 FINDING THE RIGHT TEAM Identify key participants for your workshop. You should include people from the Product, Marketing, Support & Success, Operations, and Learning & Development teams (if available). Training Documentation eGUIDE 5STEP 2 PREPARING FOR THE WORKSHOP STEP 2 Learn about the product/application for which you have been tasked to create training documentation. Do a quick industry research and competitor analysis to understand other perspectives. Then, shortlist some training documentation formats that can be used for the product. Schedule the workshop for 3 and a half hours (breaks included) and send invites upfront. Training Documentation eGUIDE 6STEP 3 ORIENTATION AND INTRODUCTIONS Initiate the workshop with a quick intro of the hosts and what the workshop is meant for. Lay down the goal of the workshop and align participants on why everyone is there. Ask a representative from the Product team to quickly describe the vision and strategy of the product to refresh everyone's memory. This will serve as the North Star throughout this exercise. Training Documentation eGUIDE 7STEP 4 Let everyone add some details about themselves on the Miro board, so that they can not only introduce themselves but also get acquainted with Miro in the process. FAMILIARIZING WITH MIRO Training Documentation eGUIDE 8STEP 5 WORKSHOP QUESTIONS AND GOALS In this section, we try to understand the vision of this exercise and identify the risks in achieving that goal. Training Documentation eGUIDE 9STEP 6 IDENTIFYING STAKEHOLDERS AND STEPS IN THE USER JOURNEY Ask the team to list down all the stakeholders (both internal and external) who will be consuming the training documentation. Get the team to map down all the steps in the training user journey, like: ● Before launch, ● At the time of launch, ● Post launch, ● Pre-purchase, etc. Training Documentation eGUIDE 10By this point, your team could probably use a breather, so this is the perfect time for a 10-minute break. You can use this time to map the information (Stakeholders, Training Stages) from the previous section on to the board.STEP 7 MAPPING STAKEHOLDER NEEDS IN TRAINING JOURNEY Ask the team to identify and list down the training needs of all stakeholders at each stage of the training journey. Training Documentation eGUIDE 11STEP 8 LIGHTING DEMOS, PRESENTATION AND VOTING At this stage, ask everyone to browse the Internet and research to find other examples and ideas (at least 3 per person) of Training Documentation. Encourage them to go beyond their immediate industry. Identify "BIG IDEAS" that can inspire. Put a sticky note beside it to sum up the idea. At the end, ask everyone to present their ideas and conduct a voting exercise to shortlist ideas (in the next step). Training Documentation eGUIDE 12Take another breather here before the workshop moves into the final stages of overview, other suggestions, and final voting. A 10-minute pause here will help participants reorganize their thoughts before going into the concluding discussions.STEP 9 OVERVIEW, SUGGESTIONS AND VOTING FOR DOCUMENTATION FORMATS Present some popular and relevant training documentation formats to the team based on your research before the workshop. Present pros and cons for each format. Ask the team for any additional ideas not covered previously and add them to the list. Lastly, vote on the formats. Training Documentation eGUIDE 13STEP 10 CONCLUSION AND DISCUSSION Review the content covered in the workshop and give everyone some time to share anything they would like to add that was not already covered. Thank everyone for their time and conclude the workshop. Training Documentation eGUIDE 14POST-WORKSHOP RECOMMENDATIONS REPORT Use the "Recommendations Report" template (provided on the Miro link at the end of this guide) to summarize and synthesize your workshop insights and to propose recommendations for the Training Documentation formats. Training Documentation eGUIDE 15HELPFUL LINKS This eGuide covers only the Discovery and Solutioning stages of the entire process of creating Training Documentation. Here’s the link to the Miro template you can use when you conduct the workshop: https://miro.com/miroverse/training-documentation/ Discovery and Solutioning are only the initial two steps of our 5-step Process of Creating User-Centric Training Documentation for Effective Digital Onboarding. To learn more on our 5-step process to create user-centric training documentation, please refer to this article: https://www.mobilelive.ca/blog/create-user-centric-trai ning-documentation-for-effective-digital-onboarding Training Documentation eGUIDE 16ABOUT MOBILELIVE We’re your full-service digital team obsessed with helping you make smart investments and reduce time-to-launch. Our team of experts specializes in designing experiences, building products, and scaling technology with flexible engagement models, outside-in views, bespoke solutions, and a succession of early wins while never losing sight of the big picture. 100% client retention since day one 40+ iconic & Fortune 500 clients 20+ industry awards & distinctions http://www.mobilelive.ca contact@mobilelive.ca Training Documentation eGUIDE 17
Discovery and Solutioning Workshop for User-Centric Training Documentation
With the wave of Digital Transformation every business is trying to ride, how do you keep up with the wave of users that comes with it?Online and self-serve systems have seen a massive rise with the growth of digital platforms, and an even bigger push with the COVID pandemic. These systems became the need of the hour to limit human interaction, and so technology evolved to a point where providing a seamless and swift digital experience became a standard.But despite that, many corporations still can’t take full advantage of these new technologies and often end up losing potential customers simply because their legacy systems can’t keep up.The Challenge of Adopting New Technologies For Large EnterprisesDue to a dependency on mission-critical legacy systems — predominantly owing to high replacement costs — enterprises are forced to build digital systems on top of the existing legacy systems. This results in two architecture layers with different service-level agreements (SLAs).But while digital APIs deployed on the cloud can scale automatically to sustain any workload, these legacy systems would eventually crash under heavy load, causing loss of business opportunities.In this article, we’re diving into a real-world application of the Command Query Responsibility Segregation (CQRS) design pattern and event sourcing to prevent this issue of overloaded legacy systems.CQRS Pattern to the RescueLet’s dissect the problem stated in the previous section.As we understand it more deeply, we find the root cause behind the need for CQRS and event sourcing patterns: modern digital APIs have the ability to produce a much higher volume of transactions than legacy systems can support.Can we have an architecture where the legacy system is required to process only a subset instead of all the transactions?The CQRS software architecture pattern offers a good solution.The command query responsibility segregation separates the write and read transactions in such a way that digital APIs can commit to one system and query from another.The diagram below shows how the CQRS pattern changes the architecture to send a subset of transactions, "write actions", which usually represent a tiny portion of all the transactions generated by a digital system.Usually, the CQRS pattern is coupled with event sourcing patterns that facilitate data synchronization between the source operational data store and the target read-only data cache in near real-time.The data cache is a high performing database that is located close to the digital APIs to reduce latency. The data model is designed to maximize read performance instead of data integrity since this is a read-only data store.Once the changes above have been implemented, we notice a huge performance increase in the read transaction. We can also observe that the backend systems were able to cope with the seasonal spikes in volume.Implementation Strategy for the CQRS PatternGiven the complexity of the CQRS design pattern, it is preferable to implement it in phases, focusing on the most critical APIs first.When we were using the CQRS pattern, my team had an urgency to fix the issue completely. Hence, the phased approach was rolled out.We’ve realized it works well, and using it for every single API that the digital systems use is found to deliver a reliable experience.But as with any new technology, there can be challenges with the implementation of the CQRS pattern.Two Challenges with CQRS ImplementationThe CQRS pattern has its benefits, but it also comes with many challenges that we need to take into consideration before making a decision to implement it.The two biggest challenges we faced while implementing command query responsibility segregation with event sourcing pertained to:Testing Race Conditions Challenge #1: TestingThe CQRS pattern impacts the most valuable corporate asset in the digital world: customer data.Any defects that are not detected and fixed in a non-production environment may have a drastic impact on customers. For instance, a CQRS defect can result in a privacy issue where a customer gets access to some other customer data.In order to avoid such issues, testing in non-production environments alone is not sufficient.For our project, we went with the traditional approach of testing in non-production and allowing for a period of business readiness testing in production. Then, we launched the system to the customers.As a result, there were many corner cases and special data that we never accounted for that caused some serious issues in production.Solution:A better approach would have been to run the old and new system side by side for some period of time and automatically validate the output from both systems for the same requests in production. The diagram below outlines this approach.Challenge #2: Race conditionsThe operational data store is the source of truth and is updated by many systems, amongst which is the digital system itself.When the update happens from the digital system, the customer expects the new information to reflect on all the views immediately.With the CQRS implementation, this may not be feasible since there could be a delay in updating the data cache and hence the digital APIs would still read the old data.Solution:To get around this race condition, the write transactions that are initiated from the digital system had to also mark the data in the cache as stale and save parts of the write transaction response that can be used for temporarily presenting the latest version of the data on the user interface.TakeawayDespite the challenges that came with it, when we implemented the CQRS pattern, the benefits were evident:An enhanced user experience An increased system availability during the transaction spike period However, with that said, the CQRS pattern is still relatively nascent.While it may seem to be a disruptive solution for a very pressing issue in legacy-based systems, implementing the CQRS pattern should always be taken on a case-by-case basis and with preparedness for the challenges that could arise with it.FAQsIs GraphQL a CQRS?CQRS is a design pattern used in programming that treats data in queries and commands separately. While GraphQL also defines input and output object types separately, aligning with the concepts of CQRS design patterns, CQRS GraphQL is a query language that standardizes data queries.Is CQRS an architecture or pattern?CQRS is a design pattern that can be applied in software architecture. It is an architectural pattern that separates commands and queries in the application architecture.What is the difference between CQRS and event sourcing?The implementation of CQRS happens through a segregation of commands and queries. On the other hand, for implementing event sourcing, the sequence of the events is used to track any changes in data. Event sourcing is a practice to store domain events.How do you implement CQRS?The implementation of CQRS lies in the segregation of commands and queries, meaning it is executed using separate models for read and write. It’s best to implement CQRS in phases and coupled with event sourcing patterns.
Surviving User Overload: How CQRS pattern helps build digital APIs on legacy systems
Have you ever ended up abandoning a product because it forced you into multiple, seemingly unnecessary security layers?Let’s take an example of what I’m talking about. In fact, I’ll share four examples of how security layers meant to “enhance security” can cause more harm than good.Product Security Layers That Do Not WorkComplex Password PolicyBack in my college days, this is what the password policy of our IT department looked like:A default password was set up for every student, which looked something like this: Press@123 All students were required to change this password every month This was a classic example, and not the only one, of a complex password policy that both required symbols, numbers, upper and lower case letters, and also required the users to reset the passwords at an interval of every few days.As a result of this, most users would end up using the same passwords across various websites and, in worse cases, saving their passwords in unencrypted notes.This wasn’t the only consequence of the complex password policy adopted by my college IT department:A massive worldwide online hack took place during those days, due to which a staggering 8 billion login passwords were leaked.I, along with some friends, downloaded and analyzed this dump of account login passwords. A key observation we made while analyzing these passwords, which belonged to university students, professors, and even cyber security experts, was that every time these users were asked to reset their passwords, they would only increment the last number in the password.If this event proved anything, it was how user behavior could easily render such complex security measures redundant and useless.No ‘Show Password’ OptionApple users, in particular, will be able to relate to this issue the most.The unavailability of a ‘show password’ option often results in users opting for other, non-secure ways to avoid typing errors when inserting their passwords. Oftentimes, users are more likely to simply copy and paste their passwords from other non-encrypted places where they've written down these passwords, mostly their notes.Another consequence of a missing ‘show password’ option is that users end up creating short, less complex passwords that are easy to remember as well as type out without mistakes.Fortunately, to prevent such counter-productivity, many platforms have equipped users with the capability to ‘show password’, which is enabled as a default feature so as to minimize errors and enhance the user experience without compromising on the security of their passwords.Frequent LogoutsIn efforts to prevent misuse of account information, a lot of applications and platforms frequently log out the users after a certain time of inactivity (or, as per their own respective policies, even every week or month, etc.).While the need for such policies is evident in sensitive cases like banking and finance apps, these measures can hamper the user experience and end up being an overkill for other use-cases, like HR software or other internal applications.Thankfully, as a solution to this dilemma, we have technologies of Face and Touch IDs to deal with such concerns as they don’t compromise on the user experience while keeping security intact.Prohibited Multi-device LoginsThere are also some applications that will automatically log you out from one place if you try to log in from another device or IP address.This, again, hampers the usability of the application and can be off-putting for users.A better implementation of this, rather than automatically expiring the user sessions, is where users are allowed to add and remember any new device that they log in from. Once they go through the verification for the new device, they can simply ‘remember’ that device, so they don’t have to log in every single time.Product Security and User Experience: Finding the BalanceThe dilemma of choosing between amping up product security versus accounting for better usability and an improved overall user experience is one that we often face in our daily work.According to Avi Douglen, founder and CEO of Bounce Security, AviD’s Rule of Usability posits that:“Security at the expense of usability comes at the expense of security.”The Pessimists Vs. the OptimistsThere are two sides to this dilemma that stems from product security and usability:The Pessimists:These are the Security and IT teams whose belief lies with Murphy’s Law, meaning that what can happen will happen. So, being preemptive, the Pessimists will want to ensure Government-level security for all apps.The Optimists:On the other hand, there are The Optimists. These are the Design, CX, and Sales teams whose primary job is building products that engage the customers and are easy for them to use rather than adding more barriers and complications for the user.Which of these two sides comes out at the top?Neither, because both of them have weight, and both are (usually) valid in their own ways.Is the Minimum Viable Secure Product (MVSP) Worth It?For Product Managers, the most vital thing they need to achieve is to find a balance between usability of the product and its security. This sweet spot between the two is what has come to be known as the Minimum Viable Security Product.Exactly where this balance is achieved will always vary according to the situation and circumstance at hand.For example, some products involve Personally Identifiable Information (PII) or other data that is of high sensitivity. In this case, a product manager may need to lean more towards The Pessimists and adopt a layered security model, endpoint security controls, or another stringent product security framework.With that said, what is of utmost importance here is to not ignore either side of the coin.Often, finding this balance between product security and usability may require you to adopt measures that are costlier.From personal experience, and having learnt the hard way, I’ve found that identifying and choosing such a solution that does the job of keeping both usability and security intact is often worth the added cost.I’ve also found that oftentimes security teams can get lax and suggest a security measure that damages the user experience, while better alternatives do exist that can at least come close to achieving similar levels of security.All that would be required to achieve the latter rather than the former is some added effort (e.g., HTTPS, local storage encryption, app bundle encryption, etc.).TakeawayIn the presence of so many innovative solutions that product teams now have at their disposal, balancing product security with product usability is not the impossible, gargantuan task that it was previously believed to be.In some cases, you might need to encourage and challenge design teams to innovate for an enhanced user experience amidst security constraints. It’s safe to say that seeing how long we have come with technological advancements, solutions like Passwordless logins, Face IDs, Touch IDs, MFA, SSO, and more have made it possible to achieve the right balance between security and usability.FAQsWhat is the Minimum Viable Secure Product (MVSP)?Minimum Viable Security Product (MVSP) is the baseline of the minimum security measures and checks that a product must meet to ensure security. Much like the need has grown from simply providing an MVP to instead creating a Minimum Viable Experience, so has Minimum Viable Security become more relevant and required.What is the Minimum Viable Secure Product checklist?For any software, application, or enterprise-ready products and services, a Minimum Viable Secure Product checklist is a baseline for security measures that can be implemented at various stages of the product and sales cycle. These security controls help manage the security levels for things like requests for proposals, self-assessments, third-party security, vendor compliance, licensing, etc.
Minimum Viable Security: Balancing Product Security and Usability
If there's one thing all UX designers, product designers, and anyone working with design can relate to, it's the plethora of questions that they have to deal with on a daily basis.And our designers were no exception to this.So, in an open conversation with no holds barred, we decided to sit with five designers from our design team to discover:what these questions tend to look like, how designers deal with these queries regularly, and the uniquely distinct perspectives of each UI/UX designer in the field of user experience design. For the sake of this article, we chose three questions to ask each of our five designers:Question 1: What is the most common question you are asked by clients?Question 2: How do you negotiate deadlines with product owners and stakeholders?Question 3: What is your approach when presenting your work?Question 1What is the most common question you are asked by clients?Response 1:Would you recommend this approach based on UX? What is your UX opinion if we do this particular thing? Can we do A/B testing to make a final decision? How can we improve this particular journey based on UX? Can you provide some feedback on the current pages and let us know your thoughts on improvements? What is the best practice for serviceability check for user experience? — Ella Rabiei Senior Product DesignerResponse 2:"Typically, the most common question designers run into is, "When will this be ready for review?""I find that there are generally 2 kinds of questions that a client will ask; "What is the cost? (in time, funds, effort, or stress)" or "Can you explain your thinking?". While these questions may seem like an oversimplification (as it probably is), understanding the underlying intent of these questions helps me, as a designer, ensure I'm providing answers that are effective and concise.Being pedantic and looking at my typical question above, that boils down to "How much time with this cost the project". And why not? Time is as finite a resource as a budget is."— Dana Mitchell Senior Product DesignerResponse 3:"When can you have this done?"This raises an important point, which is that often the emphasis is placed on quantity/output rather than quality. It also highlights the client's lack of understanding of the UX designer's role, which is a weak link in the chain of product design."— Véronique Janosy Senior Product DesignerResponse 4:"How fast can you finish? Will it be quick? Can you do it sooner?"— Carlos Salguero Senior Product DesignerResponse 5:We want to do a redesign. Where do we start? We want to add a new feature to this page. Can you implement this solution? We have these requirements, and we are looking for this solution. When can you deliver this? "Clients need help to uncover problems. Like most humans, they are very good at communicating solutions. My job is to help them find the best solution by first identifying the problems. This is called problem-solution fit. A solution must fit a problem. If the solution does not fit a problem, it is not worth doing it. Furthermore, the solution must be appropriate and not perfect."— Amir Abura Lead Product DesignerQuestion 2How do you discuss and deal with deadlines while working with Product Owners (POs) and stakeholders? If the requested deadline is too tight, how do you negotiate?Response 1:"The POs mostly set a deadline and try to adjust it with the designer's capacity. Depending on the situation, there are several ways to deal with it:If the deadline is for "Tomorrow morning" and it is mid-day already, it depends on the task's value. If it is a small design request, like updating a specific page, I would do it while working on other tasks. If the task is a major design request, I'd let my manager know that I need to pause some of the work to deliver the task, and I would try to see how I can expedite the design process and, if possible, use some of the currently designed pages as my placeholders. To negotiate, I always try to look at the big picture and see how I can accommodate the client's request and my current capacity; I'd also ask to see if there is any possibility of pushing the deadline to some days in the future to avoid the conflict between the deliveries. I'd also ask POs what goals they are trying to achieve with the requested design and try to deliver it in semi-high fidelity mocks instead of high fid versions." — Ella Rabiei Senior Product DesignerResponse 2:"For myself, the easiest way to prioritize is to directly ask what the priorities are. When I have several tasks on the table and not nearly enough time, I start by giving each task a rough time estimate and lay out what I will be able to accomplish in the given time frame.Armed with this knowledge, the PO or stakeholders are better able to understand what my current workload is and decide what the focus will be, hence setting the expectations for the deadline and ensuring that everyone is on the same page.Alternatively, it could be agreed to reduce the amount of work involved; instead of creating a high-fidelity design, a wireframe could be produced instead. Concessions like this allow for the work to begin and start the iterative design process on its merry way."— Dana Mitchell Senior Product DesignerResponse 3:"I'm very up-front about it. If it hasn't been mentioned during the brief, I'll always bring it up; this helps me to prioritize and also to avoid nasty surprises later. More often than not, somebody's got some kind of date in mind–whether it's a hard date that developers need assets by or a more general one, like "sometime during Q2"– and that's a good starting point. Knowing the timeline helps me field questions like "when can you have this done?", since I already know what the projected timeline is. If a timeline is too short, I voice my concern to the PO, and then we work together to reprioritize whatever's on my plate. I always try to remember that there are only so many hours in a workday, and I try to be as honest as possible when it comes to my workload and capacity. When stakeholders are up-front about deadlines, then I can be better prepared to manage expectations."— Véronique Janosy Senior Product DesignerResponse 4:"I explain the intricacies of the work and only negotiate if PO is being reasonable and understanding. Otherwise, UX requires a minimum of 2 weeks and a couple of grooming sessions to consider a wireframe "ready for sprint"."— Carlos Salguero Senior Product DesignerResponse 5:"There is no perfect solution. There is only an appropriate solution given the time and resources available. This is how I negotiate. What can we do now with the time given and resources available? And more importantly, is it worth it? Is there a problem-solution fit? My job is to provide this analysis, so the PO has more info to make a decision: let's do this now and iterate after or let's not do this now and spend more time on the problem."— Amir Abura Lead Product DesignerQuestion 3What is your approach when presenting your work?Response 1:"This question is a bit general when it comes to the audience; I'd try to respond in different scenarios:In General: I'd prepare a fictional or real scenario that includes the research data based on the actual product/design subject and gather the most common/potential pain points plus the written ideation on how to resolve the problems or create a better environment to avoid the potential problems. Then I'd prepare the low-fidelity mockup to discuss the user journey and the product's process path. And once I showcased it, I'd prepare the high-fid mocks to present the finalized idea with showing the details for the solved/improved path, and at the end, I'd present it to the client, including POs, Stakeholders, marketing executives, etc. To the POs and the Dev team: After updating the mocks on Abstract, I'd share my screen in our review session to present the changes/ ideas/ new outcomes on fictional scenarios. I would receive the feedback and comments and will apply them to the design before presenting to the stakeholders. To Stakeholders: mostly happens after the [presentation to PO's] session, once everything is updated, the POs would proceed with presenting the design." — Ella Rabiei Senior Product DesignerResponse 2:"Whenever presenting my work, the first thing I do is prepare myself for feedback. Feedback is a vital part of any design process, but it's not always easy to swallow. I prepare myself to stay grounded and receptive.Next, I consider the audience to whom I am presenting. The goals of each team within a project, myself included, will be different; the artifacts created will be used differently by each team, and I try to keep that under consideration. At the end of the day, my goal is to help set clear expectations to ensure that everyone knows what we are driving towards."— Dana Mitchell Senior Product DesignerResponse 3:"In my job, I don't have to do any kind of formal presentation. I'll usually present what I have to the PO, who will then bring back my work to marketing. The PO acts as a buffer between design and marketing because the interactions can get pretty intense. So when presenting to the PO, I pretty much just tour the component, noting where the design followed or deviated from the original requirements and explaining why. I may have a few different options to present, and the PO and I will usually discuss which one we align with.When presenting to developers, I start by introducing the purpose of the component (if it's a net-new component) or, if it's just a variant, I'll be sure to lead with that. I then talk about the various technical aspects of the component and how it may be similar to or different from either its parent component or a similar one. I then open the floor for questions."— Véronique Janosy Senior Product DesignerResponse 4:"I put myself in my audience's shoes. How can I make this easy for anyone to understand? I make flows, add arrows, include annotations, and, if time allows, create a prototype with all possibilities."— Carlos Salguero Senior Product DesignerResponse 5:"Present the problem, evidence, explorations, and the best solution that fits the problem with the time and resources available. Again there is no perfect solution. Only an appropriate solution that benefits the customer and the business in the present moment with the resources, technology, and time available."— Amir Abura Lead Product DesignerTakeawayAs with any designer, I, as a lead designer myself, have always been at the receiving end of the aforementioned questions, amongst many others.Therefore, this effort was made to not just bring these questions to light but to truly get to discover the perspectives and thoughts of other product and UX designers as well.After all, it's a designer who can best understand another designer, and this candid discussion with my design team provided an opportunity to uncover their unique, creative personalities that make them so good at what they do.FAQsWhat does a UX designer do day to day?In their daily lives, UX designers often work with Product Owners (POs) and other stakeholders to capture the project requirements, conduct research, ideate, design, and demonstrate how a new feature or product is going to work and why any design changes would be required.Does UX design require graphic design?Graphic design is one of the many aspects of UI and Product Design. Having a foundational understanding of graphic design is what drives good interfaces: understanding visual hierarchy, good typography, understanding how to use color, etc.Is UX design in demand?While it is a relatively nascent field, UX design is very much in demand with an exceptional career outlook. In fact, the demand for UX designers continues to grow, leading to the field of UX design being included in one of the 50 best jobs in 2022, according to Glassdoor, amongst other similar rankings.Is UX design coding?Coding is not required, but understanding HTML/CSS code is an asset as a UX/UI or Product Designer. It helps in communicating with developers to speak their language.
Ask A Designer: An Open Conversation with Product & UX Designers
Since the start of this decade, large-scale transformations in business strategy and technology have become standard features across industries and business models. Over the past two years, a significantly large (and rising) number of enterprises have begun adopting a Multi-Cloud strategy.IBM's 2021 global study on cloud transformation also confirms this rapid change. The study found that the use of single-cloud systems declined to a mere 2% in 2021, compared to 29% in 2019. Most companies are either already running Multi-Cloud architecture or will be moving there within the next few years. So it seems fairly obvious that in the years ahead, hybrid or Multi-Cloud will remain the most dominant type of IT infrastructure across industries and business models.In this blog, I aim to discuss what keeps driving organizations to the Multi-Cloud strategy and what challenges they face during this transformation. But before we dive into further details there, let's first examine what we mean by Multi-Cloud.What is a Multi-Cloud Strategy?At its most basic, I would say a Multi-Cloud strategy is simply utilizing two or more public or private clouds. For example, a company utilizing AWS or GCP, or Azure and AWS, can be said to be adopting a Multi-Cloud strategy.Is this strategy applicable to any type of enterprise? Not exactly.I would say a Multi-Cloud strategy is usually a better fit for larger enterprises. Particularly because of the significant costs and complexity involved in running and maintaining the architecture. For small to medium organizations, I would typically recommend Hybrid Cloud as a more appropriate solution option.What Does Hybrid Cloud Mean?Picture a business running a local data center. This business could also be extending some services or replicating its local data center services into a public cloud in parallel. This is precisely what you call a Hybrid cloud model. But there can be further segmentations in the terminology involved. For example, a hybrid strategy where there is a local data center as well as more than two or three public clouds. The combination of these is called Hybrid-Multi Cloud.Why Are More Businesses Turning to Multi-Cloud?Many businesses already have a dual cloud vendor strategy because they want to be more competitive while keeping the cost down. It may sound like a generic motivator, but that does not invalidate the fact that it keeps driving so many enterprises in that direction.FlexibilityThere are also several more specific drivers for organizations to adopt Multi-Cloud strategies. From my perspective, I really feel it's the flexibility to pick services that different vendors offer compared to others that adds to the eagerness to shift.SecurityAnother key aspect, I would say, is from the security standpoint. Cyberattacks continue to be a huge concern for businesses anywhere, causing companies to lose billions in terms of data breaches, confidential information, and reputational damage. It is significantly difficult to bring down a Multi-Cloud environment using a denial-of-service attack or DDOS attack, in my experience. Therefore, the added security would most definitely prove a key motivator for businesses to shift in that direction.Data RecoveryThe third aspect I'd say is data recovery or disaster recovery. Businesses need only to slip up once to become the victims of a significant data breach. So it makes a lot of sense to have contingencies that protect against that. A multi-cloud strategy lets you can create one of the most reliable architectures, which, in turn, can mitigate the fallout from a single-point failure in parallel.In a nutshell: the ability to do best of breed, improved security, disaster recovery, and data availability all contribute to the rapid rise of Multi-Cloud adoption across industries.The Top Challenges When Implementing a Multi-Cloud StrategyWith any new technology and its adoption, there are almost always some challenges. This is no different when it comes to Multi-Cloud. As a technology, it is safe to say that it's still evolving, and organizations are already invested in it. The biggest hurdles they currently face include:A Lack of Industry StandardsOne of the key challenges that many companies face is that there is no industry standard that regulates architecture guidelines, principles, or best practices that need to be implemented.A Lack of Holistic ToolsIn addition, there aren't many tools in the market that can help support or monitor a Multi-Cloud environment. Also, when it comes to provisioning, deploying, and testing the technology, you often don't see too many tools either.Problems with Identity BrokeringLet me use an example for this one. Say you are running an application in a local data center that has typically been authenticated by an active directory. This has to stay in contact with or receive some information from your AWS file system, for instance. AWS, on the other hand, uses an IM service or identity management that authenticates any request that is coming in before it is executed. Translating an AD token or AD authentication to authenticate automatically in AWS is called identity brokering.From an application standpoint, identity brokering is a big challenge in the Multi-cloud environment because each service provider might end up using their application to identify the request and response.Recommendations to Enterprises Implementing a Multi-Cloud StrategyIn my line of work, I can see in the industry there is a new adoption called "infrastructure as code" or IaC. This is one of the ways that you can address admin tasks, from provisioning to deploying, like:Configuring, Testing the virtual machines, Creating containers, Deploying containers, Creating serviceless functions, etc. All of that can be automated by IaC, and this code can then be run by virtually anybody. What this results in is an automated CI/CD pipeline spanned across a Multi-Cloud environment. So, to see more success when implementing Multi-Cloud, I would recommend every business leverages IaC.Specialized Tools for Multi-Cloud ManagementI've already talked about how there aren't a lot of tools that assist in managing and monitoring Multi-Cloud. But I have come across a select few that can help with Multicloud monitoring.The most useful ones are Flexera and Embotics. They are fairly closer than other comparable ones when it comes to managing Multi-Cloud environments. IBM Cloudpark is another fairly good choice, and it sees a lot of use for data insights.For certain use cases, you can also combine tools. Terraform combined with Ensemble, for example, can act as a powerful IT automation tool. This can automate many IT tasks in the context of public and private cloud environments.But, from experience, I would still say I have observed a significant demand from enterprises for a holistic platform that can be used to allocate workload strategy and manage business containers in a hybrid Multi-Cloud environment. Unfortunately, we have yet to see a comprehensive tool that can act as a unified management platform.TakeawayThere is no question about the benefits a Multi-Cloud or Hybrid Multi-Cloud environment brings to a business. The real hurdle is the need for a unified infrastructure management tool. That's what we really need to comprehensively and holistically manage these environments. While there isn't a tool that can claim that title yet, I would say it is only a matter of time before we witness a major cloud provider getting around to releasing it. After all, why not, right?I have come across many companies and vendors trying to get there. There are several third-party tools that are already in development. Azure and AWS have already implemented tools to aid with Hybrid Cloud management. In essence, nothing is really stopping them from making a Multi-Cloud management platform as well. Or to go a step further and create a unified infrastructure management platform. Once that happens, that tool could prove a significant game changer in overcoming challenges to implementing and managing Multi-Cloud architectures. Who knows, maybe that could be a watershed moment right around the corner!
What’s Driving Large Enterprises to Multi-Cloud?
Holding your customer’s attention when they have no time is a big challenge.Particularly in today’s digital age, with average attention spans that only last a few seconds, driving business outcomes now relies heavily on employing the right user engagement strategy.But before jumping into the various types of user engagement strategies used for improving customer experience and retention, let’s quickly understand exactly what a user engagement strategy entails.What is a User Engagement Strategy?A user engagement strategy is a plan to:Grab your customers’ attention, Improve their experience of using your product, and Make them engage with the product offering on a regular basis. Such engagement tools enable us to not only increase customer loyalty and lifetime values, but also optimize the customer experience and boost customer retention for the long term.User Engagement StrategiesThere are many ways to hold your customers’ attention and engage them long enough to make sure they have a great time using your product.The user engagement strategies highlighted in this article are ideal for growth marketing and product success, and are proven ways to garner customer loyalty and retention:Customer Journey Mapping Hyper-Personalisation Integrating Analytics Rewards and Customer Loyalty Program Chatbot 1. Map Your Customer JourneyTo effectively develop and implement a user engagement strategy, it is crucial to understand who your target audience is and how they use your product.A good way to do this is through customer journey mapping.In this user engagement strategy, you map out your product’s customer journey and understand all the different ways a customer can interact with your digital platform or app. This allows you to unravel any challenges the user may face and discover more about your users’ needs and behavior, which is instrumental in identifying opportunities for engagement.Illustration 1.1: Common touch points surrounding a customer journey map that a visitor takes to start the buyer journey process.Additionally, analyzing your customers’ behavior will allow you to segment your target base and create customer personas. Collecting this knowledge is the first step in creating a tailored user engagement strategy to interact with each persona according to their distinct preferences.2. Create Hyper-Personalized OfferingsIn today’s day and age, providing personalized experiences has become an essential user engagement strategy. And it makes sense why – customers have grown to want products that are tailored to their needs.Personalization can be something as simple as showing customers a location-based ad or using their first name in an email.It could also become something much more complex, by applying nascent technologies such as machine learning and AI to offer hyper-relevant services.A good example of personalisation in the Telecommunication industry is when a customer who regularly purchases data add-ons to meet his monthly needs is offered an exclusive package to upgrade their plan at a discount. Usage behavior, interests, location, device information, etc. could be used to create a unique offer for each customer that best meets their needs.Push notifications are also essential when it comes to a successful user engagement strategy and plan. Using these notifications effectively can help improve awareness and usage for your product features. Location-based notifications or offers can allow you to give the users of your product a contextual experience that would be hard to match.3. Track Business AnalyticsThere is no way of knowing whether your customer engagement strategies are working or not if you don’t have specific success metrics in place and aren’t tracking them on a regular basis.Therefore, it is essential to close the customer loop by listening to their feedback and incorporating it into improving your product offering.Data driven decision-making can allow you to identify behavioral trends and understand how the customers use your product in order to address their needs.Tracking your daily active users (DAU) as a % of your monthly active users (MAU) can give you an estimate on the number of days your customers use your product in each month.Segmentation and analytics play a pivotal role in driving the marketing strategy for an organization. Quantitative insights can help build your brand persona and also help identify the USPs of your product. This can be used to create acquisition and retention campaigns to increase usage.Understanding the onboarding process for your platform and the use of conversion funnels to identify the relevant drop-offs can also play a crucial role in helping uncover design or product level issues that might be hampering your overall user engagement.The use of analytics tools can significantly impact the growth of engagement on your platform. Using these tools can enable you to segment your user audience based on multiple attributes i.e. age, location, interests. These segments can then be targeted with the use of available communication channels or push notifications, thus generating not only interest but also customer retention and loyalty to your offering.4. Design a Customer Loyalty ProgramAnother effective user engagement strategy is to engage your customer through a customer loyalty program that rewards them with points, discounts and gifts in return for frequently engaging with your platform.These programs can prove to be very effective as they not only improve customer retention, but also increase customer switching costs by boosting customer loyalty. Having a loyal customer base can generate word of mouth and referrals for your product, which can attract the desired audience to your platform.Illustration 1.2: Customer loyalty programs improve customer engagement and boost retention rates.5. Implement a ChatbotChatbots are a proven tool to increase conversion rates, especially when it comes to digital platforms.An AI-powered chatbot has the ability to analyze data and appropriately reply to users based on preprogrammed recommendations. The program can also be engineered to trigger specific messages based on customer attributes such as location, time, or engagement levels.Bots can also help users choose products they like, answer FAQs, or even act as a trouble-shooter to decrease churn rates.Additionally, another key advantage of using chatbots as part of your user engagement strategy is their ability to offer minimum service levels even during after-hours when a call center representative is not available. This helps create an additional channel through which key customer information can be collected so that they can be contacted later.TakeawayThere’s no doubt that the digital age of today has transformed the way businesses operate and seek out customers.Therefore, in efforts to keep up with such transformation, it is now more crucial than ever to adopt smart approaches and strategies that put the customer at the center of your engagements with them.User engagement strategies like the ones discussed above all help you hold your ideal customers’ attention, improve their experience using your product, and keep them coming back for more.Remember, the right user engagement strategy will pave the way for transforming your target audience into loyal customers that stick with you for the long haul.FAQsHow to optimize user engagement?There are many user engagement strategies that help optimize user engagement for your product, digital platform, or app. Some great ways to do this include customer journey mapping, hyper-personalization, integrating analytics, customer loyalty programs, rewards, and chatbots.How do you increase product engagement?Product engagement can be increased by incentivising customers (e.g. through loyalty programs and rewards), using and studying user data to create personalized notifications and offers, and streamlining and optimizing the overall user experience through customer journey mapping.
Improving Customer Experience with the Right User Engagement Strategy
What do you do when you need people to come together and be on the same page for a new concept at your organization?Some back and forth via emails? Putting in requests for meetings where the conversations are driven by PowerPoint pitches? Having one discussion at a time, and that too only led by the extroverts or people with titles and power?What about the value that the rest of the individuals at your organization can bring into these discussions? How do you cultivate an environment for b