PHILIP BURGESS

By Philip Burgess | UX Research Leader When I first started working in UX research, the process often felt slow and repetitive. Gathering user feedback, analyzing data, and generating insights took weeks, sometimes months. I knew there had to be a better way. That’s when I began exploring agentic AI—intelligent systems that can act autonomously to support and improve UX research workflows. Since then, I’ve seen firsthand how this technology can transform the way we work, maki

By Philip Burgess | UX Research Leader Artificial intelligence is reshaping how companies design products and understand users. As a UX researcher, I’ve seen firsthand how AI changes the tools we use and the questions we ask. To stay effective, we must develop new skills that help us work alongside AI, interpret complex data, and design for intelligent systems. Here’s what I’ve learned about the skills UX researchers need in an AI-enabled organization. A UX researcher reviewi

By Philip Burgess | UX Research Leader When I first started working in UX research, I often felt overwhelmed by the uncertainty that came with interpreting user data. We would gather feedback, run tests, and analyze results, but there was always a lingering question: Are we really understanding what users need? Over time, I realized that the challenge wasn’t just about collecting data quickly—it was about making sense of it in a way that reduces doubt and leads to confident

By Philip Burgess | UX Research Leader When AI tools deliver insights at lightning speed, it feels like decision-making should become faster too. Yet, I’ve noticed the opposite happening in many cases: quicker AI insights sometimes slow down the actual decisions. This might sound counterintuitive, but the reality is more complex. I want to share what I’ve learned about why faster AI insights don’t always translate into faster decisions and what we can do about it. Faster AI I

By Philip Burgess | UX Research Leader Artificial intelligence research moves fast. Every week, new papers, models, and breakthroughs appear, promising to change how we live and work. But I’ve learned that taking AI research outputs at face value can lead to misunderstandings, misplaced trust, and even setbacks. In this post, I want to share my experience and insights on why it’s crucial to look beyond the headlines and dig deeper into what AI research really means. What happ

By Philip Burgess | UX Research Leader When I first started designing research studies, I often faced a frustrating challenge: investing weeks or even months recruiting participants only to find that my hypotheses were weak or flawed. This meant wasted time, resources, and sometimes the need to start over. Over time, I discovered a powerful way to avoid this pitfall—using artificial intelligence to stress-test research hypotheses before recruiting anyone. This approach transf

By Philip Burgess | UX Research Leader When I first started working in UX research, I quickly realized how time-consuming and complex gathering user insights could be. Sorting through mountains of data, conducting interviews, and analyzing feedback often felt overwhelming. That’s when I began exploring AI tools designed to make UX research faster and more effective. If you’re wondering which AI tools can truly help you understand your users better, I’ll share my experience an

By Philip Burgess | UX Research Leader Card sorting is a powerful method for organizing information, improving user experience, and designing intuitive navigation systems. When I first started using card sorting in my projects, I found it challenging to create prompts that would guide participants effectively. That’s when I turned to AI for help. Using AI-generated prompts transformed how I approached card sorting, making the process smoother and more insightful. In this post

By Philip Burgess | UX Research Leader Tree testing is a powerful method to evaluate the structure of a website or app by testing how easily users can find information. Yet, setting up effective tree tests and analyzing results can be time-consuming and complex. Over the past year, I’ve explored how AI can simplify this process, making tree testing faster and more insightful. In this post, I’ll share the best AI prompts that helped me improve my tree testing workflow, from de

By Philip Burgess | UX Research Leader When I first started working in UX research, analyzing data felt overwhelming. There were so many numbers, user comments, and behavioral patterns to sift through. I knew AI could help, but I wasn’t sure how to ask the right questions or use prompts effectively. Over time, I discovered specific AI prompts that transformed my approach to data analysis. These prompts helped me uncover insights faster and make stronger design decisions. If y

By Philip Burgess | UX Research Leader When I first started working in UX research, I quickly realized how time-consuming and complex gathering and analyzing user data could be. Sorting through interviews, surveys, and usability tests often felt overwhelming. Then I discovered AI tools designed specifically to support UX research. These tools transformed my workflow, making it faster and more insightful. If you want to improve your UX research process, here are some of the be

By Philip Burgess | UX Research Leader When I first started working on usability testing, I quickly realized how time-consuming and repetitive the process could be. Crafting the right questions and scenarios to uncover real user pain points felt like an endless challenge. That’s when I turned to AI prompts to help streamline my workflow and get more insightful feedback faster. Over time, I discovered several AI prompts that consistently deliver clear, actionable results durin

By Philip Burgess | UX Research Leader When I first started working in UX research, I quickly realized how time-consuming it could be to gather and analyze user data. Sorting through surveys, interviews, and usability tests often felt overwhelming. Then I began exploring how artificial intelligence (AI) could help me work smarter, not harder. Over time, I discovered practical ways to use AI tools that improved my research process and delivered clearer insights faster. If you

By Philip Burgess | UX Research Leader When I first started using ChatGPT in my UX research projects, I was curious but cautious. Could an AI really help me understand users better? Over time, I discovered practical ways to use ChatGPT that saved me time, sparked new ideas, and improved the quality of my research. If you are a UX researcher looking to enhance your process, this post shares my experience and tips on how to use ChatGPT effectively. ChatGPT conversation on UX re

By Philip Burgess | UX Research Leader When I first encountered AI confidence scores, I assumed they worked just like the confidence levels I learned about in research studies. It seemed natural to think that a high AI confidence score meant the same thing as a high confidence interval in scientific research. Over time, I realized this assumption was misleading. The two concepts serve different purposes and come from very different processes. Understanding these differences c

By Philip Burgess | UX Research Leader Artificial intelligence has become a powerful tool in research, offering data-driven recommendations that can accelerate discovery and improve decision-making. Yet, what happens when AI suggestions clash with evidence or insights gathered by researchers themselves? This tension between machine-generated advice and human judgment is a challenge many research leaders face today. I want to share how I and others in the field navigate these

By Philip Burgess | UX Research Leader When I first started working in UX research, measuring user experience felt like trying to catch smoke with my bare hands. We relied heavily on surveys, interviews, and manual observation. These methods gave us valuable insights but often lacked precision and speed. Today, artificial intelligence (AI) is reshaping how we measure UX research, making the process faster, more accurate, and more insightful. I want to share how AI has transfo

By Philip Burgess | UX Research Leader I remember the first time I relied heavily on AI-generated metrics to evaluate a user experience (UX) project. The numbers looked impressive: high engagement scores, positive sentiment analysis, and smooth navigation paths. I felt confident presenting these findings to the team, convinced we had nailed the user journey. But soon, real user feedback told a different story. The AI metrics had painted an overly optimistic picture, missing c

By Philip Burgess | UX Research Leader When I first joined a UX research team, our work revolved around manual data collection, lengthy interviews, and hours spent sifting through notes. The process was slow, and the pressure to deliver actionable insights often felt overwhelming. Over the past few years, I’ve witnessed a significant shift in how AI tools have transformed our daily tasks and the expectations placed on our team. This change is not just about faster workflows b

By Philip Burgess | UX Research Leader When I first started working with AI tools in research, I believed they could solve nearly every problem. After all, AI can process vast amounts of data faster than any human. But over time, I realized that even with perfect data, AI falls short in making the nuanced judgments that research demands. This post explores why human judgment remains essential in research, despite advances in AI and data quality. Researcher reviewing complex d