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

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 conflicts and make decisions that balan

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

By Philip Burgess | UX Research Leader When I first started using AI tools in my UX research, I was excited about the speed and scale they offered. But I quickly realized that without clear guardrails, these tools could lead me astray. AI can generate insights fast, but it can also introduce bias, misinterpret data, or overlook important human context. Designing guardrails for AI-assisted UX research is essential to get reliable, ethical, and useful results. In this post, I’l

By Philip Burgess | UX Research Leader Agentic AI is changing how we think about artificial intelligence. Unlike traditional AI that follows strict instructions, agentic AI acts with a degree of autonomy, making decisions and taking actions to achieve goals. This shift has significant implications for UX research and the professionals who shape user experiences. What Is Agentic AI? Agentic AI refers to systems designed to operate independently, with the ability to set objecti

By Philip Burgess | UX Research Leader Artificial intelligence has transformed many fields, including research. Yet, AI’s role in research often gets misunderstood. It is not a researcher in the traditional sense but serves best as a research critic. This distinction matters because it shapes how we use AI tools effectively and ethically in academic and scientific work. AI analyzing research data with critical insights Understanding the Difference Between Researcher and Resea

By Philip Burgess | UX Research Leader Artificial intelligence (AI) has transformed many industries, from healthcare to finance, by automating tasks and uncovering insights from vast data. Yet, despite rapid progress, AI systems often fall short of their full potential. The key to unlocking more effective AI lies not just in technology but in the expertise of experienced researchers. These professionals bring critical skills and judgment that machines alone cannot replicate.

By Philip Burgess | UX Research Leader User experience (UX) research has always played a key role in shaping products that meet user needs. Traditionally, UX research involved manual data collection, interviews, and usability testing, which could be time-consuming and limited in scope. Today, artificial intelligence (AI) is transforming how UX research informs decisions, making the process faster, more precise, and more insightful. This shift is changing how teams understand

Research teams often face the challenge of managing vast amounts of information while trying to stay focused on their core tasks. An AI research assistant can help by automating routine tasks, organizing data, and providing quick insights. Building such a tool tailored to your team’s needs can boost productivity and improve the quality of research outcomes. AI research assistant interface on laptop screen AI Research Assistant: Understand Your Team’s Research Workflow Before

User experience research demands creativity, precision, and efficiency. AI tools can help researchers generate ideas, analyze data, and communicate findings faster. However, knowing which AI prompts to use can make a big difference in the quality and speed of your work. This post shares essential AI prompts every UX researcher should save to improve their workflow and deliver better insights. A UX research dashboard with visual data and notes Generating Research Questions and

Creating user experience (UX) designs quickly without losing quality is a challenge many designers face. AI prompt templates offer a practical way to speed up UX synthesis by providing structured starting points for generating ideas, wireframes, and user flows. This post explores how using AI prompt templates can help UX professionals work more efficiently while maintaining creativity and clarity. UX wireframe template displayed on laptop screen AI Prompt and UX Research Temp

By Philip Burgess | UX Research Leader User experience (UX) research has always played a key role in shaping products that meet user needs. Today, artificial intelligence (AI) is transforming this field, shifting UX research from a purely tactical function to a strategic driver of innovation and business growth. This change affects how teams gather insights, make decisions, and design experiences that truly resonate with users. UX researcher reviewing AI-driven insights AI En

By Philip Burgess | UX Research Leader Research today moves faster than ever, and artificial intelligence plays a key role in helping scholars, scientists, and students manage data, analyze results, and generate insights. Choosing the right AI tools can save time, improve accuracy, and open new possibilities in research. This post highlights some of the best AI tools available in 2025 that are worth using for various research tasks. Researcher working with AI software on lapt

By Philip Burgess | UX Research Leader User experience (UX) research is essential for creating products that truly meet user needs. Recently, artificial intelligence (AI) tools have become popular for speeding up data analysis and generating insights. While AI offers clear benefits, relying too heavily on it in UX research can lead to serious pitfalls. This post explores why depending too much on AI can harm the quality of UX research and what researchers should keep in mind

By Philip Burgess | UX Research Leader User experience (UX) research relies on clear, accurate insights to shape products that truly meet user needs. As artificial intelligence (AI) tools become more common in UX research, they offer powerful ways to analyze data and uncover patterns. Yet, AI can also introduce errors that reduce the quality of research findings. Avoiding these mistakes is essential to maintain trust in your UX insights and build better user experiences. This

By Philip Burgess | UX Research Leader User experience (UX) researchers face growing challenges as AI tools become part of their daily workflow. Crafting effective AI prompts can unlock powerful insights, speed up analysis, and improve design decisions. Yet, many UX researchers struggle to write prompts that deliver clear, relevant, and actionable results. Mastering the right AI prompt frameworks can transform how you gather and interpret user data. This post explores 10 esse