Artificial intelligence is reshaping many fields, and UX research is no exception. As AI tools become more capable, they challenge long-standing methods and habits in user experience research. This change is not about losing the value of UX research but about letting go of outdated practices that no longer serve the evolving landscape. Understanding what AI will force UX research to leave behind helps professionals adapt and focus on what truly matters. What AI Will Force UX

Artificial intelligence (AI) offers powerful tools that can transform research strategies across industries. Yet, integrating AI into research requires careful planning to avoid risks such as data misuse, bias, and loss of human insight. Leaders face the challenge of balancing innovation with responsibility to ensure AI supports research goals safely and effectively. This post explores practical steps leaders can take to introduce AI into their research processes while mainta

User experience (UX) research has seen rapid advances thanks to artificial intelligence (AI). AI tools can analyze vast amounts of data quickly, identify patterns, and even predict user behavior. Yet, despite these strengths, human intuition remains essential in UX research. This post explores where human insight outperforms AI and why combining both approaches leads to the best results. Where Human Intuition Still Beats AI in UX Research Understanding Context and Emotions AI

Research planning often feels overwhelming. Defining clear goals, gathering relevant data, and organizing resources can slow progress before it even begins. Generative AI offers new ways to simplify and improve this process. It helps researchers clarify ideas, generate hypotheses, and structure their work efficiently. This post explores how to plan research using generative AI, with practical tips and examples to guide you. Planning Research with Generative AI How Generative

Research drives progress in science, medicine, technology, and many other fields. Yet, traditional research methods often require long hours, extensive data analysis, and repeated experiments. Artificial intelligence (AI) offers a way to speed up these processes, helping researchers find answers faster and more efficiently. This post explores how AI accelerates research, with practical examples and insights into its growing role. Accelerating research with AI Accelerating res

Artificial intelligence often gets attention for how fast it can process data or automate tasks. But speed is only part of the story. One of AI’s most valuable roles is helping people and organizations reduce uncertainty. When decisions involve unknowns or risks, AI can provide clarity and confidence. This post explores how AI can do more than just accelerate work—it can make outcomes more predictable and reliable. AI Uncertainty Understanding uncertainty in decision-making U

Usability testing remains a cornerstone of creating user-friendly digital products. Yet, traditional methods can be time-consuming and sometimes miss subtle user experience issues. AI-assisted usability testing offers a fresh approach by speeding up analysis and uncovering patterns that might go unnoticed. This post provides a practical checklist and a template to help you integrate AI tools into your usability testing process effectively. AI-Assisted Usability Testing Checkl

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, making research faster, more accurate, and

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 reviewing AI-driven user insights on a laptop

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 decisions. That’s where AI stepped in f

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

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 during usability testing. In this post, I’ll

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