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 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