When I first started working on user experience (UX) design, I quickly realized that delivering a seamless experience across multiple channels and platforms is one of the toughest challenges. Users expect to switch from a mobile app to a website or even a physical kiosk without feeling lost or confused. But how do you measure if your UX is truly consistent? Over time, I developed a practical approach to evaluate UX consistency that I want to share with you. Measuring UX Consi

When I first started working in user experience, I was eager to quantify every insight I gathered. It seemed logical: if something is important, it should be measurable. But over time, I realized that not every UX insight fits neatly into a metric. Some insights lose their meaning or even mislead when forced into numbers. This post shares why some UX insights should remain qualitative and how turning them into metrics can sometimes do more harm than good. Why Some UX Insights

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 writing UX research case studies, I thought the story was simple: show the problem, explain the research, and present the solution. But over time, I realized many case studies miss a crucial part — demonstrating the real impact of the research. This gap often leaves readers wondering if the research made any difference at all. In this post, I want to share what I’ve learned about why most UX research case studies fai

By Philip Burgess | UX Research Leader When I first started working in the UX field, I often found myself confused by the terms "UX research case study" and "UX design case study." They seemed similar on the surface, but as I gained experience, I realized they serve very different purposes and highlight distinct parts of the product development process. Understanding these differences can help you communicate your work more clearly and showcase your skills effectively. A UX r

By Philip Burgess | UX Research Leader Starting a ResearchOps function can feel overwhelming. When I first faced this challenge, I didn’t have a clear roadmap. I knew research was crucial for product success, but organizing it efficiently was a puzzle. Over time, I learned that building ResearchOps is about creating a system that supports research activities, making them smoother and more impactful. If you’re ready to build your ResearchOps function from scratch, I’ll share p