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

When I first stepped into a UX research leadership role, I quickly realized that guiding a team through complex projects was far more challenging than I expected. Mistakes happen, but some can slow down progress or even damage the credibility of UX research within an organization. Over time, I learned which pitfalls to watch for and how to steer clear of them. If you lead a UX research team or aspire to, this post will help you avoid common errors and build stronger, more eff

Using research effectively can transform how designers and product managers (PMs) make decisions. Yet, many teams struggle to integrate research into their daily workflows. I’ve seen firsthand how coaching can bridge this gap, helping teams not only understand research but also apply it in ways that improve products and user experiences. In this post, I’ll share practical strategies I’ve used to coach designers and PMs to get more value from research. These approaches focus o

When I first joined a product team, I quickly realized we lacked a clear way to measure how users experienced our product. We relied on gut feelings and anecdotal feedback, which made it hard to know what truly worked or where to improve. Building a UX measurement framework changed everything. It gave us clear data to guide decisions and improve user satisfaction. If you want to create a similar system in your organization, here’s how to start. UX Measurement Framework: Under

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