By Philip Burgess | UX Research Leader User experience (UX) research generates vast amounts of qualitative and quantitative data. Organizing this data effectively is essential to uncover meaningful insights that improve product design and user satisfaction. One powerful method to manage and analyze UX data is through tagging taxonomies . This guide explains how to build and use tagging taxonomies in UX research, offering practical advice and examples to help you get started.

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

By Philip Burgess | UX Research Leader Call centers often face high call volumes that strain resources and increase support costs. Many of these calls come from users struggling to navigate websites or apps. Improving user experience (UX) can reduce these calls by making digital interactions smoother and more intuitive. This post explores how UX improvements lower call center volume and support costs, with practical examples and clear benefits. Customer service desk with clea

By Philip Burgess | UX Research Leader Understanding user experience (UX) metrics is crucial for making informed design decisions. However, raw numbers alone can be misleading without a sense of their reliability. Confidence intervals provide a way to express the uncertainty around UX measurements, helping teams understand how much trust to place in their data. This post explains how to build confidence intervals for UX metrics, making your analysis clearer and more actionabl