AI in UX research — achieve more with less

What the article covers

Paul Boag, a UX consultant with over 25 years of experience, shares lessons from integrating AI into real client projects. The article covers five main use cases for AI in user research: online research, analyzing interviews and surveys, research projects with persistent context, UX audits, and quick competitor analysis. Boag also discusses AI applications in design and development.

Context

Unlike most AI-in-research articles that are either enthusiastic or skeptical, Boag writes from a pragmatic consulting perspective. He admits to wasting hours trying to get AI to do things it is bad at, which gives his recommendations more credibility than advice from people who have only had successes.

Key takeaway

The most useful mental model in the article is treating AI like an intern with zero experience: enthusiastic and qualified, but requiring detailed instructions, supervision, and review. Applied to research specifically, Boag demonstrates this with concrete techniques. For interview analysis, he always requires direct quotes from transcripts so he can verify the AI is not fabricating. For survey analysis, he now uses open-ended questions freely because AI can process hundreds of text responses in seconds, a change that has altered his survey design approach. His concern about AI is specific rather than general: over-reliance could disconnect researchers from real users, which is a risk worth monitoring but not a reason to avoid the tools.

Who should read this

UX practitioners and consultants who want practical, honest guidance on where AI helps and where it wastes time in real client work, based on someone who has made the mistakes and figured out the patterns.