AI is reliably useful for two parts of user research right now: turning a plain-language question into a structured study, and turning a pile of open-ended responses into a clear summary with the original quotes attached. It is not useful, and shouldn’t be trusted, for two other things: standing in for a real participant, or deciding what a finding means without a person checking the reasoning behind it. Almost everything people argue about when they argue about “AI in research” is a version of one of those four claims.
Where AI genuinely helps: turning a question into a study
Writing a good study is a skill most teams don’t have lying around: choosing the right method for the decision at hand, phrasing tasks so you’re not leading the participant, deciding how many questions is too many before people start clicking through on autopilot. A model that’s seen enough of this structure can take “I want to know if people understand our new pricing page” and turn it into a specific set of tasks and questions built from an established method, ready for a human to review before anything goes out. That’s genuinely useful compression: the model isn’t inventing research methodology, it’s applying patterns that already exist, faster than a person would look them up.
Where AI genuinely helps: reading responses back into findings
The other side of the same coin: once responses come back, someone has to read them, group the recurring complaints, and separate a real pattern from one loud participant. A model is good at exactly this kind of compression across a large pile of open-ended text, and good at keeping the original quote attached to the summary it produced, so a human can check the model’s reasoning against what was actually said rather than trusting a paraphrase on faith.
Where AI does not help: it has no lived experience
A model can describe what confuses people, drawing on patterns from things it’s read. It cannot be confused itself. It has never clicked the wrong button because a label was ambiguous, never felt the specific frustration of a checkout flow that asks for the same information twice. Every insight it produces is a compression of things real people already said or did. It has nothing of its own to add about how a genuinely new interface feels to use, because it has never used one.
Where AI does not help: synthetic respondents aren’t respondents
A growing slice of the research-tools market now sells AI personas trained to answer as if they were real customers, marketed as a faster, cheaper stand-in for actual people. They’re useful for pressure-testing whether a survey instrument is even coherent before you send it to anyone real. They are not evidence about what real people think, because a persona has no lived friction, no unexpected interpretation of your question, and no complaint you didn’t already think to script into its training. Asking a model to imagine how a user would answer, and asking a real user, are not two versions of the same data. They’re two different things wearing the same chart.
AI is very good at compressing what people already said. It has never been good at generating what people would say, and there’s no evidence that changed.
The honest way to use AI in a research workflow
The practical line holds up well in practice: let AI handle the drafting and the reading-back, and keep the actual participants real. That split doesn’t require a paid tier to access, either. Fiuto runs on Anthropic’s Claude models on every plan, including Free, and every AI feature there is metered by usage credits rather than gated behind a subscription tier: which model handles a given task depends on the task, not on what you’re paying. What stays constant across every plan is that responses only ever come from people you invited yourself, never a generated persona standing in for them.
A line worth drawing
If a claim in your findings traces back to something a real person actually said or did, AI compressed it faster than you would have by hand, and that’s worth using. If a claim traces back to a model imagining what someone might have said, it isn’t a finding. It’s a guess wearing a finding’s clothes, and the fix is the same one it’s always been: put the question in front of real people, and let the model help you read what comes back.
