How to Use AI for User Research Synthesis

Synthesis is turning a mess into a small number of things you can act on. Interview transcripts, open ended survey answers, support tickets.

It is slow, and it is where bias gets in, because after four hours of reading you start finding the pattern you expected to find.

AI is genuinely good at the volume part. It is not good at deciding what any of it means.

Cluster first

Paste the notes and ask for themes with a count of how often each appears.

This gives you a first map in a minute rather than an afternoon. Treat it as a map, not as a finding.

Rank by frequency and by severity

Ask it to sort by how often something appears and how painful it sounds.

Those two are different and keeping them separate is the point. It splits the loud and rare complaint from the quiet and common friction, and the second one is usually the more expensive problem.

Ask for the contradictions

Explicitly request quotes that contradict the majority.

Outliers often point at a segment you are underserving. A summary averages them away by design, which is the one thing you do not want from research.

Then go back to the quotes

Before you trust a theme, read the actual quotes behind it.

Models will over generalise and produce a tidier story than the data supports. The raw evidence is the only thing that keeps you honest, and it takes ten minutes.

Questions I get asked

Will it misrepresent my data? It can, if you let it summarise unchecked. Trace every theme back to source quotes and the risk mostly disappears.

Does this replace a researcher? No. It speeds up clustering. Deciding what the insight means, and what to do about it, is still a person's job and always was the hard part.