A new pair of eyes for research data

Talking to users produces a lot of material. Notes from interviews, recordings, transcripts, screenshots, support tickets, survey responses. Within a few weeks of serious research, you can have dozens of hours of input.

Reading through all of it carefully is hard. Spotting patterns across it — without favouring the things you already believe — is harder still. This is exactly the kind of work AI is well suited to help with.

Used carefully, AI can become an extra pair of eyes for your research: surfacing themes, comparing perspectives, flagging contradictions, and generally helping you see what your own attention might be missing. Used carelessly, it can compress useful nuance into smooth summaries that bias your decisions in subtle ways.

Let’s walk thro...

Using AI to analyse what you’re hearing (without bias)