In-person research has been squeezed for years. It’s viewed as expensive, slow, and hard to scale. As AI accelerates everything else, the temptation is to automate away human time in the field, for the sake of speed.
I contend that AI’s real opportunity lies in depth, not just speed. We’re getting distracted by speed, and failing to see the potential of depth.
In this talk, I argue that AI gives us the rare opportunity to restore depth to qualitative research. Instead of treating automation as a shortcut, we used AI tools to absorb labour-intensive tasks (cleaning transcripts, tagging footage, structuring notes etc) so we could reinvest that time in what remains the most data-dense method in our practice: immersive, in-person ethnography.
I’ll draw on three recent consulting projects I directed:
- In-home health research in Atlanta
- Retail ethnography across London, Hamburg, and Milan
- A follow-on multi-city retail study in London, Paris, Berlin, and Milan
By redirecting AI-generated time savings into deeper fieldwork, we expanded ethnographic activities: from extended “deep hanging out” (to borrow Clifford Geertz’s phrase) to ethnographic journaling and collaborative interpretation moments in-field.
I’ll also share how we brought non-research stakeholders into the field, and why AI was essential in making that investment possible — transforming not just insights, but internal conviction and cross-functional alignment.
My goal for the audience is to come away from the talk inspired and motivated. I want people to use AI tools to help mitigate all the usual tradeoffs we as researchers have had to make over the years when conducting in-person research.
