AI Customer Discovery & Research

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AI Customer Discovery & Research    

This is the foundation layer: every roadmap decision, prioritisation call, and PRD inherits its quality from how well you understand the customer, so it belongs first in the stack. Traditional discovery — static surveys with 5-15% response rates, or a PO manually re-reading 40 interview transcripts — simply doesn't scale. AI research tools solve two different problems. Repository tools like Dovetail ingest interviews, support tickets and call recordings, then auto-tag and summarise them so patterns across hundreds of conversations surface in minutes instead of days. AI-moderated interview tools like Perspective AI go further, running the actual conversation: they ask a real question, listen to the answer, and adaptively follow up on the 'why' the way a skilled human researcher would, at a scale no research team could match manually. Usability-testing platforms like Maze sit in between, validating a specific prototype or flow with real users before it reaches engineering. For a Product Owner, the practical payoff is speed and rigour: you can run discovery continuously rather than as an occasional research sprint, and you walk into prioritisation conversations with synthesised evidence instead of a gut feeling or the loudest stakeholder's opinion. The caution worth flagging: AI summarisation is good at triage but the research community's consensus is that it's not yet reliable enough to replace a human doing the final synthesis on a decision that really matters — use it to narrow 200 data points to the 10 worth reading closely, not to skip reading them altogether.