Your AI product is already interviewing every user.
Most teams throw the transcript away.
Traditional analytics forced us to infer intent from clicks, funnels and abandoned screens. An AI interaction often gives us something better: the user states the goal directly, explains what is missing, corrects the system, and reveals whether the answer was useful.
Yet those signals usually remain scattered across model logs, support tickets, sales calls and engineering dashboards. That is a missed learning loop. With consent, redaction and clear retention rules, teams can classify repeated goals, identify failure clusters, connect outcomes to revenue or retention, and decide what the product should change next.
This is not only agent observability. It is a new product-research surface.
The valuable asset is not another dashboard; it is a reliable path from what users said to what the organization changes.