AI customer discovery begins with observation and interviews rather than a predetermined model feature. Engineers examine recurring work, completed cases, delays and error costs to identify where model capability can remove a genuine bottleneck.
The process must distinguish an impressive demonstration from a product people will trust and use. Watching real users work reveals missing context, informal steps and adoption barriers that requirements documents often omit.
ELI5
AI customer discovery studies real users and their work to find problems where AI could provide measurable value. It starts by understanding needs and constraints instead of beginning with a model feature that someone wants to sell.
For example, a team can watch support workers handle difficult cases and learn that finding old account information causes the main delay. That evidence may support a retrieval tool, while also revealing privacy rules and trust concerns that a quick demonstration would miss.
