The first useful AI feature is usually smaller than the first idea for an AI product. It helps someone finish one defined task: find a passage, sort an incoming request, or prepare information for review.
A narrow scope makes it possible to test the output, see where it fails, and decide whether AI is the right tool at all.
Name the task and the person
Describe the user, the input they have, and the decision or action they need to take. 'Help support agents find the right policy passage' is testable. 'Make support smarter' is not.
- What arrives as input, and where does it come from?
- What should the feature return or suggest?
- What must a person check before the result is used?
Collect examples before choosing the interface
Gather representative requests, documents, or records, including awkward cases. Agree on what a useful result would look like for each one. Check source quality and permissions before making those materials available to a feature.
A small evaluation set gives the team something concrete to review. It also exposes where a simple search, rule, or form may solve the task more reliably.
Make review part of the workflow
Decide how a user will see the source of an answer, spot uncertainty, correct a field, or reject a suggestion. For decisions with meaningful consequences, keep a person responsible for the final action.
A good first deliverable is a narrow prototype tested against the agreed examples, with the failures visible. That is more useful than a polished chatbot whose answers nobody can verify.