Start with a concrete work situation, not with a model choice. A strong first use case happens often, consumes meaningful time, and has a clear quality criterion.
Find the bottleneck
List processes where people search for information, transfer data, draft routine responses, or review documents. For each one, estimate frequency, the cost of errors, and the availability of source data.
Choose the most testable scenario, not the most impressive one. If you can compare time, quality, or completed volume, the pilot will give you an honest answer.
Limit the first scope
Do not automate the entire process at once. Define one input, one expected outcome, and the conditions under which the solution hands the task to a person.
A successful pilot is not a perfect demo. It is a clear decision about the next step.
Design measurement from day one
Before development, record the current baseline: time per task, where errors occur, and the share of cases that require manual handling. After launch, compare equivalent periods and request types.
This is how AI moves from an experiment to a controlled tool for business growth.