Your first AI project should be a useful one.
A practical framework for finding the right first use case.
Start with the work, not the model.
Look for a recurring task with a clear input, a clear output, and someone who can judge the result. Searching an internal knowledge base, categorizing requests, or extracting fields from a document can be better starting points than an open-ended assistant. The question is what work should become easier.
Define what good looks like.
Choose a baseline before building. That might be the time spent preparing a draft, the accuracy of an extracted field, or the share of answers backed by a useful source. Include the cost of review and correction. A fast answer that creates more work downstream is not a win.
Check the information and the boundaries.
Confirm that the information needed is accessible, current, and appropriate to use. Decide what the system may do, where a person must approve an action, and what happens when the system is uncertain. Those decisions are part of the product, not an afterthought.
Run a contained pilot.
Use representative examples and a small group of users. Compare the result with the existing workflow, record failure patterns, and decide whether to improve, expand, or stop. A pilot should answer a business question rather than merely demonstrate a technology.