Start with a workflow, not a model
A useful AI project begins with a specific piece of work. What information arrives, who handles it, and what decision follows? Map the process before deciding where an AI system belongs. Sometimes a straightforward rule or a better form solves the problem more reliably.
Define what a good result looks like
Create a small set of realistic examples and write down what success means. Accuracy, time saved and the effort required to correct a mistake are different measures. Decide which matters most, and compare the proposed system against the way the work happens today.
Keep people in the loop
Design review and escalation into the workflow. People need to understand when a result is uncertain and how to correct it. Begin with a limited pilot, observe the exceptions and expand only when the evidence supports it.