how to choose an AI project
Choose an AI project by looking at four things together: the value of the decision or workflow, the readiness of its information, the consequence of a mistake, and whether someone owns the outcome. The best first project is rarely the flashiest one.
( The Detail )
Avoid the common false start
Do not start with a tool you want to try and then search for a use case. Start with a costly or slow operating moment, then decide whether a model, automation, data connection, or process change is actually the right answer.
How to approach it
Begin with the decision rather than the tool. Name the recurring judgement this affects, the information it depends on, and the person accountable for acting on the result. That framing keeps the first build small enough to inspect and useful enough to matter.
Keep a human review point in the loop until the quality and the failure modes are understood. A system that shows its working - what it drew on, where it is uncertain, and what it deliberately left alone - is one a business can keep running after the initial build.
( Next Step )
Start small enough to review, but on a workflow important enough to show whether a better system is worth building.