AI projects that actually get used
An AI project is more likely to be used when it improves a real recurring moment in the business and a named person is responsible for acting on the result. The project must fit the team’s current systems and working rhythm rather than ask people to create an entirely new one.
( The Detail )
Design for trust and handover
Show the source information, make uncertainty visible, and give people a way to correct or challenge the output. A system that can be inspected and maintained is more valuable than an impressive demo that only its builder understands.
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.