Many AI programs stall because the first project is chosen for novelty rather than fit. A clearer selection process helps leadership invest in work that can reach production.
The most useful AI initiatives usually sit close to an existing workflow. They have a decision owner, a measurable outcome, and a data path that can be governed. Ideas that only sound impressive in a briefing often fail those tests.
A simple scoring model is enough to start: business value, data availability, process stability, integration effort, and risk. The point is not to create a perfect formula. It is to make trade-offs visible so teams stop arguing from anecdotes.
Once a shortlist exists, design the smallest production path rather than the largest vision. A limited release with review, measurement, and a named owner teaches more than a broad pilot with no destination.