Why most manufacturing AI pilots stall at POC, and how to design past it
A working demo is not a working deployment. The pilots that cross the gap share three habits, and they are all decided before any model is trained.
Most manufacturing AI pilots produce an impressive demo and then quietly die. The model hits 95 percent on a slide, the room nods, and six months later nothing has shipped to the floor. The failure is rarely the model. It is the setup around it.
Define success before you train
The pilots that survive write down a measurable target first: which defect, at what recall, compared to which human baseline, judged on which week of real production data. Without that, every result is arguable and the project drifts. A POC is not a science fair. It is a decision test with a pass mark agreed up front.
Start from one expensive problem
Broad platforms stall. The pilots that ship pick the single most expensive recurring problem on one line, and prove value there before touching anything else. Narrow scope is what makes the result believable, and believable is what gets budget for the next step.
The last habit is unglamorous: plan the handoff on day one. Who owns the model when the consultant leaves, where do the labels live, how is drift caught. A pilot designed without an operations owner is a pilot designed to be forgotten. Design the deployment, not the demo, and the gap mostly disappears.