The demo isn't the hard part
Most AI pilots clear their first hurdle without much trouble. A model gets connected to some data, a prototype gets built, and it does something impressive enough to get budget approved for the next phase. That's usually where the momentum stops.
What actually stalls production
In our experience, pilots stall for a small number of recurring reasons: the workflow the pilot automated wasn't the one causing the most pain, the people expected to use it weren't involved early enough to trust it, or nobody defined what "success" meant in operational terms before building started.
None of these are technology problems. A pilot can use a well-chosen model and still fail to reach production if it was never anchored to a real operational priority.
What closes the gap
The pilots that make it to production usually share three things: they started with a workflow someone was already actively frustrated by, they involved the people doing that work from the first week rather than the last, and they had an agreed, measurable definition of success before a line of code was written.
None of this is exotic. It's closer to project discipline than machine learning expertise — which is exactly why it's easy to skip when the technology itself is the exciting part.
Where to start
If you're evaluating your own AI initiatives, the fastest diagnostic is to ask who specifically asked for this, and how you'll know if it worked. If either answer is vague, that's usually where the real work needs to happen before anything gets built.