Why AI pilots stall before production

A pilot usually starts with a model and looks for a problem. The ones that reach production start the other way round: with a process that costs real money, an owner who feels the pain, and a measure everyone agrees on before anything is built.

When we run Discover, we rank use cases on three questions. Does it pay back in this financial year? Is the data good enough today, or only in theory? Is there a person who will own the outcome once we step away? A use case that fails any one of them is a slide, not a seed.

The first point worth planting is rarely the most exciting one. It is the one with a clear owner, clean enough data and a number that moves. Get that one into production and the second use case gets easier, because the plumbing, the governance and the trust are already in the ground.