Almost every organisation we meet has run an AI pilot. Very few have an AI system that a business owner depends on. The difference is rarely model quality.
Pilots stall for four repeatable reasons: nobody owns the process the AI is meant to change, accuracy was never defined as a measurable target, the integration into systems of record was treated as a later phase, and run cost was never modelled per resolved task.
Programmes that succeed invert this. They start with a process owner who wants a number to move, define an evaluation set from real cases before building, integrate into the tool where work already happens, and track cost per outcome from week one.
None of this requires a larger model. It requires treating AI as a production system with an owner, an SLA and a budget.