In most failed pilots, the problem lies in operational design before model accuracy. Even when technology is sufficient, missing data chains, ownership, and behavior change prevent lasting value.
The problem is often not defined clearly. “Let us use AI” is not a goal. The goal must be measurable — reducing repeat service, cutting stock wait time, or shortening decision cycles.
The data production chain is not established. When required data is missing, late, or unreliable, the model runs on assumptions. Data collection, validation, and ownership must be part of the first pilot scope.
User behavior is ignored. Even the best recommendation fails if it does not reach the right person at the right time. Interface, responsibility, and feedback mechanisms matter as much as the technical model. Outcomes must return to persistent event history — what was applied, what happened, and under which conditions it failed.