Most failures are not model failures. They are workflow design failures: no owner, no KPI baseline, no approval architecture, and no evaluation path once the demo ends.
Production AI programs break when teams try to prove platform breadth before proving operational fit. The healthier sequence is narrow scope, measurable value, explicit controls, and then expansion. That is the difference between a good pilot story and a workflow that can survive security review, buyer scrutiny, and weekly operating variance.
The initial use case is too broad, so the team cannot define a realistic KPI stack or rollout boundary.
Governance is treated as a final approval step instead of a design constraint that shapes the architecture.
There is no release discipline, so every model or prompt update changes behavior without a measurable decision process.