Pilots fail at the engineering layer — integration, governance, evaluation, and ownership. Here is what we see across enterprise deployments.
Most enterprise AI initiatives do not fail because the model is wrong. They fail because nobody engineered the path from demo to production.
The engineering gap
Pilots run on clean data with manual overrides. Production requires ERP integration, permission-aware retrieval, cost controls, monitoring, and a team that owns the system after launch.
Production-readiness criteria
We assess existing pilots against production-readiness criteria: integration contracts, evaluation baselines, security review, observability, and operational runbooks. The output is a clear scale, pivot, or sunset recommendation — then execution.
What to do next
If your board is asking why last year's AI pilot never shipped, the answer is usually engineering — not ambition.