AI pilots fail when teams skip operations. The model might perform well in isolated tests, but production behavior depends on data drift, latency budgets, and integration constraints.
A practical rollout sequence
- Define business outcomes before model choices.
- Add evaluation gates for quality and safety.
- Build deployment paths with rollback behavior.
- Instrument usage, latency, and error quality.
- Track cost per successful business action.
What usually goes wrong
Most failures are process failures, not model failures. Teams over-index on experimentation and under-invest in operational design. A better pattern is incremental release with visible metrics and clear ownership.
Field rule
If you cannot explain how the model is monitored, you are not production-ready.