From PoC to Production: The AI Gap Nobody Budgets For
What the demo didn’t show you
A successful proof-of-concept is a trap if you mistake it for a finished system. The PoC answers "can this work?" Production answers "will this keep working, safely, at cost, for real users?" — and that second question is where budgets quietly blow up.
The four line items PoCs skip
1. Evaluation you can defend. You need a repeatable eval set tied to a business KPI, not vibes. Regressions must be catchable before they ship.
2. Integration with systems of record. Reading and writing production data with correct permissions, audit logging, and failure handling is most of the real work.
3. Monitoring and guardrails. Drift, hallucination rates, latency, and spend all need live observability. Silent degradation is the default failure mode of AI systems.
4. Cost control. Token spend scales with usage in ways a PoC never reveals. Caching, routing to cheaper models, and batching are production concerns from day one.
A pragmatic path
Scope the production build as its own phase with its own budget. Instrument evaluation first, integrate against real systems second, and only then optimize cost and expand scope. Ship one narrow use case fully before widening — a system in production teaches you more than three more prototypes.
The teams that win treat "PoC to production" as the main event, not the victory lap.