Most GenAI proofs of concept look impressive in a demo and never make it past that stage. The gap between "it works in a demo" and "it runs reliably in production" is where most initiatives stall.
The three gaps that matter most
- Observability — if you can't trace why a model produced a given output, you can't debug it, and you can't govern it.
- Cost discipline — token costs at demo scale are trivial. At production scale, they are a line item the CFO will ask about.
- Guardrails — a model that behaves well 95% of the time still fails in front of the 5% of users who matter most.
Closing these gaps is less about better models and more about better engineering discipline around the models you already have.