The demo is not the product
Almost anyone can wire an LLM to a prompt and get an impressive demo. The gap that kills AI initiatives shows up later: evals, guardrails, data quality, cost control, and the plumbing that turns a clever response into something a business can depend on.
Where it breaks
- No data foundation. The model is only as good as what it can retrieve. Messy pipelines and an undefined source of truth sink accuracy.
- No evals. Without success metrics defined up front, "it feels better" becomes the release criteria.
- No guardrails or observability. You cannot ship what you cannot measure or contain.
Crossing the gap
Start with the data. Define what done means with real evaluations. Add guardrails and observability from day one. That is the difference between an AI you demo and an AI you run.