Why most enterprise AI projects fail before they ship

A demo proves the model can do the thing. Production proves it keeps doing the thing — under real load, real data, real scrutiny, and real consequences when it's wrong. Most enterprise AI dies in the gap between those two.

The demo trap

A demo runs on a happy path you control: clean inputs, a forgiving audience, no SLA. It's genuinely useful for buy-in — but it quietly skips everything that makes the system hard.

The demo is the easy 20%. The boring 80% — evals, guardrails, observability, rollback — is what keeps it alive.

Where they actually break

• No eval set. Teams tune prompts by vibes. Without a graded eval, you can't tell an improvement from a regression.
• Ungrounded answers. A model that confidently invents a policy number is worse than no model. Retrieval, citations, and "refuse if unsure" are non-negotiable in regulated work.
• Cost and latency discovered late. A flow that's fine for one user melts at a thousand. Budget both up front.
• No owner for failure. When it's wrong at 2am, who gets paged, and how do you roll back?

What shipping actually looks like

1. Write the eval before the prompt.
2. Ground every answer; cite or refuse.
3. Put it behind guardrails and observability.
4. Give it an SLA and an owner.

None of that is glamorous. All of it is the difference between a screenshot and a system.