Why AI PoCs fail to reach production

The demo worked. A model answered a question, drafted a ticket, or called a tool. Then the work stalled.

That stall is rarely “the model is not good enough.” It is the gap between a capability and a system.

What the PoC was allowed to ignore

A typical proof of concept is allowed to skip:

• Evaluation. A few happy-path chats, no held-out cases, no regression after prompt changes.
• Permissions. A personal API key, a shared admin token, or “we’ll tighten that later.”
• Data. A hand-picked corpus, no freshness, no access control, no lineage.
• Failure. Timeouts, tool errors, and wrong-but-confident answers are out of scope.
• Cost. Token math on a dozen examples, not retries, agents, or review load at volume.
• Operations. No owner, no rollback, no way to turn it off without a meeting.

None of that is visible in a screenshot. All of it shows up the week you try to put the thing next to a real workflow.

The usual failure modes

The workflow was never valuable enough. The demo answered a question people already knew how to answer. There was no time saved, no risk reduced, no bottleneck moved. Production has a cost of ownership; a curiosity does not pay it.

The architecture was a chat window. Production work is a pipeline: intake, retrieval or tools, deterministic checks, human approval, write-back. An LLM in the middle of that is a component. A chat UI is not the system.

Nobody could say what “correct” meant. If you cannot score a run without arguing, you cannot ship a change. Teams then freeze the prompt and hope.

Integration was treated as a later phase. Identity, audit logs, change management, and the system of record were “phase two.” Phase two is where the project dies.

What to do instead

1. Name the workflow and the decision or action it changes.
2. Decide which steps must be deterministic (policy, schema, permissions) and which may be probabilistic.
3. Write twenty ugly real cases before you tune the prompt.
4. Price the operating loop: tokens, tools, retries, and human review.
5. Require an owner and a kill switch before you call it a pilot.

If you cannot do those, you do not have a production candidate. You have a demo.

The useful question is not “can an LLM do this?” It is “what surrounding system would make this dependable in our environment — and is that system worth building?”