Most AI projects die in the demo. Here's how to ship to production.

It's never been easier to build an AI demo that wows a room. It's still hard to build one that runs unattended, handles the weird inputs, stays accurate as your data drifts, and doesn't quietly cost a fortune. That gap — demo to production — is where most AI projects stall.

The agents that make it to production do one well-defined job. 'Triage incoming support tickets' ships; 'be an AI assistant for the whole company' doesn't. A narrow scope is testable, and a testable system is one you can actually trust in front of customers.

Production AI is mostly the unglamorous scaffolding: evaluation against real examples, guardrails on inputs and outputs, a human-in-the-loop step where the cost of a mistake is high, and monitoring so you find out about drift before your users do.

Skip that and you don't have a product — you have a demo that happens to be deployed.

The fastest way to de-risk an AI build is a short pilot against your own data, in production conditions, before you commit to the full thing. If it works, you scale with evidence. If it doesn't, you found out in weeks instead of quarters.