Resources · 2 min read
Why AI pilots die before production
The demo impresses, the pilot gets approved, and six months later nobody uses it. It's not the model: it's the engineering missing around it.
The pilot doesn't fail because of the model
Today's models are more than enough for most business cases. When a pilot dies it's almost never because the AI “wasn't good enough”: it's because the system around the model never existed. The demo ran on a three-month-old data extract, answered on a channel the operation doesn't use, and nobody with a first and last name was responsible for keeping it truthful.
The three typical deaths
First: it never got integrated — the agent answered from a copy of the catalog, not from the system the operation actually consults, and the day they disagreed, the system won. Second: nobody maintains it — the catalog changed, the rules changed, the model changed underneath, and the bot kept selling the business of six months ago. Third: nobody measures whether it tells the truth — without a scenario suite that runs before every change, every improvement is a gamble.
What changes when you design for production
Production is not “the demo, but public”. It's a different list of requirements: data comes from the live system, not an export; guardrails decide what happens when the agent doesn't know; a human approves the sensitive; and health, cost and quality are monitored with alerts going to a person. None of that gets bolted onto a demo afterwards — it's designed in from day one.
How to tell if yours is going to die
Three questions: does the agent query the live system or a copy? Is there a written list of what it does when it doesn't know the answer? Can anyone tell you today how many answers it gave yesterday and how many it escalated? If two of the three have no clear answer, the pilot isn't headed to production — it's headed to a drawer. The good news: all three are fixed with engineering, not with a more expensive model.
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