Ninety-five out of every hundred enterprise AI pilots produce nothing a CFO would sign off on. The reflex is to blame the model — too dumb, too small, the wrong vendor. It almost never is. The thing quietly killing enterprise AI is older and more boring than any model: data nobody organized for machines, and rules nobody ever wrote down. The strangest part of the story is who is losing the fight hardest — the firms whose entire business is selling everyone else the cure.
The most expensive irony in enterprise software
In late 2025, Deloitte gave part of a government cheque back. The firm had delivered a report to Australia's Department of Employment and Workplace Relations, and reviewers found something awkward buried inside it: citations to academic papers that did not exist, and a fabricated reference to a federal court judgment. The work had been produced with help from generative AI, and no one had checked it before it went out the door. Deloitte agreed to refund part of its fee.
It is tempting to read that as a story about a hallucinating chatbot. It is not. A capable model can cite a real paper; the failure was not that the AI was too weak. The failure was that nothing in the process forced a human to verify machine output before it reached a client. There was no standard operating procedure, no checkpoint, no rule with teeth. That distinction — between a model problem and a data-and-governance problem — is the entire subject of this essay, and the firms that sell AI for a living have just handed us the clearest possible illustration of it.
