The Judgment Was Never in the Data

Every CMMS vendor is racing to bolt AI onto their platform right now. Predictive maintenance, anomaly detection, automated work order generation, take your pick. Most of the pitch decks sound the same: feed the machine enough sensor data and it'll catch what your technicians catch, eventually faster and more consistently than they do.

Here's the problem nobody's pricing in. The judgment call a 20-year technician makes when he says "that doesn't match what I'm hearing from that pump" never made it into a work order. It lived in his head, got passed down informally if you were lucky, and mostly walked out the door with him at retirement. The AI isn't being trained on that judgment. It's being trained on whatever got typed into the CMMS, which is usually "replaced, ran fine" and nothing else.

That's not a data gap you backfill later. It's a closing window. The generation that can still catch a model being confidently wrong is retiring faster than anyone's capturing what they know, and every one of them who leaves without that reasoning written down is a training set that never gets a second chance to exist.

None of this means AI doesn't belong in maintenance. It means the job isn't "replace the technician's judgment," it's "organize the evidence, remove the admin, and put the decision back in front of the person who can actually verify it." One CMMS founder put it to me plainly this week: the tool should make the technician smarter, not make the software look smarter. That's the whole test. If a feature doesn't pass it, the vendor doesn't need it, no matter how good it looks in a demo.

The plants that get this right aren't the ones adding the most AI. They're the ones treating the last decade of skilled technicians as a capture problem before it becomes an unsolvable one.