Predictive maintenance sounds straightforward: collect sensor data, train a model, and get a warning before a machine fails.

But getting a model to work in a pilot is one thing. Getting a maintenance team to trust it on the plant floor is another.

A few things that can go wrong:

1. Choosing the AI model before identifying the failure that actually matters.
2. Underestimating how messy industrial sensor data can be.
3. Training on failure records that are incomplete or unreliable.
4. Sending alerts without involving the people who operate the equipment.
5. Treating deployment as the finish line instead of monitoring performance over time.

The fourth one is especially interesting. Even a technically good model has little value if operators stop paying attention to its warnings.

I think starting with one asset, one failure type, and a measurable outcome is a much better approach than trying to predict every possible failure from day one.

For those who have worked on predictive maintenance:

What was the biggest challenge in your project? Was it the data, the model, the false alarms, or getting people to actually use the system?