Predictive maintenance is the most oversold concept in manufacturing AI. Everyone talks about it. Few actually make it work.
The problem isn't the technology. It's the implementation.
Why Most Predictive Maintenance Fails
- Too many alerts: Operators get 50 warnings a day and ignore all of them.
- Wrong data: Sensors that don't measure what actually predicts failure.
- No integration: AI says "maintenance needed" but the work order system doesn't know.
- No trust: Maintenance teams don't believe the model because it's wrong too often.
What Works
1. Start With Your Most Expensive Failures
Don't try to predict everything. Focus on the 3-5 pieces of equipment whose downtime costs the most. Nail those first.
2. Use the Right Signals
Vibration, temperature, power draw — but only for equipment where those signals actually predict failure. Work with your maintenance team to identify what matters.
3. Integrate With Existing Workflows
AI predictions should create work orders in your existing CMMS. Maintenance techs shouldn't need to learn a new platform to see what the AI is telling them.
4. Measure Accuracy Relentlessly
Track false positives and false negatives. Tune the model. Build trust by being right more often than wrong.
The ROI
When it works, predictive maintenance reduces unplanned downtime by 30-50%, extends equipment life, and optimizes spare parts inventory. For a facility with $1M in annual downtime cost, that's $300K-$500K saved — year after year.