How to choose a predictive-maintenance platform that delivers decisions, not alarm fatigue — plus the six questions that separate real ML from dashboards.
Roughly 70% of predictive-maintenance projects fail — not because the data is missing, but because the tool floods teams with alerts no one can act on. The winning question in 2026 is not “how many alarms?” but “how many decisions?”
The market splits three ways. Sensor-led vendors (Augury, Tractian, SKF) sell hardware for rotating equipment. Enterprise suites (Siemens Senseye, IBM Maximo Predict) fit large multi-site groups with big teams. Vertical ML platforms run on the data you already have and suit mid-size manufacturers who want results without a data-science function.
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They generate alarm fatigue — too many alerts, no ranked action. Success comes from decisions an operator can act on.
Usually not. Modern vertical-ML platforms work with the sensors, PLCs and historians you already have.
Leading platforms reach a first production prediction in days to a few weeks, not a multi-month rollout.
Use the free ROI calculator to size annual savings from less unplanned downtime.
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