Begin Here: How predictive maintenance pilots actually deliver value

Many teams hear that "AI can predict failures" and think the hardest part is the model. In practice, the hardest part is making predictions useful, trustworthy, and actionable for operations. This research project exists to help maintenance and operations leaders choose the right use cases, verify data readiness, design minimally viable pilots, and set governance so pilots reduce downtime without creating noise, false confidence, or wasted effort.

Who this helps

This resource is for frontline maintenance supervisors, reliability engineers, operations leaders, plant managers, and technical teams who want a practical path from idea to an operational pilot. It’s intended for small-to-midsize facilities and teams as well as larger organizations that prefer targeted, measurable experiments over broad, unfunded promises.

What you can do next

1. Take a quick data & readiness check (use the Data & Readiness Assessment in this project).
2. Use the Pilot Plan Template to capture a clear hypothesis, success metrics, and owners.
3. Run a short, focused pilot on a small set of assets where failures are meaningful and actions are clearly defined.

Why this approach matters

Too many pilots fail because they chase broad ambition (detect any failure) rather than a narrow, valuable outcome (reduce unplanned downtime of a critical motor by X% in 12 weeks). Narrow goals make it easier to pick signals, design validation methods, and align operations to act when alerts appear.

Continue with the practical guide for scoping pilots or jump directly into the Data & Readiness Assessment to see how close you are to running a viable experiment.