June 24, 2026 · 2 min read · Texas Integrated Services
The Predictive Maintenance Readiness Checklist Nobody Sells You
Predictive maintenance is the most promised and least delivered application in industrial AI. Run this readiness check before believing any savings estimate — including ours.
Every industrial AI vendor deck ends the same way: a machine, a dashboard, and a promise that failures will announce themselves in advance. Sometimes it is even true. Whether it can be true for you is answerable before spending anything on models — and the honest answer is often "not yet."
The readiness check
Sensor coverage. Are the failure modes you care about actually instrumented? Vibration, temperature, and current on the components that fail — not just the PLC data you happen to log.
History depth. Machine learning learns from examples. Two years of clean sensor history is a reasonable floor; six months is a science project.
Recorded failures. The uncomfortable one: a model that predicts failures needs recorded failures to learn from, with timestamps that line up with the sensor record. Well-maintained plants often lack exactly this.
Data plumbing. Can the history be exported, joined, and cleaned without a heroic project? If the answer involves three retired systems, budget for that first.
What to do if you are not ready
Not being ready is normal and fixable: instrument the critical failure modes now, start disciplined failure logging now, and let the data accumulate while your AI program earns its keep on text-heavy work — reporting, knowledge access, quoting. That is why predictive maintenance sits in the final phase of our roadmap, behind an explicit readiness audit.
A vendor who skips this check is not selling you a model. They are selling you their learning curve.