Predictive Maintenance Begins With Failure History

Johnson Controls reported that 67% of surveyed facilities teams were using AI and 61% planned to expand its use; its public page states that full methodology is in the downloadable report.[9]

Analysis

Predictive maintenance requires more than sensor volume. Asset identity, operating context, failure labels, maintenance quality and intervention outcomes must be dependable.

Osmos Global analysis: the first maturity test is whether the organisation can reconstruct why a critical asset failed and what action restored performance.

Leadership implication: cleaning the maintenance history may create more near-term value than purchasing another analytics layer.

Decision lens

Recommended action. Validate asset hierarchy and failure coding for one critical system before launching predictive models.