Manufacturing organizations have dramatically increased capital allocation to predictive maintenance AI programs over the past three years. The operational promise — identifying equipment failure before it occurs and reducing unplanned downtime — is compelling. The reality for many early adopters has been more constrained.
The most consistent implementation finding is that predictive maintenance AI models incorporating parts availability and maintenance history data underperform when the CMMS and spare-parts catalog contain duplicate records with split inventory positions.
The data readiness investment required before predictive maintenance AI deployment is not a technology project. It is a catalog and maintenance-data quality program that can be scoped, executed, and validated independently of the AI platform procurement.
