Research from leading management consultancies consistently identifies data quality as the primary inhibitor of industrial AI program success. In asset-intensive industries, the specific failure mode is predictable: AI models deployed on untrusted maintenance, material, and procurement master data produce unreliable recommendations that operations teams quickly learn to discount.
The pattern is particularly acute in MRO and spare-parts contexts. Predictive maintenance models incorporating parts availability signals perform significantly worse when the parts catalog contains duplicate records with split inventory positions.
The governance implication is clear: industrial AI adoption requires a data readiness standard, not a data transformation program. A bounded, diagnostic-first approach that quantifies the master-data problem before selecting technology platforms is consistently associated with faster AI adoption timelines and higher operational outcomes.
