Predictive maintenance (PdM) is the most frequently cited use case for Industrial AI, featured prominently in vendor marketing, industry conference agendas, and enterprise AI strategy documents. Yet the actual return on investment for predictive maintenance AI remains poorly understood, obscured by vendor-driven narratives that emphasize sensor cost reductions and downtime avoidance while systematically underreporting the true costs of deployment. This paper argues that the total cost of ownership for predictive maintenance AI is 2.5-4x higher than vendor projections typically suggest, and that the breakeven horizon is correspondingly longer than most capital expenditure approvals anticipate. Drawing on transaction cost economics and evidence from discrete manufacturing and process industry deployments, the paper proposes the PdM-TCO Framework-a five-stage total cost of ownership model specifically designed for predictive maintenance AI that captures costs across instrumentation, data pipeline construction, model development, operational integration, and continuous improvement. The framework identifies five categories of systematically underreported costs: data engineering infrastructure, change management for maintenance teams, false positive fatigue and its trust erosion effects, model retraining as equipment degrades, and organizational integration overhead. The paper further proposes the PdM Investment Justification Matrix, which identifies the conditions-asset criticality, failure consequence severity, data availability, and maintenance team readiness-under which predictive maintenance AI investment is genuinely justified versus conditions under which simpler condition-based monitoring delivers equivalent value at lower cost. Implications are developed for VP Operations evaluating PdM investments, Plant Managers implementing PdM programs, and CFOs approving capital expenditure for Industrial AI.
