The Total Cost of AI Error: A Cost-Accounting Framework for Enterprise AI Deployment

Enterprise adoption of artificial intelligence has produced measurable task-level productivity gains, yet corresponding improvements in firm-level operating margin remain difficult to identify. We propose that this productivity-margin gap is the consequence of an unmeasured cost category we term the Total Cost of AI Error (TCAE). TCAE comprises three components: a Verification Tax that captures the labor reallocated from production to audit; a Containment Cost that captures detection, isolation, and remediation of AI-driven incidents; and a Liability Premium that captures expected regulatory and reputational exposure. We derive each component from formal sub- models, anchor calibration ranges to published empirical evidence, and extend the framework with scenario-weighted expectation and Monte Carlo sensitivity analysis. We illustrate the model with a worked example calibrated to mid-size US bank holding companies, using the operational-loss findings of McLemore and Mihov (2026) as the empirical anchor for the Containment Cost sub- model. The illustrative TCAE for the case institution exceeds 35% of gross AI-attributable labor savings under central-tendency assumptions and exceeds 100% under the worst-case scenario weighting, suggesting that current enterprise AI business cases may systematically overstate net economic value. We discuss implications for governance investment, model calibration discipline, and the design of regulatory reporting frameworks.