Predictive Maintenance Beyond the Hype: Total Cost of Ownership Models for Industrial AI Deployments

Predictive Maintenance Beyond the Hype: Total Cost of Ownership Models for Industrial AI Deployments | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 April 2026 V1 Latest version Share on Predictive Maintenance Beyond the Hype: Total Cost of Ownership Models for Industrial AI Deployments Author : Ali Sadhik Shaik 0009-0004-8347-083X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177574488.83538065/v1 138 views 87 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (04 predictive maintenance beyond the hype_ total cost of ownership models for industrial ai deployments.docx.pdf) Download 228.54 KB Information & Authors Information Version history V1 Version 1 09 April 2026 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords asset management condition-based monitoring digital transformation industrial ai manufacturing technology predictive maintenance total cost of ownership Authors Affiliations Ali Sadhik Shaik 0009-0004-8347-083X [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 138 views 87 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ali Sadhik Shaik. Predictive Maintenance Beyond the Hype: Total Cost of Ownership Models for Industrial AI Deployments. Authorea . 09 April 2026. 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