Integration of Artificial Intelligence in Maintenance Management: A Comparative Analysis With Traditional Approaches

This paper explores the integration of Artificial Intelligence (AI) in maintenance management, contrasting its effectiveness with traditional methodologies such as Failure Mode, Effects, and Criticality Analysis (FMECA), Risk-Based Inspection (RBI), and Condition-Based Inspection (CBI). Through industry case studies and empirical data, we demonstrate that AI-enhanced systems deliver significantly superior performance in predictive accuracy, cost efficiency, and reliability. Results show that AI implementations can improve failure prediction by 25–40%, reduce maintenance costs by up to 30%, and lower unplanned downtime by as much as 50%. These findings underscore the transformative impact of AI on maintenance strategies, especially in complex, high-stakes industries such as oil and gas.