Enhancing predictive maintenance capabilities by integrating artificial intelligence: systematic review - Thippeswamy G. N; Neelambike S; Sanjay Pande M. B (Indonesian Journal of Electrical Engineering and Computer Science, 2026)
Organizations are under pressure to increase productivity and lower operating costs because facility operations and maintenance (O&M) account for a significant portion of a facility's life-cycle cost. By facilitating real-time monitoring and data-driven decision-making, artificial intelligence (AI) has become a promising catalyst for enhancing predictive maintenance. In order to investigate how AI can be combined with predictive maintenance to lower operational and maintenance overhead, this systematic review examines peer-reviewed studies that have been published in the last five years. Using an evidence-based review methodology and adaptive structuration theory (AST), the study synthesized results from 14 excellent publications. Unbiased maintenance planning, cost-effective resource utilization, and AI-enabled operational visibility emerged as three key themes. According to the review, AI-driven predictive maintenance greatly increases operational effectiveness and reduces costs; however, successful implementation necessitates better data governance and organizational preparedness.