AI-Driven Predictive Maintenance: Enhancing Reliability and Cost Efficiency in Enterprise IT Infrastructure (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2023)
Enterprise IT infrastructure—spanning data centers, cloud platforms, and mission-critical networks—faces mounting pressures from escalating workloads, cybersecurity risks, and stringent uptime requirements. Traditional maintenance strategies, whether reactive or preventive, often lead to costly downtimes, resource inefficiencies, and compliance risks. Recent studies estimate that unplanned IT downtime costs enterprises over $5,600 per minute, while nearly 60% of outages could be anticipated with predictive insights. This paper explores the role of AI-driven predictive maintenance in transforming IT operations by shifting from static monitoring toward proactive, data-driven reliability engineering.The proposed approach integrates machine learning models, anomaly detection, and time-series forecasting to monitor hardware health, application performance, and network reliability. By leveraging telemetry data from servers, storage arrays, power and cooling systems, and hybrid cloud environments, predictive models can identify early warning signals such as latency drifts, CPU/GPU overheating, disk I/O degradation, and abnormal energy consumption. Advanced techniques—including deep learning for multivariate sensor fusion, reinforcement learning for dynamic resource scheduling, and edge-AI for localized anomaly detection—are applied to optimize both performance and cost.A case study of a global BFSI enterprise with 25,000+ servers and 50 PB of data assets demonstrates tangible outcomes: a 40% reduction in unplanned outages, 25% lower infrastructure maintenance costs, and improved compliance with ITIL, ISO 27001, and SOC 2 frameworks. Additionally, predictive maintenance enabled sustainable IT operations, cutting energy waste by 18% through proactive cooling system adjustments.Findings reveal that AI-driven predictive maintenance not only enhances system reliability and operational resilience, but also delivers significant financial and sustainability value for enterprises. Beyond technical gains, it strengthens business continuity, customer trust, and regulatory alignment. The paper concludes with a roadmap for adopting predictive maintenance at scale, highlighting cloud-native monitoring pipelines, explainable AI for auditability, and integration with IT service management (ITSM) platforms as critical enablers for future enterprise IT ecosystems.