Mei Lin, Zhang and Dr. Rajiv, Menon and Michael, Anderson (2023) AI-Driven Predictive Maintenance Enhancing Reliability and Cost Efficiency in Enterprise IT Infrastructure. American Journal of Engineering, Mechanics and Architecture, 1 (10). pp. 376-386. ISSN 2993-2637

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Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	18 Sep 2025 07:00
Last Modified:	18 Sep 2025 07:00
URI:	http://eprints.umsida.ac.id/id/eprint/16351

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