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Global Academic Journal of Economics and Business
Volume-8 | Issue-05
Original Research Article
AI-Enabled Predictive Maintenance for Improving the Resilience of Mission-Critical Data Center Infrastructure in Saudi Arabia
Waqas Ahmad
Published : Oct. 14, 2026
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.073
Abstract
Mission critical data centres require continuous power, stable thermal conditions, reliable sensing and disciplined maintenance. As Saudi Arabia ramps up its cloud, AI and digital service infrastructure under Vision 2030, the traditional approach of calendar based maintenance is increasingly inadequate for the operation of such tightly coupled, high availability facilities. This paper reviews the application of AI to enhance predictive maintenance and thus the resilience of mission critical data centre infrastructure in Saudi Arabia. A structured integrative review of the literature was undertaken (2020 to 2025) with a focus on data centre power and cooling assets, batteries, sensors, storage, digital twins, anomaly detection, fault diagnosis, remaining useful life estimation, transfer learning, explainability and maintenance prioritisation. The synthesis indicates that the greatest resilience value is achieved when the predictions are tied to asset criticality, redundancy state, intervention lead time, uncertainty and the existence of controlled operational workflows rather than simply as a standalone measure of prediction accuracy. The strongest evidence base is for battery health assessment, cooling system fault diagnosis, sensor validation, interpretable component failure prediction, transfer learning with limited fault data and digital twin enabled monitoring. The key remaining barriers are sparse failure labels, concept drift, site heterogeneity, imbalanced data, uncertain generalisability and poor integration of the analytical output into engineering governance. A proposed Saudi implementation framework would start with telemetry and asset criticality governance, move through a series of well defined use cases and model assurance and finish with a closed loop maintenance learning process. The paper concludes that AI enabled predictive maintenance can deliver a significant contribution to resilient digital infrastructure when it is deployed as an accountable engineering decision support capability with explicit human oversight, validation, cybersecurity and post intervention verification.

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