From Reactive ITSM to Predictive Incident Prevention Using AI

Authors

  • Nayan Kumar Sureshbhai Patel

Keywords:

AIOps, anomaly detection, enterprise resilience, IT service management, predictive incident prevention

Abstract

Traditional IT service management (ITSM) relies on a reactive operating model in which operations teams respond to failures after end users are already affected. This approach is increasingly insufficient in cloud-native, distributed, and always-on enterprise environments where the cost of delayed incident response includes downtime, reputational damage, and compounding operational waste. This article examines how artificial intelligence transforms ITSM from reactive response to predictive incident prevention through four core capabilities: anomaly detection, failure prediction, risk scoring, and proactive automation. Drawing on a structured review of 22 sources spanning AIOps, machine learning operations, enterprise integration, and sustainable computing, the article proposes a unified framework that connects AI-driven prevention capabilities to the six architectural patterns, microservices decomposition, event-driven integration, containerization, orchestration, caching, and observability, that are required to sustain them in production. The article presents three tables and three figures that structure the reactive-to-predictive transition as a maturity model, detail the AI capability stack, and map architectural contributions to sustainability outcomes. Findings indicate that predictive ITSM delivers measurable resilience improvements and sustainability co-benefits when implemented within an architecturally disciplined platform. The article concludes with governance requirements for automated decision-making and directions for future empirical research.

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Published

31.08.2026

How to Cite

Nayan Kumar Sureshbhai Patel. (2026). From Reactive ITSM to Predictive Incident Prevention Using AI. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 2367 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8541

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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