ENHANCING IT INCIDENT MANAGEMENT EFFICIENCY THROUGH AI - DRIVEN PREDICTIVE ANALYTICS AND AUTOMATION

Abstract

In today's rapidly evolving digital landscape, organizations are increasingly dependent on robust and uninterrupted IT services. However, the escalating complexity of modern IT infrastructures, characterized by distributed systems, cloud environments, and a proliferation of applications, presents significant challenges to traditional, reactive IT incident management approaches. These conventional methods often lead to prolonged downtime, substantial operational costs, alert fatigue among IT staff, and a reactive posture that hinders proactive problem resolution. The inability to anticipate and prevent incidents before they impact business operations results in reduced productivity, diminished customer satisfaction, and potential financial losses. This MBA research project, conducted by HCLTech for Xerox Corporation, investigates the transformative potential of integrating Artificial Intelligence (AI)-driven predictive analytics and automation into IT incident management workflows. The study aims to demonstrate how this synergistic approach can revolutionize the efficiency and effectiveness of incident response, shifting the paradigm from reactive troubleshooting to proactive prevention and autonomous resolution. Through a comprehensive literature review, this research explores the core capabilities of AI, including machine learning algorithms for anomaly detection, failure forecasting, and intelligent root cause analysis. It also examines various automation technologies that streamline incident creation, triage, diagnostics, and enable self-healing capabilities. The proposed conceptual framework illustrates a holistic integration of these advanced technologies, encompassing data ingestion, an AI-driven predictive engine, an automation and orchestration platform, and an AI-augmented knowledge management system, all supported by a continuous human-in-the-loop feedback mechanism. This framework highlights how AI can provide early warnings and contextual insights, while automation can execute rapid, predefined actions, significantly reducing Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR). Key benefits identified include substantial cost savings through optimized resource utilization and prevention of costly outages, increased operational efficiency by liberating IT staff from repetitive tasks, enhanced IT service reliability, and improved customer satisfaction. While acknowledging challenges such as data quality, integration complexity, and skill gaps, the project provides practical recommendations for successful implementation, emphasizing a phased approach, investment in training, and fostering human-AI collaboration. Ultimately, this research concludes that AI-driven predictive analytics and automation are not merely technological enhancements but strategic imperatives for organizations like Xerox seeking to build resilient, cost-effective, and future-proof IT operations, thereby gaining a significant competitive advantage.