AI-Powered ITSM Automation: Enhancing service management efficiency through machine learning

The rapid advancement of Artificial Intelligence (AI) has significantly transformed IT Service Management (ITSM) by enabling automation, predictive analytics, and decision support systems. AI-powered ITSM automation leverages machine learning (ML), natural language processing (NLP), and deep learning techniques to enhance service efficiency, reduce operational costs, and improve user experience. Traditional rule-based ITSM models often fail to handle complex service requests and lack adaptability, leading to increased downtime and poor customer satisfaction. This paper presents a comprehensive review of AI-driven ITSM automation, analyzing various methodologies, challenges, and potential solutions. Key AI techniques, including supervised and unsupervised learning models, reinforcement learning, and generative AI, are explored in their application to incident prediction, anomaly detection, and service optimization. Despite recent progress, several challenges, such as data quality issues, ethical concerns, and integration complexities, remain unaddressed. This review highlights the critical research gaps and proposes future research directions aimed at further enhancing AI-driven ITSM systems. The findings provide valuable insights for researchers and IT practitioners looking to implement AI in IT service management.