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This study takes a mixed-methods approach to explore the impact of AIOps on IT Service Management process. Modern IT operations face unprecedented challenges in managing increasingly complex, distributed systems. This paper examines how Artificial Intelligence for IT Operations (AIOps) transforms traditional IT Service Management (ITSM) from reactive firefighting to proactive, intelligence-driven operations. This comprehensive framework demonstrates how organizations can implement AIOps across the Incident and Change management lifecycle to achieve quantifiable improvements in key performance metrics. Additionally, AIOps enables contextual enrichment of incidents, automated remediation workflows, and data-driven change risk assessment. This paper presents a maturity model for AIOps adoption, identifying critical success factors and implementation strategies that enable organizations to realize maximum ROI. Through analysis of implementation case studies across diverse industries, we document significant enhancements in operational efficiency, including average reductions of 73% in detection time and 62% in resolution time. Our findings illustrate how AIOps systematically addresses the fundamental challenges of modern IT environments by creating resilient operations that can anticipate and prevent disruptions before they impact users