AI-Driven IT Operations and AIOps Enablement Across Hybrid Cloud Platforms

Abstract

The rapid adoption of hybrid and multi-cloud computing has significantly increased the complexity of modern IT environments. Enterprises now operate across on-premise data centers, private clouds, and multiple public cloud providers, creating challenges in monitoring, incident response, performance optimization, and operational efficiency. Traditional IT operations approaches rely heavily on manual processes and siloed monitoring tools, which struggle to scale with dynamic, distributed infrastructure

Artificial Intelligence for IT Operations (AIOps) has emerged as a transformative paradigm that applies machine learning, big data analytics, and automation to modernize IT operations. By leveraging real-time telemetry, predictive analytics, anomaly detection, and intelligent automation, AIOps enables organizations to move from reactive incident management toward proactive and autonomous operations

This article presents a comprehensive exploration of AI-driven IT operations across hybrid cloud platforms. It examines the evolution of IT operations, core AIOps architecture, data pipelines, observability strategies, and automation frameworks. The paper also discusses key use cases including predictive incident management, root cause analysis, capacity optimization, cost governance, and security operations. Implementation challenges, governance considerations, and best practices for enterprise adoption are analyzed. Finally, the article highlights future trends such as self-healing infrastructure, generative AI for operations, and autonomous cloud management

The goal of this research is to provide a structured, vendor-neutral framework that helps organizations understand how AIOps can improve reliability, reduce operational costs, and enhance service resilience in hybrid cloud ecosystems