Generative Artificial Intelligence in IT Operations: Architectural Transformation from Reactive to Autonomous Support Systems

Abstract

This article examines the paradigmatic shift catalyzed by generative artificial intelligence within information technology operations across enterprise environments. As organizations confront exponentially increasing complexity in their technological infrastructure alongside heightened demands for near-instantaneous issue resolution, traditional reactive support paradigms have proven fundamentally inadequate [1]. This research presents a comprehensive architectural framework for autonomous IT systems that can detect, diagnose, and remediate technical issues with minimal human intervention [4]. The proposed framework outlines a progressive evolution from rudimentary rule-based automation to advanced intelligent systems, which demonstrate the capacity for experiential learning from historical incident data and autonomous decision-making [5]. Empirical implementation data demonstrates that properly architected AI systems can autonomously resolve approximately 40% of common IT incidents while simultaneously reducing mean-time-to-resolution by up to 70% [8]. The research addresses intricate technical integration challenges, organizational resistance factors, and provides a structured implementation methodology with defined maturity stages [7]. Through transformation from reactive to autonomous support paradigms, organizations can achieve substantial operational cost reductions while concurrently enhancing service reliability and strategically redirecting technical expertise toward innovation rather than maintenance operations [10]. This architectural transformation represents a fundamental reconceptualization of enterprise technology management that transcends incremental improvement to deliver transformative operational capabilities.