The rapid acceleration of cloud-native software delivery, microservice-oriented architectures, and continuous integration and deployment pipelines has fundamentally altered the operational fabric of modern software engineering. Organizations today no longer struggle only with writing correct code but with governing, monitoring, and evolving large-scale, continuously changing software ecosystems. Within this environment, the convergence of Artificial Intelligence for IT Operations and AI-driven DevOps has emerged as one of the most consequential paradigms of contemporary digital infrastructure management. This article develops a comprehensive theoretical and empirical synthesis of how AIOps and AI-driven DevOps jointly reshape software deployment, reliability engineering, and operational decision-making. Building on machine learning-based automation for deployment and maintenance articulated in the recent synthesis of AI-driven DevOps (Varanasi, 2025), this study integrates broader AIOps research on anomaly detection, log analytics, tracing, governance, and predictive reliability engineering into a single unified analytical framework.

The article positions AIOps not as a standalone toolset but as a socio-technical intelligence layer embedded within DevOps pipelines. Drawing on the extensive literature on log-based anomaly detection, time-series modeling, distributed tracing, and failure prediction, it argues that AI-driven DevOps represents a shift from reactive, human-centered operations toward proactive, data-centric and semi-autonomous operational governance. This transformation is examined historically, theoretically, and methodologically. Historically, the work situates AIOps within the evolution from traditional system administration to automated operations and continuous delivery. Theoretically, it draws on systems theory, reliability engineering, and organizational learning to conceptualize how machine learning alters the epistemology of operational knowledge. Methodologically, it develops a structured qualitative-analytical synthesis of prior empirical studies, surveys, and industrial case analyses.

Ultimately, the article contributes a comprehensive, integrative theory of AI-driven DevOps as a foundational pillar of modern software engineering. It proposes that future cloud enterprises will increasingly rely on algorithmic operational intelligence not merely to keep systems running, but to actively shape how software is designed, deployed, and evolved over time, consistent with the emerging evidence from AIOps research and industrial practice (Dang et al., 2019; Gulenko et al., 2020; Varanasi, 2025).

AIOps, AI-Driven DevOps, Cloud-Native Systems, Log Analytics, Operational Intelligence

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