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International Journal of Next-Generation Engineering and Technology

Open Access Peer Review International
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From Reactive IT to Cognitive Operations: The Evolution of AI-Driven DevOps in Large-Scale Software Systems

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Xavier P. Lockwood 1 ,
4 National University of Science and Technology, Russia

Abstract

The rapid evolution of software engineering has necessitated novel approaches to deployment, maintenance, and operational management, driving the integration of artificial intelligence (AI) with DevOps practices. AI-driven DevOps, often encapsulated under the umbrella of AIOps, provides a framework for predictive analytics, anomaly detection, and intelligent automation that significantly enhances software reliability, scalability, and operational efficiency. This research offers an exhaustive exploration of AI-empowered DevOps environments, synthesizing contemporary literature, empirical findings, and theoretical constructs to establish a coherent understanding of the interplay between AI, machine learning, and operational technology in software ecosystems. We examine the historical evolution of DevOps, contextualize the emergence of AIOps within IT operations, and analyze the practical and theoretical implications of AI-based interventions in system monitoring, incident management, and predictive maintenance. The study systematically critiques existing methodologies, highlights operational bottlenecks, and articulates the nuanced challenges of implementing machine learning models in real-world IT environments. Special attention is given to the ethical, governance, and reliability considerations inherent in autonomous systems, while the discussion extends to strategic decision-making, risk mitigation, and continuous improvement in software lifecycles. By integrating insights from Varanasi (2025) with broader scholarly discourse, this work bridges the gap between conceptual frameworks and applied AI-driven operational strategies. The findings underscore the transformative potential of AI in enhancing DevOps workflows while emphasizing the need for rigorous methodological approaches, robust model governance, and context-sensitive deployment strategies to ensure sustainable and secure operational practices. This research contributes to the academic dialogue on intelligent automation by offering a multi-dimensional analysis that encompasses technical, managerial, and policy-oriented perspectives, serving as a comprehensive reference for researchers, practitioners, and policymakers engaged in next-generation software operations.

Keywords

Credit risk prediction, machine learning, profit-based scoring, financial decision-making

References

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