Intelligent Automation and Predictive Maintenance in Contemporary Software and Industrial Systems A Unified Theoretical and Empirical Synthesis of AI Driven DevOps and Industry Four Point Zero Paradigms

Abstract

The accelerating convergence of artificial intelligence, software engineering, and cyber physical production systems has created a historically unprecedented transformation in how modern organizations design, deploy, maintain, and evolve complex technological infrastructures. Within this convergence, two research streams have emerged as especially influential: AI driven DevOps in modern software engineering and predictive maintenance in Industry Four Point Zero environments. While these streams have traditionally been treated as distinct domains, both are fundamentally rooted in the same epistemological logic of continuous learning, automated decision making, and data driven system governance. This article advances the central thesis that AI driven DevOps and predictive maintenance are not merely parallel innovations but are manifestations of a deeper structural shift toward intelligent, self regulating socio technical systems. Drawing extensively on contemporary scholarship, particularly the integrative review of AI driven DevOps in software deployment and maintenance by Varanasi (2025), this study develops a comprehensive analytical framework that links machine learning enabled automation, lifecycle management theory, and cyber enabled industrial operations into a single unified paradigm. Through an interpretive and theory driven methodological approach, the research synthesizes findings from software engineering, logistics optimization, product lifecycle management, IoT based maintenance, and industrial analytics to demonstrate how intelligent automation reconfigures not only operational efficiency but also organizational power structures, risk management practices, and epistemic authority within engineering processes. The results show that AI driven DevOps and predictive maintenance systems converge around three foundational dynamics: the replacement of reactive intervention with anticipatory governance, the embedding of learning algorithms into organizational routines, and the transformation of human expertise from direct control to supervisory orchestration.