The transformation of contemporary industrial systems into software-intensive, cyber-physical, and continuously evolving infrastructures has fundamentally altered the nature of maintenance, reliability, and operational governance. Traditional predictive maintenance emerged from mechanical engineering and operations research traditions that sought to anticipate physical component failure through degradation modelling, statistical inference, and condition monitoring. In parallel, modern software engineering has undergone its own transformation through the rise of DevOps and, more recently, AI-driven DevOps, where machine learning automates deployment, monitoring, testing, and self-healing of software systems. These two trajectories, although historically separate, are increasingly converging within Industry 4.0 environments in which physical assets, software platforms, data pipelines, and organizational workflows are deeply entangled. This article develops a comprehensive theoretical and methodological framework that integrates predictive maintenance models with AI-driven DevOps architectures to conceptualize a unified paradigm of algorithmic prognostics for industrial software-intensive systems.

Drawing on a broad range of literature on degradation modelling, Bayesian inference, Markov decision processes, neural networks, fuzzy logic, and ontology-based maintenance, the article demonstrates that predictive maintenance is no longer confined to the monitoring of physical assets but extends to the governance of entire digital–physical ecosystems. The review of AI-driven DevOps, particularly as articulated in contemporary research on intelligent automation for deployment and maintenance, provides the missing software-centric layer that enables predictive maintenance insights to be operationalized in real time within continuous delivery pipelines and autonomous system management. The study therefore positions AI-driven DevOps not merely as a software productivity tool, but as an infrastructural backbone for predictive maintenance in Industry 4.0.

A qualitative, integrative methodology is adopted to synthesize heterogeneous scholarly traditions into a coherent analytical framework. The results of this synthesis reveal that predictive maintenance accuracy, interpretability, and organizational effectiveness are significantly enhanced when prognostic models are embedded into AI-driven DevOps feedback loops. This allows maintenance policies to be dynamically updated, validated, and deployed in the same way that modern software updates are managed. The discussion elaborates the theoretical implications of this convergence, including the redefinition of failure, reliability, and accountability in cyber-physical systems, and critically examines the risks associated with algorithmic opacity and over-automation.

By situating predictive maintenance within a DevOps-enabled, AI-orchestrated lifecycle of continuous learning and intervention, the article offers a new conceptual foundation for understanding how Industry 4.0 organizations can achieve resilient, adaptive, and economically sustainable operations.

Predictive maintenance, AI driven DevOps, Industry 4.0, cyber physical systems

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