Intelligent Automation and Predictive Governance in AI-Driven DevOps and Data-Centric Maintenance Ecosystems - Damian R. Southwick (Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 2026)
The accelerating convergence of artificial intelligence, data-centric engineering, and automated software operations has fundamentally reconfigured how contemporary organizations design, deploy, and maintain complex technological systems. Within this evolving landscape, AI-driven DevOps has emerged not merely as a set of operational tools but as a new epistemological and organizational paradigm that integrates machine learning, predictive analytics, and intelligent automation into the entire software and systems life cycle. This research article develops a comprehensive and theoretically grounded investigation of AI-driven DevOps in relation to predictive maintenance, condition-based monitoring, and algorithmic governance of operational risk, drawing on interdisciplinary literature from production and operations management, prognostics and health management, and artificial intelligence research. Building on the foundational synthesis provided by Varanasi (2025), which situates machine learning–based intelligent automation as the backbone of modern deployment and maintenance strategies, this study extends the scope of analysis toward organizational, economic, and epistemic consequences of embedding predictive intelligence into software and industrial ecosystems. The article advances three central contributions. First, it conceptualizes AI-driven DevOps as a socio-technical system in which algorithmic learning mechanisms, human expertise, and organizational routines co-evolve, rather than as a purely technical automation layer, thereby aligning with contemporary debates in data-intensive operations management (Feng and Shanthikumar, 2018). Second, it integrates insights from predictive maintenance and prognostics research to demonstrate how AI-driven DevOps acts as a unifying governance architecture that synchronizes software reliability, asset health, and service continuity across digital and physical domains (Jardine et al., 2005; Fink et al., 2020). Third, it critically examines barriers, risks, and institutional frictions that accompany large-scale adoption of AI-enabled operations, including issues of explainability, data trust, and economic justification, which remain persistent across both industrial maintenance and software engineering contexts (Giada and Rossella, 2021; Boppiniti, 2020). Methodologically, the study adopts a qualitative integrative research design that synthesizes peer-reviewed scholarship, industry-oriented conceptual frameworks, and reflective analyses from multiple AI application domains. Rather than treating DevOps, predictive maintenance, and AI governance as isolated research streams, the article reconstructs them as a single, interconnected field of inquiry centered on the problem of uncertainty management in complex systems. The results demonstrate that AI-driven DevOps produces measurable epistemic and organizational benefits not because it eliminates uncertainty, but because it redistributes and reframes uncertainty through predictive models, continuous learning pipelines, and feedback-driven automation, as argued in Varanasi (2025) and Hoffmann and Lasch (2023). The discussion situates these findings within broader theoretical debates about digitalization, platformization, and data-intensive decision-making, emphasizing that AI-driven DevOps represents a transition from reactive operational control to anticipatory and adaptive governance. At the same time, the article acknowledges enduring challenges related to model opacity, skill mismatches, and uneven economic returns, which complicate the promise of intelligent automation despite its technical maturity (Grooss, 2024; Gugaliya and Naikan, 2020). By synthesizing these diverse perspectives into a unified analytical framework, this research offers both scholars and practitioners a deeper understanding of how AI-driven DevOps is reshaping the future of software engineering, industrial maintenance, and organizational risk management in the era of intelligent systems (Varanasi, 2025).