AI-Driven Predictive Maintenance in Retail IT Systems Using DevOps: A Review

Retail IT infrastructure needs to be extremely reliable and highly available to provide seamless operations, best customer experience, and effective resource utilization. Predictive maintenance driven by Artificial Intelligence (AI) and natively integrated with DevOps practices has become a game-changing solution in this regard. This paper provides an extensive review of 40 recent research articles on AI-DevOps convergence for predictive maintenance in retail IT infrastructures. A systematic literature review methodology was adopted, involving structured search, selection, and thematic analysis of peer-reviewed studies from 2019 to 2026. Some of the prime subjects discussed include common DevOps practices in retail IT, using AI for predictive analytics assistance, and merging machine learning models with failure prediction. Techniques for anomaly detection and pattern recognition in favour of early detection of possible problems are given special attention. Also, merging AI insights with DevOps pipelines is discussed in terms of automated feedback loops, CI/CD optimization, and real-time monitoring of system health. The review also addresses open issues like data quality, model drift, and integration complexity, and explores growing trends in self-healing systems and AIOps. This research aims to offer researchers and practitioners an extensive review of state-of-the-art techniques and their probable contribution to increased operational resilience and cost-effectiveness in retail IT systems through smart, automated maintenance practices.