Authors: Pooja Verma
Abstract: This review article investigates the integration of artificial intelligence and machine learning into SAP DevOps pipelines to achieve autonomous quality assurance and operational monitoring. As enterprises migrate to cloud-native architectures such as SAP S/4HANA and the Business Technology Platform, the traditional manual and threshold-based oversight of software delivery is increasingly insufficient. The research evaluates how AI-driven methodologies transform the continuous integration and delivery lifecycle by introducing self-healing test automation, risk-based test scoping, and synthetic data generation. Central to the discussion is the role of AIOps in replacing static monitoring with dynamic anomaly detection and automated root cause analysis, which allows for proactive self-healing of distributed cloud environments. The study also analyzes the operational impact of these technologies on accelerating time-to-market, optimizing cloud resource costs, and enhancing the stability of mission-critical business processes. Furthermore, the paper addresses implementation challenges, including data quality, explainable AI for regulated industries, and the convergence of specialized engineering skills. The review concludes that the transition toward agentic DevOps and autonomous infrastructure is a strategic necessity for organizations seeking to maintain agility and resilience in complex multi-cloud enterprise landscapes.
DOI: https://doi.org/10.5281/zenodo.19427831
