Machine Learning Driven SAP DevOps Automation For Scalable Enterprise Software Delivery

This review article investigates the transformation of SAP software delivery through machine learning driven DevOps automation. As enterprises migrate to complex, multi-cloud architectures such as S/4HANA and the Business Technology Platform, traditional manual and threshold-based CI/CD pipelines fail to scale with the increasing frequency of changes. The research evaluates how ML models enhance the delivery lifecycle by introducing predictive risk assessment, intelligent test impact analysis, and self-healing deployment scripts. A primary focus is placed on the architectural evolution toward data-centric pipelines that leverage AI Core for real-time telemetry processing and ABAP code governance using large language models. The study further analyzes the operational impact of AIOps on progressive rollout strategies and automated root cause analysis within hybrid landscapes. Addressing critical challenges such as the \\\"cold start\\\" data problem and the necessity for explainable AI in regulated environments, the review concludes that the transition toward autonomous, \\\"zero-touch\\\" delivery is the essential roadmap for sustaining high-velocity innovation and industrial resilience in 2026.