Artificial Intelligence (AI) and Machine Learning (ML) are fundamentally transforming the landscape of Configuration Management (CM) within DevOps ecosystems. Traditionally, CM relied on static scripts and manual interventions to maintain system consistency, which often led to configuration drift and delayed deployments in large-scale environments. AI-driven configuration management introduces predictive analytics, automated drift detection, and intelligent remediation strategies that allow infrastructure to self-heal and scale dynamically. By integrating AI into the CI/CD pipeline, organizations can transition from reactive maintenance to proactive optimization. This review explores the convergence of AI and DevOps, focusing on how intelligent algorithms manage complex environmental variables, enhance security compliance, and reduce human error. The integration of AI not only accelerates delivery cycles but also ensures higher reliability by anticipating failures before they manifest. As cloud-native architectures become more intricate, AI-driven CM emerges as a critical necessity for maintaining operational excellence and achieving true continuous deployment.
Kiran Patel (2020) studied this question.