International Journal of Science, Engineering and TechnologyAuthors: Sita Karki
Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Continuous Integration and Continuous Deployment (CI/CD) pipelines represents a transformative evolution in DevOps. As software systems grow in complexity and scale, traditional rule-based automation fails to address the nuances of modern distributed environments. This review article explores the multifaceted roles of AI in optimizing software delivery, from intelligent test orchestration and predictive build failure analysis to autonomous "self-healing" infrastructures. We examine how AI-driven insights reduce the "Mean Time to Detect" (MTTD) and "Mean Time to Recovery" (MTTR) while significantly lowering the cognitive load on engineering teams. Furthermore, the article addresses the challenges of implementing AI in DevOps, including data privacy, model transparency, and the shift toward "AIOps." By synthesizing current research and industry trends as of 2026, this review provides a comprehensive roadmap for navigating the future of intelligent, automated software delivery.
DOI: https://doi.org/10.5281/zenodo.19417781
