Ai-Augmented DevOps: A paradigm shifts in scalable software engineering and IT operations

With respect to scalable software engineering in addition to IT operations, AI-enabled DevOps, also known as AI-augmented DevOps or AIOps, is a phenomenal transformation catalyst. Last but certainly not least, traditional DevOps had its share of very strong and effective automation processes and collaboration modalities; however, modern software systems continued to change: complexity grew rapidly, scalability, and immediacy were continuously increased, thereby creating challenges for traditional DevOps. Automation, prediction, and a self-recovery feature based on AI address some of the drawbacks mentioned with regards to intelligent decision making, anomaly detection, and resource optimization. It deals with the role that AI is expected to play in changing, revolutionizing, and transforming DevOps and continues even beyond that and into covering what will bring system reliability, rapid incident resolution, security, and compliance. However, the scenario is not closed without bringing in the obstacles which AI faces while entering DevOps, namely, integration complexities, data governance issues, and scalability issues. It develops a roadmap by compiling and reviewing the insights of existing research and industry practices on how organizations can approach using AI-powered DevOps for more resilient, efficient, and adaptive software engineering processes. This research is an important contribution to a process of continuous evolution in AI-driven automation discourse as it lays down guiding recommendations and future research directions towards optimizing DevOps in smart computing.