Leveraging Artificial Intelligence to bridge execution gaps in SAFe®-Scaled Agile based Programs

Large enterprises adopting the Scaled Agile Framework (SAFe®) often face challenges in Program Increment (PI) planning accuracy, objective backlog prioritization, timely risk detection, and manual compliance verification. We surveyed 12 teams across 10 Agile Release Trains (ARTs) to quantify these gaps. To address them, we propose an AI-augmented DevOps pipeline—built on common Infrastructure as Code tools—to integrate predictive analytics, natural language processing, reinforcement learning, and anomaly detection. Experimental results on enterprise projects show a 35 % reduction in PI-velocity forecasting error, 20 % faster backlog lead time, and 62.5 % quicker risk detection.