In contemporary IT organizations, Change Advisory Boards (CABs) are entrusted with ensuring that technical changes—ranging from infrastructure updates to software deployments—do not compromise operational stability. These boards traditionally rely on manual processes, expert judgment, and historical precedents to assess risk and approve changes. While effective in low-volume environments, this model begins to break down as the rate and complexity of changes increase in DevOps-driven ecosystems. The reactive nature of traditional CAB operations often leads to delays, inconsistent risk assessment, and suboptimal change approvals.

This research introduces a machine-learning-based system designed to augment CAB decision-making with automated, data-informed risk evaluations. By pulling data from multiple IT sources—including change records, deployment logs, historical incidents, and system health indicators—the proposed framework applies predictive analytics to compute a quantifiable risk score for each change. This score, combined with interpretable AI justifications, provides CAB members with clear, actionable insights. The goal is not to replace human oversight but to enhance it by directing attention to high-risk cases and accelerating the approval of safe, repetitive changes.

The paper outlines the architecture, data pipeline, feature engineering, model training methodology, risk scoring strategy, and interface integrations. Results from experimental trials demonstrate tangible benefits in incident reduction, review efficiency, and decision consistency. By embedding this system into ITSM workflows, organizations can reduce change failure rates and enhance service reliability while maintaining governance and compliance.