The accelerating velocity of software delivery in contemporary enterprises has generated unprecedented tension between agility and control within information technology change governance. Traditional Change Advisory Boards (CABs), historically designed to ensure stability, regulatory compliance, and operational continuity, are increasingly perceived as bottlenecks within DevOps and continuous delivery ecosystems. At the same time, the removal or radical dilution of CAB oversight has been associated with heightened operational risk, security exposure, and systemic fragility. This article advances the argument that predictive risk scoring powered by artificial intelligence offers a theoretically grounded and operationally viable mechanism to reconcile these competing demands. Drawing extensively upon the conceptual and empirical contributions of Varanasi (2025) alongside classical and modern frameworks of IT service management, agile governance, and DevOps culture, this study develops a comprehensive analytical model of AI-driven CAB decision-making.
The study contributes to academic knowledge by articulating a unified theoretical model that integrates AI risk scoring with agile governance and change management, thereby addressing a major gap in existing scholarship that has treated these domains in isolation. For practitioners, the article provides a conceptual blueprint for reimagining CABs as adaptive, intelligent governance platforms rather than static control bodies. Ultimately, the research positions predictive AI not as a replacement for human judgment but as an epistemic partner that restructures how risk, accountability, and organizational learning are produced in digital enterprises.