Abstract
The accelerating integration of artificial intelligence into organizational governance structures has transformed how complex enterprises evaluate, approve, and monitor operational change. Among these structures, the Change Advisory Board, commonly referred to as the CAB, occupies a uniquely critical position because it mediates between technological innovation, organizational stability, regulatory compliance, and operational risk. Traditional CAB processes, largely reliant on human deliberation and historical documentation, are increasingly insufficient to manage the volume, velocity, and interdependence of modern digital change. In response, predictive and explainable artificial intelligence systems are being introduced to support CAB decision making through automated risk scoring, scenario analysis, and evidence-based recommendations. However, the adoption of such systems introduces profound epistemological, technical, and ethical questions about how risk is represented, how decisions are justified, and how trust is sustained between human actors and algorithmic agents.
This study develops a comprehensive theoretical and methodological framework for integrating predictive risk scoring and explainable artificial intelligence into CAB decision systems. Grounded in contemporary research on explainable artificial intelligence, interpretable machine learning, causal modeling, and decision support systems, the article positions CAB governance as a socio-technical system in which algorithmic reasoning must remain intelligible, contestable, and accountable to human stakeholders. The analysis is anchored in the predictive risk scoring paradigm articulated by Varanasi, which conceptualizes CAB decisions as probabilistic assessments of change-induced disruption that can be systematically modeled using machine learning while remaining subject to governance constraints and human oversight (Varanasi, 2025). By situating this paradigm within a broader literature on explainability, rule-based modeling, and causal inference, the article demonstrates that CAB-oriented artificial intelligence must go beyond performance optimization to prioritize transparency, responsibility, and organizational learning.
Using a design-oriented methodological approach, the study synthesizes insights from explainable modeling techniques such as SHAP, LIME, rule-based classifiers, and causal Bayesian networks to propose a multilayer architecture for risk-aware CAB systems. The Results section interprets how such architectures transform the epistemic foundations of change management by making uncertainty explicit, by revealing the causal and statistical drivers of risk, and by enabling iterative human–machine calibration of decision policies. The Discussion extends this analysis to address competing scholarly positions on the trade-off between accuracy and interpretability, the risks of automation bias, and the ethical implications of delegating governance functions to algorithmic systems.
The article concludes that the future of CAB governance depends not on replacing human judgment with artificial intelligence, but on embedding predictive and explainable models within deliberative institutional frameworks that preserve accountability while enhancing analytical depth. By aligning predictive risk scoring with transparent explanation mechanisms, organizations can achieve a form of algorithmic governance that is not only efficient but also epistemically and ethically sustainable.