The accelerating digitization of enterprises has fundamentally transformed how organizational change is initiated, evaluated, approved, and governed. Change Advisory Boards, traditionally designed as expert-driven governance mechanisms for assessing technological and operational changes, now face unprecedented complexity, uncertainty, and interdependence driven by digital platforms, cloud infrastructures, cyber–physical systems, and globally distributed supply chains. In this environment, conventional qualitative and checklist-based change management practices are increasingly insufficient for anticipating cascading risks, financial exposure, and systemic failures. This article develops a comprehensive theoretical and analytical framework for Artificial Intelligence–driven predictive risk scoring in Change Advisory Board decision making, synthesizing insights from enterprise risk management, financial risk analytics, supply chain resilience, and machine learning research. Drawing on the emerging paradigm of algorithmic governance, the study situates AI-based CAB decision support as a critical extension of ISO 31000 risk management principles, COSO internal control frameworks, and enterprise IT governance models. Particular emphasis is placed on the predictive risk scoring approach articulated by Varanasi, which conceptualizes CAB decisions as probabilistic risk optimization problems that can be learned, updated, and refined through historical change data and continuous organizational feedback loops (Varanasi, 2025). The article advances a multidimensional interpretation of CAB risk that integrates technical, financial, operational, compliance, and reputational dimensions into a unified predictive architecture. Through extensive theoretical elaboration and comparative analysis with supply chain risk management, financial default prediction, and intelligent early warning systems, the study demonstrates how machine learning–driven risk intelligence enables a shift from reactive change governance toward anticipatory, data-informed, and resilience-oriented decision making. The findings suggest that AI-enhanced CABs not only improve approval accuracy and reduce failure rates but also create new forms of organizational learning, strategic foresight, and governance transparency. However, the article also critically examines ethical, epistemological, and institutional limitations associated with algorithmic risk scoring, including bias propagation, model opacity, and over-reliance on automated judgment. By situating CAB predictive analytics within broader debates on digital governance and enterprise resilience, this work contributes a foundational scholarly framework for understanding how AI reshapes the future of organizational change management in complex socio-technical systems.

Artificial intelligence, Change Advisory Board, predictive risk scoring,, enterprise governance

Song, Y., Du, H., Piao, T., and Shi, H. Research on financial risk intelligent monitoring and early warning model based on LSTM, transformer, and deep learning. Journal of Organizational and End User Computing, 36(1), 1–24.

De Haes, S., and Van Grembergen, W. Enterprise Governance of Information Technology. Springer.

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., and Verma, S. Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism. Annals of Operations Research, 1–26.

Lander, G. P. The Sarbanes–Oxley Act of 2002. Journal of Investment Compliance, 3, 44–53.

Varanasi, S. R. AI for CAB Decisions: Predictive Risk Scoring in Change Management. International Research Journal of Advanced Engineering and Technology, 2(06), 16–22. https://doi.org/10.55640/irjaet-v02i06-03

Baryannis, G., Validi, S., Dani, S., and Antoniou, G. Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57, 2179–2202.

Anand, M., Velu, A., and Whig, P. Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering, 3(1), 1–13.

Hegde, J., and Rokseth, B. Applications of machine learning methods for engineering risk assessment. Safety Science, 122, 104492.

COSO. COSO Internal Control Integrated Framework Frequently Asked Questions. Committee of Sponsoring Organizations of the Treadway Commission, 6, 1–11.

Karasan, A. Machine learning for financial risk management with python. O Reilly Media.

Giannakis, M., and Louis, M. A multi agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29, 706–727.

Ho, W., Zheng, T., Yildiz, H., and Talluri, S. Supply chain risk management: A literature review. International Journal of Production Research, 53, 5031–5069.

Arora, S., Bindra, S., Singh, S., and Nassa, V. K. Prediction of credit card defaults through data analysis and machine learning techniques. Materials Today Proceedings, 51, 110–117.

Paltrinieri, N., Comfort, L., and Reniers, G. Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486.

Ivanov, D., Dolgui, A., and Sokolov, B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57, 829–846.

Suhadolnik, N., Ueyama, J., and Da Silva, S. Machine learning for enhanced credit risk assessment. Journal of Risk and Financial Management, 16(12), 496.

Fan, Y., Heilig, L., and Voss, S. Supply chain risk management in the era of big data. Lecture Notes in Computer Science, 9186, 283–294.

Braga, P. L., Oliveira, A. L. I., and Meira, S. R. L. Software effort estimation using machine learning techniques with robust confidence intervals. Proceedings of the International Conference on Hybrid Intelligent Systems, 352–357.

Pereira Gaspar, P. G., Santos Ceryno, P., Ferrer, A. L., and Tavares Thome, A. M. Phases and tools for supply chain risk management. Gestao e Producao, 27, 1–21.

Alagic, A., et al. Machine learning for an enhanced credit risk analysis. Machine Learning and Knowledge Extraction, 6(1), 53–77.

Valli, L. N. Predictive analytics applications for risk mitigation across industries. Bullet Journal of Multidisciplinary Studies, 3(4), 542–553.

Mathur, P. Machine learning applications using python. Apress.

Lewinson, E. Python for finance cookbook. Packt Publishing.

ISO 31000. Risk management guidelines. International Organization for Standardization.

Olowe, K. J., Edoh, N. L., Zouo, S. J. C., and Olamijuwon, J. Review of predictive modeling and machine learning applications in financial service analysis. Computer Science and IT Research Journal, 5(11), 2609–2626.

Khambam, S. K. R., Kaluvakuri, V. P. K., and Peta, V. P. The cloud as a financial forecast: Leveraging AI for predictive analytics. SSRN Working Paper.

Thanaki, J. Machine learning solutions. Packt Publishing Ltd.

Lohani, B. P., Trivedi, M., Singh, R. J., Bibhu, V., Ranjan, S., and Kushwaha, P. K. Machine learning based model for prediction of loan approval. Proceedings of the International Conference on Intelligent Engineering and Management.