Strategic Governance Intelligence: An AI-Driven Framework for Prescriptive Project Risk Assessment and Decision Optimization
Ms. Surabhi KS1, Sneha M2
1Professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
ksurabhi454@gmail.com
2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
snehamohan3214@gmail.com
ABSTRACT
Strategic project governance in contemporary enterprise environments demands informed decision-making under significant financial exposure, technological complexity, and operational uncertainty. Conventional governance frameworks predominantly rely on static risk matrices, periodic reviews, and expert judgment, which often lack adaptability, explainability, and predictive capability when applied to large-scale or innovation-driven projects. As a result, organizations face challenges such as cost overruns, delayed timelines, and inefficient resource allocation.
This paper presents Strategic Governance Intelligence, an AI-driven decision support system designed to perform prescriptive project audits and portfolio-level risk analysis. The proposed framework integrates semantic similarity detection using sentence embedding models to identify redundant or overlapping project proposals, large language model–based strategic reasoning to generate explainable governance insights, and probabilistic risk modeling through Monte Carlo simulation to quantify uncertainty and estimate project success likelihood. The framework is designed to support scalable governance analysis across multiple projects while maintaining transparency and interpretability of AI-driven decisions.
An interactive web-based implementation developed using Streamlit enables real-time governance workflows, providing multi-dimensional risk visualization through radar charts, ISO 31000–compliant 5×5 risk matrices, sensitivity analysis, and comparative project evaluation dashboards. The system also supports historical audit tracking and portfolio-level analytics to assist strategic capital allocation decisions.
Experimental evaluation demonstrates that the proposed system effectively identifies high-risk and redundant project proposals, improves transparency in governance decision-making, and delivers actionable strategic recommendations related to budgeting, staffing, and timelines. The results highlight the potential of combining explainable artificial intelligence, probabilistic modeling, and visual analytics to enhance enterprise-scale governance processes and support data-driven strategic decision intelligence.
Keywords:Strategic Governance, Decision Intelligence, Artificial Intelligence, Large Language Models, Risk Assessment, Monte Carlo Simulation, Semantic Similarity, Project Management
