INTEGRATING AI-DRIVEN PREDICTIVE ANALYTICS IN PROJECT RISK MANAGEMENT TO OPTIMIZE DECISION-MAKING AND PERFORMANCE EFFICIENCY

In today’s dynamic business landscape, project risk management is crucial for ensuring the successful execution
of complex initiatives. Traditional risk management frameworks rely on historical data, expert judgment, and
deterministic models, which often lack the adaptability required to address rapidly evolving project environments.
Integrating artificial intelligence (AI)-driven predictive analytics into project risk management enhances decisionmaking by leveraging advanced data analysis, machine learning algorithms, and real-time risk assessment. AI
enables organizations to proactively identify potential risks, quantify their impact, and recommend optimal
mitigation strategies. By analysing structured and unstructured data from diverse sources, AI-driven predictive
analytics provides deeper insights into risk patterns, allowing project managers to shift from reactive to proactive
decision-making. This paper explores the integration of AI-driven predictive analytics in project risk management,
focusing on its ability to optimize risk identification, assessment, and response strategies. It examines key AI
methodologies, including machine learning models, natural language processing (NLP), and reinforcement
learning, that enhance risk prediction accuracy. Furthermore, it discusses the challenges of AI adoption, such as
data reliability, model interpretability, and integration with existing project management tools. A comparative
analysis of AI-enhanced risk management versus conventional approaches demonstrates its effectiveness in
improving project performance efficiency, reducing cost overruns, and mitigating schedule delays. The study
concludes with future directions for AI-driven project risk management, emphasizing the need for hybrid AIhuman decision-making models to enhance strategic project execution.