LEVERAGING ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS FOR DYNAMIC RISK MANAGEMENT IN COMPLEX INFRASTRUCTURE PROJECTS

Abstract

With a more complex and uncertain world, infrastructure projects face multivariate risks that necessitate dynamic, data-driven risk management solutions. Drawing on quantitative, cross-sectional survey research, this study examines the potential of Artificial Intelligence (AI) and Predictive Analytics (PA) in improved dynamic risk management of complex infrastructure projects. Drawing on a quantitative, cross-sectional survey design, 168 professional practitioners working in Nigeria's infrastructure industry (project managers, engineers, and risk analysts) were surveyed. A formal questionnaire was utilized to quantify levels of AI and PA adoption, type of risks faced, and efficacy of countermeasures adopted. Descriptive statistics indicated profound consciousness and growing utilization of AI tools in risk planning and response actions. Correlation and multi-regression analysis revealed statistically significant relationships between AI adoption and better risk mitigation performance (r = 0.732, p < 0.01; β = 0.481, p < 0.001), and predictive analytics and performance improvement (β = 0.364, p < 0.001). Complex project modulated risk mitigation performance, emphasizing the value of smart technology in high-uncertainty, high-risk settings. The last model accounted for 64.3% of risk mitigation effectiveness variance, illustrating the potential of AI-based methods to make predictions. The results validate that PA and AI are enablers of highest importance to enable real-time proactive risk management, especially in circumstances where conventional approaches fall short. In extending TAM and Dynamic Capabilities Theory, this research provides developing world empirical findings. It suggests policy reforms, investments in digital infrastructure, and upskilling the workforce as enablers of scalable use of AI among emerging economies' infrastructure projects.