Impact of Artificial Intelligence-Driven Project Management on Operational Performance in Oil and Gas Projects: A Structural Equation Modeling Approach

Abstract

The oil and gas industry is a capital-intensive, high-risk sector in which project delays, cost overruns, and unmanaged operational risk carry substantial financial and safety consequences. Artificial intelligence (AI) is increasingly promoted as a lever for improving project management practice, yet the mechanisms and boundary conditions through which AI-driven project management (AI-PM) translates into operational performance (OP) remain insufficiently tested in this sector. Drawing on the resource-based view and dynamic capabilities theory, this study develops and tests a structural model in which project risk management (PRM) mediates, and organizational readiness (OR) moderates, the relationship between AI-PM and OP. Survey data were collected from 285 professionals working across the upstream, midstream, and downstream segments of the oil and gas value chain and analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated adequate reliability, convergent validity, and discriminant validity. The structural model explained 41.1% of the variance in operational performance and 23.7% of the variance in project risk management, with acceptable predictive relevance. All five hypothesized relationships were supported: AI-PM positively affected OP directly and indirectly through PRM, and organizational readiness positively moderated the AI-PM-OP relationship. The findings suggest that AI-driven project management is most beneficial when it is embedded in strong risk management routines and supported by organizational readiness, offering both theoretical extensions to technology-performance research and practical guidance for oil and gas project organizations.