THE USEFULNESS OF ARTIFICIAL INTELLIGENCE IN ENERGY PROJECT MANAGEMENT

This article examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) into Project Management (PM) practices within the energy sector, highlighting how data-driven technologies enhance planning accuracy, risk assessment, and operational efficiency. The study combines a theoretical approach with a bibliometric analysis based on the Scopus and Web of Science-Core Collection databases for the period 2019-2025 to identify emerging research trends and leading contributors. After three successive filtering stages, 35 relevant articles were retained for our analysis. Methodological steps included data cleaning, network analysis using VOSviewer tool to map co-authorships, keywords, and interpretive synthesis of main themes. Results show a significant rise in AI applications after 2019, especially in predictive analytics, hybrid algorithm development, and predictive maintenance, indicating a transition from reactive to proactive project management in energy projects. Today, AI is currently applied in energy project management mainly for analytics, forecasting, optimization, and decision support, with strongest adoption in smart grids and energy storage. Limitations of this article include a lack of empirical validation, restricted access to industrial data due to confidentiality, and exclusive reliance on Scopus and Web of Science-Core Collection-indexed studies. However, the bibliometric evidence remains statistically solid. The study also brings into light several strengths and weaknesses in the current use of AI in energy project management, as well as the substantial electricity consumption associated with AI-focused data centres. The study concludes with several recommendations derived from its findings.