Areas, benefits and challenges of using artificial intelligence tools in project management

Purpose The aim of this paper is to identify the current use of artificial intelligence (AI) in project management and to assess the benefits, implementation challenges and key competencies that determine the effectiveness of AI adoption. Design/methodology/approach The study adopts a mixed approach combining desk research with a quantitative CAWI survey conducted among 601 Service Desk employees in Poland (2025). The relationships between competencies, implementation challenges and post-implementation benefits were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings The results indicate that AI delivers noticeable benefits across project management areas, particularly in faster data-driven decision-making, improved resource management, and increased process efficiency. However, a significant underestimation of AI-related competencies was identified, suggesting a gap between technological adoption and organizational readiness. The PLS-SEM results show that competencies are strongly and positively associated with achieved benefits, whereas perceived implementation challenges are only weakly and positively associated. This latter relationship may reflect greater implementation exposure rather than a beneficial influence of the challenges themselves. Research limitations/implications The study is based on a specific sample (Service Desk employees) and cross-sectional data, which limits the generalizability of the findings across different national and institutional contexts. Future research should include longitudinal and cross-industry studies to better capture the evolution of AI maturity. Practical implications Organizations should precede AI deployment with an audit of project-team competencies and data readiness, introduce AI through controlled pilot projects and monitor its effects using project-level indicators such as decision time, resource utilization, process efficiency and forecast accuracy. The results indicate that investments in data literacy, algorithmic interpretation, AI ethics and human–AI decision-making are more likely to improve implementation outcomes than technology acquisition alone. Social implications The study highlights the importance of developing AI-related competencies and awareness to ensure responsible and effective use of AI, which may contribute to improved organizational performance and broader societal outcomes. Originality/value This paper contributes to the literature by empirically demonstrating that competencies, rather than technological factors alone, are the primary driver of value creation in AI-supported project management, thereby helping to bridge the gap between theory and practice. The study is theoretically grounded in the resource-based view and AI adoption frameworks.