Sep 2026· European Journal of Innovation Management· pp. 1-29· 0 citations· 31 references
TL;DR
It is empirically demonstrated 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.
Abstract
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.
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).
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.
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.
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.
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.
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.
This study examines the main drivers and barriers influencing the adoption of artificial intelligence (AI) in project management, with a specific focus on emerging market conditions. The research is based on a systematic literature review conducted in accordance with the PRISMA 2020 guidelines. The final sample include...
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The study empirically operationalises the definition of PM 5.0 as an AI–human–sustainability framework and offers new empirical evidence in the context of a developing economy.
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Artificial Intelligence (AI) adoption within construction procurement has the potential to significantly enhance efficiency, transparency, and decision-making; however, its adoption within the construction industry remains limited. This research investigates the critical barriers and success factors influencing AI adop...
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Evidence from 62 studies that indicate the significant impact of AI on project management is summarized, with a view to assessing the extent of AI integration into project management practices through identifying its key functions, possible spheres of application, and issues related to its implementation in different i...
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This study aims to examine Business Intelligence (BI) and its influence on Sustainable Project Performance (SPP) from the perspective of Data-Driven Project Management Capabilities (DDPMC). A capability-based view is adopted throughout the study and it is argued that BI would be more effective in terms of providing pro...
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