Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) has emerged as a transformative technology in project management, offering advanced capabilities for data-driven decision-making, predictive analytics, and process automation. This study presents a systematic literature review of AI applications in project management, focusing on peer-reviewed journal articles published between 2015 and 2021. A total of 25 relevant studies were selected following PRISMA guidelines and analyzed using both thematic and bibliometric approaches. The findings reveal that AI is predominantly applied in areas such as project scheduling, cost estimation, risk management, safety monitoring, and project portfolio selection, with a strong concentration in construction project management. Machine learning and deep learning techniques are widely used, enabling enhanced prediction accuracy and real-time monitoring. The thematic analysis highlights key benefits, including improved efficiency, better decision-making, and enhanced safety performance. However, significant challenges remain, particularly related to data quality, model interpretability, organizational readiness, and ethical concerns. The bibliometric analysis further demonstrates that research in this field is structured around four main dimensions: AI techniques, application domains, performance outcomes, and emerging challenges. A temporal shift from traditional optimization methods to advanced data-driven approaches is also observed. This study contributes to the existing literature by providing a comprehensive synthesis of AI applications in project management and identifying critical research gaps. The findings suggest that future research should focus on explainable AI, integration with emerging technologies such as digital twins and IoT, and the development of human–AI collaborative systems to enhance project management practices.

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