While artificial intelligence (AI) promises enhanced automation and real-time insights, its effective integration across the project lifecycle remains uneven and fragmented. This review synthesizes current developments, trends, and challenges of AI integration within project management information systems (PMIS). The study reconceptualizes PMIS as a strategic decision infrastructure supporting digital transformation (DX).
This study adopts a mixed-methods research synthesis approach to based on 70 peer-reviewed publications published between 2015 and 2025. Quantitative analyses were used to examine publication trends, geographical distributions and research patterns, while qualitative thematic analysis was conducted to synthesize AI functions, benefits, challenges and implementation strategies across PMIS phases. PRISMA-based procedures were applied to ensure systematic study selection.
The review highlights the growing integration of AI across key PMIS functions, including intelligent scheduling, predictive risk forecasting, automated reporting, and decision support. AI adoption contributes to improved organizational efficiency and project governance; however, implementation remains uneven due to data quality limitations, fragmented digital ecosystems, and organizational resistance. The study also identifies the emerging role of Generative AI (GenAI) and positions AI as a “cognitive co-actor” within socio-technical project environments.
This study provides actionable recommendations for managers, PMIS developers and decision-makers. Findings emphasize the importance of workforce upskilling, data governance and aligning AI-enabled PMIS with broader DX strategies. Policymakers may also leverage the findings to support regulatory compliance and responsible AI governance initiatives.
Responsible AI integration within PMIS can enhance transparency, support workforce reskilling and democratize access to intelligent project management tools.
This study develops an integrative lifecycle-based perspective that maps AI functionalities across PMIS phases while distinguishing between general AI applications and AI capabilities specifically embedded within PMIS environments. The findings contribute to the growing intersection of AI, project management and digital transformation research by providing a conceptual foundation for future AI-enabled PMIS studies.
