Artificial Intelligence in Project Management: A Systematic Review of Trends, Applications, and Ethical Challenges (2020–2026)

Abstract

Despite the rapid proliferation of artificial intelligence technologies in project management contexts, a systematic understanding of AI applications, outcomes, and ethical implications remains fragmented. This systematic literature review addresses this research gap by synthesizing evidence from peer-reviewed studies published between 2020 and 2026, following PRISMA guidelines to ensure methodological rigor and transparency. The review examined multiple databases including Scopus, Web of Science, and IEEE Xplore, employing comprehensive search strategies with predefined inclusion and exclusion criteria. Quality assessment was conducted using established frameworks appropriate to diverse study designs, and data synthesis combined narrative synthesis with thematic analysis. The findings reveal that AI applications in project management span all phases of the project lifecycle, with the planning phase receiving the most extensive research attention. AI technologies demonstrated substantial potential for enhancing estimation accuracy, optimizing schedules, improving risk identification, and enabling more effective monitoring and control. The emergence of agentic AI in 2026 represents a significant development, transforming the relationship between project managers and AI systems from tool-based augmentation toward autonomous agent collaboration. However, the evidence also reveals considerable variation in outcomes across contexts, highlighting the importance of implementation factors and organizational readiness in determining AI success. The systematic examination of ethical challenges identifies critical considerations including algorithmic transparency, accountability allocation for AI-influenced decisions, fairness in AI-driven resource allocation, and data privacy protection. These ethical dimensions have received insufficient attention in the project management literature, representing an important gap that warrants systematic examination. The review identifies several limitations in the current evidence base, including the predominance of cross-sectional studies, limited longitudinal evidence regarding sustained adoption impacts, concentration of research in certain geographic and industrial contexts, and sparse development of project management-specific AI ethics frameworks. These gaps point toward productive directions for future research, including longitudinal studies of AI adoption trajectories, implementation process research, examination of AI effectiveness across diverse project types and industries, and development of theoretical frameworks for AI-augmented project management. The findings contribute to cumulative knowledge building while providing evidence-based guidance for practitioners considering AI adoption in project management contexts. The review demonstrates that AI integration in project management has matured from experimental applications to practical deployments, while highlighting the need for continued attention to ethical considerations and implementation factors that influence successful adoption.