<![CDATA[Despite extensive methodological progress, project failure rates remain persistently high across sectors such as construction, information technology, and public infrastructure. This study employs a Systematic Literature Review (SLR) based on the PRISMA framework, analyzing 78 peer-reviewed articles published between 2015 and 2025 from databases including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. The review identifies three primary categories of factors contributing to project failures: (i) organizational shortcomings such as weak planning, limited stakeholder engagement, and ineffective risk governance; (ii) external disruptions linked to market volatility, regulatory changes, and environmental instability; and (iii) technical and operational deficiencies, including reactive monitoring and resource mismanagement. Within this context, Artificial Intelligence (AI) emerges as a transformative enabler in project management. AI applications are grouped into four domains: early risk detection and prediction, decision support and optimization, real-time monitoring and control through IoT and analytics, and systematic learning from failed projects using knowledge-driven approaches. While the literature emphasizes AI’s role in achieving project success, this study highlights its corrective and recovery potential for failing projects. The paper proposes reframing AI not only as a success enabler but as a critical tool for failure prevention and recovery. Future research should prioritize empirical validation, hybrid human–AI decision-making models, and cross-sectoral applications to strengthen AI’s role in building adaptive and resilient project management frameworks.]]>
Copyrights © 2025
