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<p>This narrative review examines how artificial intelligence (AI) is reshaping project management by augmenting forecasting, scheduling, and risk mitigation. It synthesizes peer-reviewed literature (2019–2024) from five sources including Scopus, Web of Science, PubMed, CINAHL, and Google Scholar using a pre-defined search strategy, inclusion/exclusion criteria, and quality appraisal. Evidence indicates that AI improves cost and time performance through predictive analytics, intelligent resource allocation, and workflow automation. These same capabilities also reveal socio-technical risks, including algorithmic bias and opacity, data security and privacy concerns, high adoption and lifecycle costs, skills gaps, and the erosion of human judgment when tools are over-trusted. We highlight the central tension between AI as an enabler of efficiency and insight and its role as a displacer of labor and locus of accountability, arguing for a hybrid governance approach where AI augments rather than replaces human expertise. Actionable implications include upskilling the workforce and redesigning roles; embedding FATML (fairness, accountability, transparency in machine learning) throughout the AI lifecycle; and aligning deployments with emerging governance models and international standards. We conclude with a forward agenda on adaptive AI governance frameworks, human–AI collaboration in real project environments, and workforce adaptation in AI-enabled project settings so that AI functions as an enabler, not a disruptor, of project management practice.</p>

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Evaluating AI competency in project management: benefits and challenges

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Omobolanle Olatoye,
  • Efenwengbe Nicholas Aminaho

摘要

This narrative review examines how artificial intelligence (AI) is reshaping project management by augmenting forecasting, scheduling, and risk mitigation. It synthesizes peer-reviewed literature (2019–2024) from five sources including Scopus, Web of Science, PubMed, CINAHL, and Google Scholar using a pre-defined search strategy, inclusion/exclusion criteria, and quality appraisal. Evidence indicates that AI improves cost and time performance through predictive analytics, intelligent resource allocation, and workflow automation. These same capabilities also reveal socio-technical risks, including algorithmic bias and opacity, data security and privacy concerns, high adoption and lifecycle costs, skills gaps, and the erosion of human judgment when tools are over-trusted. We highlight the central tension between AI as an enabler of efficiency and insight and its role as a displacer of labor and locus of accountability, arguing for a hybrid governance approach where AI augments rather than replaces human expertise. Actionable implications include upskilling the workforce and redesigning roles; embedding FATML (fairness, accountability, transparency in machine learning) throughout the AI lifecycle; and aligning deployments with emerging governance models and international standards. We conclude with a forward agenda on adaptive AI governance frameworks, human–AI collaboration in real project environments, and workforce adaptation in AI-enabled project settings so that AI functions as an enabler, not a disruptor, of project management practice.