ARTIFICIAL INTELLIGENCE IN IT PROJECT MANAGEMENT: SYSTEMATIZATION OF APPROACHES BASED ON PMI
DOI:
https://doi.org/10.25313/3083-7782-2026-4-4Keywords:
artificial intelligence, project management, PMBOK, process groups, performance domains, machine learning, large language modelsAbstract
Introduction. The rapid adoption of artificial intelligence (AI) technologies in project management practice is accompanied by a significant gap between adoption rates and the level of strategic understanding of these technologies. Industry reports indicate that the majority of organizations deploy AI tools in a fragmented manner, without aligning them with project management process logic, which limits the potential value of these technologies. Despite the growing volume of scientific publications, the issue of systematizing AI approaches by project management process groups remains insufficiently explored. Existing systematic reviews either do not cover the full technological spectrum from traditional machine learning to agentic AI, or do not employ process-based classification as an analytical framework, creating a need for a comprehensive interdisciplinary study.
Purpose. To classify AI technologies by five process groups (Process Groups: A Practice Guide, PMI, 2022) with cross-cutting analysis of PMBOK Guide 7th Edition performance domains coverage, assess the maturity level of research in each group, and identify key gaps.
Materials and Methods. A systematic literature review covering the period 2021–2026, drawing on Scopus, Web of Science databases, and industry reports from PMI, McKinsey, and Deloitte. The five process groups per Process Groups: A Practice Guide (PMI, 2022) serve as the classification framework.
Results. An original matrix mapping AI technologies to PMBOK process groups was constructed. Uneven research coverage was identified: the vast majority of publications focus on Planning and Monitoring and Controlling, while Closing remains a critical gap. A maturity–impact inversion was found: process groups with the most publications face adoption barriers, whereas Executing leads in practice due to generative AI. Three waves of technological evolution were documented: traditional ML (machine learning) (2011–2022), deep learning (2020–2024), and generative/agentic AI (2023–present). Projecting results onto PMBOK 7 performance domains revealed critical gaps in people-oriented domains (stakeholders, team, development approach).
Prospects. AI solutions for automating project closing processes, governance models for agentic AI, standardized performance metrics for generative AI.
References
PMI Pulse of the Profession 2025 : Navigating complexity. Project Management Institute. 2025. URL: https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/pulse_of_the_profession_2025-1.pdf (дата звернення: 25.02.2026).
The state of AI: How organizations are rewiring to capture value. McKinsey & Company. 2025. URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (дата звернення: 25.02.2026).
18th Annual State of Agile Report. Digital.ai. 2025. URL: https://digital.ai/resource-center/analyst-reports/state-of-agile-report (дата звернення: 25.02.2026).
Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027. Gartner. 2025. URL: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027 (дата звернення: 25.02.2026).
State of AI in the Enterprise: The untapped edge. Deloitte. 2026. URL: https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf (дата звернення: 25.02.2026).
Process Groups: A Practice Guide. Newtown Square, PA : Project Management Institute, 2022. 402 p.
A Guide to the Project Management Body of Knowledge (PMBOK Guide). 7th ed. and The Standard for Project Management. Newtown Square, PA : Project Management Institute, 2021. 370 p.
Fridgeirsson T. V., Ingason H. T., Jonasson H. I., Jonsdottir H. An Authoritative Study on the Near Future Effect of Artificial Intelligence on Project Management Knowledge Areas. Sustainability. 2021. Vol. 13, No. 4. P. 2345. DOI: https://doi.org/10.3390/su13042345
Taboada I., Daneshpajouh A., Toledo N., de Vass T. Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences. 2023. Vol. 13, No. 8. P. 5014. DOI: https://doi.org/10.3390/app13085014
Nenni M. E., De Felice F., De Luca C., Forcina A. How artificial intelligence will transform project management in the age of digitization: a systematic literature review. Management Review Quarterly. 2025. Vol. 75. P. 1669–1716. DOI: https://doi.org/10.1007/s11301-024-00418-z
Müller R., Locatelli G., Holzmann V., Nilsson M., Sagay T. Artificial Intelligence and Project Management: Empirical Overview, State of the Art, and Guidelines for Future Research. Project Management Journal. 2024. Vol. 55, No. 1. P. 9–15. DOI: https://doi.org/10.1177/87569728231225198
Bushuyev S., Chumachenko I., Galkin A., Bushuiev D., Dotsenko N. Sustainable Development Projects Implementing in BANI Environment Based on AI Tools. Sustainability. 2025. Vol. 17, No. 6. P. 2607. DOI: https://doi.org/10.3390/su17062607
Almeida P. M., Fernandes G., Santos J. M. R. C. A. Artificial intelligence tools for project management: A knowledge-based perspective. Project Leadership and Society. 2025. Vol. 6. P. 100196. DOI: https://doi.org/10.1016/j.plas.2025.100196
Cheligeer C., Huang J., Wu G., Bhuiyan N., Xu Y., Zeng Y. Machine learning in requirements elicitation: a literature review. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 2022. Vol. 36. P. e32. DOI: https://doi.org/10.1017/S0890060422000166
Radliński Ł., Swacha J. Large Language Models for Early-Stage Software Project Estimation: A Systematic Mapping Study. Applied Sciences. 2025. Vol. 15, No. 24. P. 13099. DOI: https://doi.org/10.3390/app152413099
Uddin S., Ong S., Lu H. Machine learning in project analytics: a data-driven framework and case study. Scientific Reports. 2022. Vol. 12. P. 15252. DOI: https://doi.org/10.1038/s41598-022-19728-x
Narbaev T., Hazir Ö., Khamitova B., Talgat S. A machine learning study to improve the reliability of project cost estimates. International Journal of Production Research. 2024. Vol. 62, No. 12. P. 4372–4388. DOI: https://doi.org/10.1080/00207543.2023.2262051
Shetty P. P., Balakrishnan A., Xu M., Xi X., Yu Z. Story Point Estimation Using Large Language Models. 2026. URL: https://arxiv.org/abs/2603.06276 (дата звернення: 25.02.2026).
Ashtari M. A., Ansari R., Hassannayebi E., Jeong J. Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach. Buildings. 2022. Vol. 12, No. 10. P. 1660. DOI: https://doi.org/10.3390/buildings12101660
Schröder M. AutoScrum: Automating Project Planning Using Language Model Programs. 2023. URL: https://arxiv.org/abs/2306.03197 (дата звернення: 25.02.2026).
Peng S., Kalliamvakou E., Cihon P., Demirer M. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. 2023. URL: https://arxiv.org/abs/2302.06590 (дата звернення: 25.02.2026).
Cinkusz K., Chudziak J. A., Niewiadomska-Szynkiewicz E. Cognitive Agents Powered by Large Language Models for Agile Software Project Management. Electronics. 2025. Vol. 14, No. 1. P. 87. DOI: https://doi.org/10.3390/electronics14010087
Campbell C., Boscoe B., Do T. AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups. 2025. URL: https://arxiv.org/abs/2508.05648 (дата звернення: 25.02.2026).
Dhar R., Kakran A., Karan A., Vaidhyanathan K., Varma V. DRAFT-ing Architectural Design Decisions using LLMs. 2025. URL: https://arxiv.org/abs/2504.08207 (дата звернення: 25.02.2026).
Kirstein F., Ruas T., Gipp B. What’s Wrong? Refining Meeting Summaries with LLM Feedback. 2024. URL: https://arxiv.org/abs/2407.11919 (дата звернення: 25.02.2026).
Schlegel D., Rosenberg B., Fundanovic O., Kraus P. How to conduct successful business process automation projects? An analysis of key factors in the context of robotic process automation. Business Process Management Journal. 2024. Vol. 30, No. 8. P. 99–119. DOI: https://doi.org/10.1108/BPMJ-06-2023-0465
Sriraman G., Shriram R. A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment. Frontiers in Computer Science. 2023. Vol. 5. P. 1214722. DOI: https://doi.org/10.3389/fcomp.2023.1214722
Santos J. I., Pereda M., Ahedo V., Galán J. M. Explainable machine learning for project management control. Computers & Industrial Engineering. 2023. Vol. 180. P. 109261. DOI: https://doi.org/10.1016/j.cie.2023.109261
Burdakov A., Ahn M. J. Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects. 2025. URL: https://arxiv.org/abs/2506.02214 (дата звернення: 25.02.2026).
Assalaarachchi L. I., Masood Z., Hoda R., Grundy J. Toward Agentic Software Project Management: A Vision and Roadmap. 2026. URL: https://arxiv.org/abs/2601.16392 (дата звернення: 25.02.2026).
Xu N., Zhou X., Guo C., Xiao B., Wei F., Hu Y. Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects. Sustainability. 2022. Vol. 14, No. 24. P. 16846. DOI: https://doi.org/10.3390/su142416846
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