MODEL FOR EVALUATING THE EFFECTIVENESS OF IT PROJECT MANAGEMENT USING ARTIFICIAL INTELLIGENCE

DOI: 10.31673/2415-8089.2026.012301

Abstract

The article substantiates the necessity of rethinking approaches to evaluating the effectiveness of
IT project management in the context of digital transformation. Traditional methods based on the
"iron triangle" (time, budget, scope) fail to account for the real business value of projects and ignore
the impact of artificial intelligence on management processes. The author proposes an original
Integrated Matrix of IT Project Management Effectiveness (IMPME) – a three-dimensional model
that combines evaluation levels (operational, tactical, strategic), project lifecycle stages, and types
of indicators (quantitative, qualitative, AI-based predictive). The model enables the integration of
heterogeneous data, provides comprehensive analysis of management effectiveness, and facilitates
proactive decision-making. Practical application of the IMPME will contribute to increasing the
success rate of IT investments, strategic alignment of project activities with business goals, and
effective utilization of artificial intelligence potential in project management.
Keywords: management effectiveness; IT project management; artificial intelligence;
performance evaluation; agile methodologies; integrated model.

References
1. Wysocki, R. K. (2019). Effective Project Management: Traditional, Agile, Extreme. Hoboken,
NJ: John Wiley & Sons, In. 656 p.
2. Zybareva, O., Pashnyak, Ye., & Kravchuk, I. (2024). Concepts and models of project-oriented
business management. Scientific Bulletin of Polissia, (1 (28)), 434-446.
http://nvp.stu.cn.ua/article/download/311978/303082
3. Project Management Body of Knowledge (PMBOK). Project Management Institute. 408 p.
https://pmiukraine.org/pmbok7 .
4. Aguilera, R. V., Marano, V., & Haxhi, I. (2019). International corporate governance: A review
and opportunities for future research. Journal of International Business Studies, 50, 457- 498.
https://doi.org/10.1057/s41267-019-00232-w
5. Ahmed, R., Samar, S., & Philbin, S. P. (2025). Unlocking project success: Investigating how
senior management support affects the performance of project management and benefits Management
in IT Projects. Engineering Management Journal, 1-14.
https://doi.org/10.1080/10429247.2025.2485960
6. Ameels, A., Bruggeman, W., & Scheipers, G. (2002). Value-based management control
processes to create value through integration: a literature review.
https://repository.vlerick.com/server/api/core/bitstreams/3e8402f0-9c61-4859-bef4-
39d074a58261/content (07.03.2026)
7. Vertil, E. (2025). Benefits Realization Management in Agile Project Portfolios (Doctoral
dissertation, Capella University)
https://search.proquest.com/openview/dd9912ec2f1ed95226d882f88697e8a0/1.pdf?pqorigsite=gsch
olar&cbl=18750&diss=y
8. Beck, K., et al. (2001). "Manifesto for Agile Software Development." Agile Alliance.
http://agilemanifesto.org.
9. Cohen, D., Lindvall, M., & Costa, P. (2004). An introduction to agile methods. Advances in
computers, 62(03), 1-66.
https://www.cse.chalmers.se/~feldt/courses/agile/cohen_2004_intro_to_agile_methods.pdf
10. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017, May). SCRUM model for agile
methodology. In 2017 International Conference on Computing, Communication and Automation
(ICCCA) (pp. 864-869). IEEE. https://ieeexplore.ieee.org/abstract/document/8229928/
11. Bhuddi, A., & Thandavarayan, V. G. (2025). AI-enabled benefits realization management.
https://www.diva-portal.org/smash/get/diva2:1979974/FULLTEXT01.pdf (дата звернення:
07.02.2026)
12. Onyao, L. A., Zivak, J., Al Turk, A. (2025). AI-driven project management: Challenges &
oppportunities. Journal of Management & Engineering Integration, 18(1), 14-22.
https://doi.org/10.62704/10057/30201
13. Dam, H. K., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2018). Towards effective AIpowered agile project management (No. arXiv:1812.10578). arXiv. http://arxiv.org/abs/1812.10578
14. Storchak, K. P., Tushych, A. M., & Bondarchuk, A. P. (2018). Cluster data analysis using
artificial neural networks. Sviaz [Communication], (6), 36-38.
https://con.dut.edu.ua/index.php/communication/article/view/2184/2085.
15. Bondarchuk, A., Korshun, N., Dibrivnyi, O., & Spivak, S. (2025). Ai-powered WI-FI access
controllers: a new approach to wireless network design. Information and Telecommunication
Sciences, 16(2), 27-35. https://infotelesc.kpi.ua/article/view/346532
16. Dima, A. M. (Ed.). (2020). Artificial Intelligence Adoption in Project Management: Main
Drivers, Barriers and Estimated Impact. Proceedings of the International Conference on Economics
and Social Sciences, 758–767. https://doi.org/10.2478/9788395815072-075
17. Hashfi, M. I., & Raharjo, T. (2023). Exploring the Challenges and Impacts of Artificial
Intelligence Implementation in Project Management: A Systematic Literature Review. International
Journal of Advanced Computer Science and Applications, 14(9).
https://doi.org/10.14569/IJACSA.2023.0140940
18. Jiang, Y. (2021). Application of Data Mining Technology in Field Verification of Project
Cost. Advances in Multimedia, 2021, 1–11. https://doi.org/10.1155/2021/3585878
19. Opeyemi Abayomi Odejide & Tolulope Esther Edunjobi. (2024). AI in Project Management:
Exploring Theoretical Models for Decision-Making and Risk Management. Engineering Science &
Technology Journal, 5(3), 1072–1085. https://doi.org/10.51594/estj.v5i3.959
20. Savio, R. D., & Ali, J. M. (2023). Artificial Intelligence in Project Management & Its Future.
Saudi Journal of Engineering and Technology, 8(10), 244–248.
https://doi.org/10.36348/sjet.2023.v08i10.002
21. Kriskun, I., & Bondarchuk, A. (2026). Evaluating the effectiveness of project management in
the information technology sector. European scientific journal of Economic and Financial
innovation, 1(19), 264-274. https://journal.eae.com.ua/index.php/journal/article/view/725.