The Integration of Artificial Intelligence in Project Management: A Review

  • Authors

    • Nouf Almojel King Saud University, Department of Computer Science, P.O. Box 51178, Riyadh, 11543, Saudi Arabia and Majmaah University, Department of computer sciences, P.O. Box 66, Majmaah, 11952, Saudi Arabia
    • Omer Alrwais King Saud University, Department of Computer Science, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
    https://doi.org/10.14419/0v55sy74

    Received date: August 26, 2025

    Accepted date: October 24, 2025

    Published date: December 19, 2025

  • Project Management; Knowledge Area; AI; Scheduling Management; Cost ‎Management; Risk Management
  • Abstract

    Project management is the coordination of a number of procedures to achieve ‎the desired goals of a project. It involves planning, monitoring and execution. The over ‎expansion of IT projects increased their complexity, uncertainty and its variability. ‎Therefore, it has become difficult for traditional methods to manage these projects ‎effectively. As these methods rely on experience and intuition and cannot deal with vast ‎amounts of data, repetitive tasks or high variability. As a result, the outcomes maybe ‎prone to errors. Consequently, the failure rate of IT projects increases. Since technology ‎is an essential element for any organization, they have to find solutions in order to ‎manage IT projects. AI has proven its effectiveness in many fields. In this research, ‎through a literature review, we will study the most prominent aspects of integration ‎between AI and project management in terms of challenges and impacts. As well as the ‎contribution of AI in the knowledge areas of project management. We will focus on ‎exploring how AI improves these specific knowledge areas: risk management, cost ‎management, and scheduling management. From this research, we conclude that AI ‎proved its ability to enhance many aspects of project management. Therefore, the ‎research offers important insights for organizations that attempt to adopt AI into project ‎management‎.

  • References

    1. PMI, “Project management - Job Growth and Talent Gap 2017-2027.”
    2. Ruchit Parekh and Olivia Mitchell, “Utilization of artificial intelligence in project management,” International Journal of Science and Research Ar-chive, vol. 13, no. 1, pp. 1093–1102, Sep. 2024, https://doi.org/10.30574/ijsra.2024.13.1.1779.
    3. O. Abayomi Odejide, T. Esther Edunjobi, and C. Author, “AI IN PROJECT MANAGEMENT: EXPLORING THEORETICAL MODELS FOR DECISION-MAKING AND RISK MANAGEMENT,” Engineering Science & Technology Journal, vol. 5, no. 3, pp. 1072–1085, 2024, https://doi.org/10.51594/estj.v5i3.959.
    4. M. R. Davahli, “The last state of artificial intelligence in project management,” arXiv preprint arXiv:2012.12262, 2020.
    5. M. I. Hashfi and T. Raharjo, “Exploring the challenges and impacts of artificial intelligence implementation in project management: A systematic literature review,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, 2023. https://doi.org/10.14569/IJACSA.2023.0140940.
    6. PMI, “Shaping the Future of Project Management With AI.”
    7. Kathy. Schwalbe, Information technology project management. Cengage, 2019.
    8. G. Auth, J. Johnk, and D. A. Wiecha, “A Conceptual Framework for Applying Artificial Intelligence in Project Management,” in Proceedings - 2021 IEEE 23rd Conference on Business Informatics, CBI 2021 - Main Papers, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 161–170. https://doi.org/10.1109/CBI52690.2021.00027.
    9. H. Sarwar and M. Rahman, “A Systematic Short Review of Machine Learning and Artificial Intelligence Integration in Current Project Manage-ment Techniques,” in 2024 4th IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 262–270. https://doi.org/10.1109/SEAI62072.2024.10674089.
    10. T. V. Fridgeirsson, H. T. Ingason, H. I. Jonasson, and H. Gunnarsdottir, “A Qualitative Study on Artificial Intelligence and Its Impact on the Pro-ject Schedule, Cost and Risk Management Knowledge Areas as Presented in PMBOK®,” Applied Sciences (Switzerland), vol. 13, no. 19, Oct. 2023, https://doi.org/10.3390/app131911081.
    11. T. V. Fridgeirsson, H. T. Ingason, H. I. Jonasson, and H. Jonsdottir, “An authoritative study on the near future effect of artificial intelligence on project management knowledge areas,” Sustainability (Switzerland), vol. 13, no. 4, pp. 1–20, Feb. 2021, https://doi.org/10.3390/su13042345.
    12. IBM, Cole Stryker, and Eda Kavlakoglu, “What is artificial intelligence (AI)?”
    13. D. K. Dukhiram, “AI-Assisted Project Management: Enhancing Decision-Making and Forecasting,” Journal of Artificial Intelligence Research By the Science Brigade (Publishing) Group 146 Journal of Artificial Intelligence Research, vol. 3.
    14. A. Mohammad and B. Chirchir, “Challenges of Integrating Artificial Intelligence in Software Project Planning: A Systematic Literature Review,” Digital, vol. 4, no. 3, pp. 555–571, Jun. 2024, https://doi.org/10.3390/digital4030028.
    15. A. Alshaikhi and M. Khayyat, “An investigation into the Impact of Artificial Intelligence on the Future of Project Management,” in 2021 Interna-tional Conference of Women in Data Science at Taif University (WiDSTaif), IEEE, 2021, pp. 1–4. https://doi.org/10.1109/WiDSTaif52235.2021.9430234.
    16. A. O. Sousa, J. Carlos, P. Faria, J. Pedro, C. Leal, and M. Moreira, “Assessing Risks in Software Projects Through Machine Learning Approaches,” FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO, 2021.
    17. T. Narbaev, Ö. Hazir, B. Khamitova, and S. Talgat, “A machine learning study to improve the reliability of project cost estimates,” Int J Prod Res, vol. 62, no. 12, pp. 4372–4388, 2024, https://doi.org/10.1080/00207543.2023.2262051.
    18. C. Capone and T. Narbaev, “Estimation of Risk Contingency Budget in Projects using Machine Learning,” in IFAC-PapersOnLine, Elsevier B.V., 2022, pp. 3238–3243. https://doi.org/10.1016/j.ifacol.2022.10.140.
    19. M. N. Alatawi et al., “A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment,” IET Software, vol. 2023, pp. 1–19, Dec. 2023, https://doi.org/10.1049/2023/4324783.
    20. I. Lishner and A. Shtub, “Using an Artificial Neural Network for Improving the Prediction of Project Duration,” Mathematics, vol. 10, no. 22, Nov. 2022, https://doi.org/10.3390/math10224189.
    21. M. A. Hamada, “Neural Network Intelligence Model for Software Projects Cost Prediction,” in Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 686–691. https://doi.org/10.1109/IDAACS58523.2023.10348828.
    22. J. G. Ruiz, J. M. Torres, and R. G. Crespo, “The application of artificial intelligence in project management research: A review,” International Jour-nal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 6, pp. 54–66, 2021, https://doi.org/10.9781/ijimai.2020.12.003.
    23. C. Cortes and V. Vapnik, “Support-vector networks,” Mach Learn, vol. 20, no. 3, pp. 273–297, 1995. https://doi.org/10.1007/BF00994018.
    24. E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, “Comprehensive review of K-Means clustering algorithms,” Criterion, vol. 12, no. 08, pp. 22–23, 2021. https://doi.org/10.31695/IJASRE.2021.34050.
    25. S. S. Nalluri, G. A. E. S. Kumar, D. K. Arumalla, and V. K. Auti, “Software Project Estimation Using Machine Learning,” in 2023 2nd Interna-tional Conference on Futuristic Technologies, INCOFT 2023, Institute of Electrical and Electronics Engineers Inc., 2023. https://doi.org/10.1109/INCOFT60753.2023.10425581.
    26. M. C. Duică, C. G. Vasciuc Săndulescu, and D. Panagoreț, “The Use of Artificial Intelligence in Project Management,” Valahian Journal of Eco-nomic Studies, vol. 15, no. 1, pp. 105–118, Jun. 2024, https://doi.org/10.2478/vjes-2024-0009.
    27. S. Raisch and S. Krakowski, “Artificial intelligence and management: The automation–augmentation paradox,” Academy of management review, vol. 46, no. 1, pp. 192–210, 2021. https://doi.org/10.5465/amr.2018.0072.
    28. K. C. Lakshminarasimham, “Integrating AI into Program and Project Management: Transforming Decision-Making and Risk Management,” Inter-national Journal of Sustainable Development Through AI, ML and IoT, vol. 3, no. 2, pp. 1–16, 2024.
    29. M. Tarawneh, F. Alzyoud, and H. Abdalwahed, “Innovating Project Management: AI Applications for Success Prediction and Resource Optimiza-tion,” 2024, [Online]. Available: https://www.researchgate.net/publication/377402362. https://doi.org/10.1007/978-3-031-56950-0_32.
    30. F. Shoushtari, A. Daghighi, and E. Ghafourian, “Application of Artificial Intelligence in Project Management,” International journal of industrial engineering and operational research, vol. 6, no. 2, pp. 49–63, 2024.
    31. A. Tlili and S. Chikhi, “Risks analyzing and management in software project management using fuzzy cognitive maps with reinforcement learning,” Informatica (Slovenia), vol. 45, no. 1, pp. 133–141, 2021, https://doi.org/10.31449/inf.v45i1.3104.
    32. M. A. Hamada, A. Abdallah, M. Kasem, and M. Abokhalil, “Neural network estimation model to optimize timing and schedule of software pro-jects,” in 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), IEEE, 2021, pp. 1–7. https://doi.org/10.1109/SIST50301.2021.9465887.
    33. R. Hassani and Y. El Bouzekri El Idrissi, “Proposal of a framework and integration of artificial intelligence to succeed IT project planning,” Inter-national Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 6, pp. 3396–3404, Nov. 2019, https://doi.org/10.30534/ijatcse/2019/114862019.
    34. L. Bendada, M. Brioua, M. R. Morakchi, and I. Djouani, “Predicting project duration using a coupled artificial neural network and Taguchi method approach,” STUDIES IN ENGINEERING AND EXACT SCIENCES, vol. 5, no. 2, p. e5641, Jul. 2024, https://doi.org/10.54021/seesv5n2-019.
    35. A. Tlili, S. Chikhi, and A. Abraham, “Software Project Risks Management: Applying Extended Fuzzy Cognitive Maps with Reinforcement Learn-ing,” International Journal of Computer Information Systems and Industrial Management Applications, vol. 12, pp. 182–192, 2020, [Online]. Avail-able: www.mirlabs.net/ijcisim/index.html.
  • Downloads

  • How to Cite

    Almojel, N., & Alrwais, O. (2025). The Integration of Artificial Intelligence in Project Management: A Review. International Journal of Basic and Applied Sciences, 14(8), 420-428. https://doi.org/10.14419/0v55sy74