Artificial Intelligence Project Management Methodologies: Insights from Field Experts in Saudi Arabia

Authors

  • Walaa Alsumari King Abdulaziz City for Science and Technology, Saudi Arabia; King Saud University, Saudi Arabia https://orcid.org/0000-0002-7659-5499
  • Omer Alrwais King Saud University, Saudi Arabia

DOI:

https://doi.org/10.34010/injiiscom.v6i2.16127

Keywords:

Artificial Intelligence, AI Workflow, Project Management Methodologies, Agile, Hybrid

Abstract

This study investigates the impact of project management (PM) methodologies on Artificial Intelligence (AI) project execution in Saudi Arabia, while addressing global AI PM challenges. Utilizing an exploratory approach combining literature reviews, multi-company case studies, and surveys with 31 local AI practitioners, this research analyzes operational workflows. Locally, Agile dominates, being applied in 60% of projects primarily focused on applied AI. Globally, projects grapple with unclear leadership, role ambiguity, inconsistent hybrid setups, and non-linear risks. These insights highlight the critical need for purpose-built frameworks tailored to AI’s inherent complexity, technical uncertainty, and ethical demands, paving the way for optimized regional AI initiatives.

References

Agrawal, A., Singh, V., & Fischer, M. (2023). LeanAI: A method for AEC practitioners to effectively plan AI implementations. arXiv preprint arXiv:2306.16799.

AWS (2023a). Machine Learning Lens - AWS Well-Architected Framework, Accessed: Nov. 29, 2024. available at: https://docs.aws.amazon.com/wellarchitected/latest/machinelearning-lens/machine-learning-lens.html.

— (2023b). What is SDLC? - Software Development Lifecycle Explained - AWS, Accessed: Nov. 29, 2024. available at: https://aws.amazon.com/what-is/sdlc/.

— (2024). Platform perspective: Infrastructure for and applications of AI - AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI, Accessed: Nov. 29, 2024. available at: https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/ platform-perspective-infrastructure-for-and-applications-of-aiml. html.

Bakry, S. H., & Saud, B. A. A. (2021). A Roadmap to AI: An Insight from the Saudi Vision 2030. In Artificial Intelligence and Its Contexts: Security, Business and Governance (pp. 201-223). Cham: Springer International Publishing.

Baldassarre, M. T., Gigante, D., Kalinowski, M., & Ragone, A. (2024, April). POLARIS: A framework to guide the development of Trustworthy AI systems. In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI (pp. 200-210).

Daraojimba, E. C., Nwasike, C. N., Adegbite, A. O., Ezeigweneme, C. A., & Gidiagba, J. O. (2024). Comprehensive review of agile methodologies in project management. Computer Science & IT Research Journal, 5(1), 190-218.

Drob, C., & Zichil, V. (2013). Overview regarding the main guidelines, standards and methodologies used in project management. Journal of Engineering Studies and Research, 19(3), 26-31.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37.

Google (2024a). Managing ML projects — Machine Learning — Google for Developers, Accessed: Nov. 29, 2024. available at: https://developers.google.com/machine-learning/managingml-projects.

— (2024b). Stakeholders — Machine Learning — Google for Developers, Accessed: 2025-01-16. available at: https://developers.google.com/machine-learning/managing-ml-projects.

Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., ... & Hill, C. (2021). Impact of big data and artificial intelligence on industry: developing a workforce roadmap for a data driven economy. Global Journal of Flexible Systems Management, 22(3), 197-217.

Leijnen, S., Aldewereld, H., van Belkom, R., Bijvank, R., & Ossewaarde, R. (2020). An agile framework for trustworthy AI. In NeHuAI@ ECAI (pp. 75-78).

Marnada, P., Raharjo, T., Hardian, B., & Prasetyo, A. (2022). Agile project management challenge in handling scope and change: A systematic literature review. Procedia Computer Science, 197, 290-300.

Microsoft (2023). What is the Team Data Science Process? - Azure Architecture Center, Accessed: Nov. 29, 2024. available at: https://learn.microsoft.com/en-us/azure/architecture/ data-science-process/overview.

SDAIA (2024). State of AI in Saudi Arabia, Accessed: Dec. 01, 2024. available at: https://globalaisummit. org/en/Pages/StateAI.aspx.

Tak, A., & Chahal, S. (2024). Risk Management in Agile Al/Ml Projects: Identifying and Mitigating Data and Model Risks. Journal of Technology and Systems, 6(3), 1-18.

Vial, G., Cameron, A. F., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3), 669-691.

West, D. M. (2018). The future of work: Robots, AI, and automation. Brookings Institution Press.

Wilkinson, Caleb, Wheelwright, Geof, and Wohlke, Alexander (2023).¨ AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy, Accessed: Nov. 29, 2024. available at: https://aws.amazon.com/blogs/machine-learning/aws-offersnew-artificial-intelligence-machine-learning-and-generative-aiguides-to-plan-your-ai-strategy/.

Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (Vol. 1, pp. 29-39).

Downloads

Published

2025-08-05

How to Cite

[1]
W. Alsumari and O. Alrwais, “Artificial Intelligence Project Management Methodologies: Insights from Field Experts in Saudi Arabia”, Int. J. Inform. Inf. Sys. and Comp. Eng., vol. 6, no. 2, pp. 246–258, Aug. 2025, doi: 10.34010/injiiscom.v6i2.16127.