Evaluating the impact of Saudi institutions' readiness to adopt artificial intelligence-based project management methodologies: an applied study in Riyadh
DOI:
https://doi.org/10.59992/IJFAES.2025.v4n7p13Keywords:
Enterprise Readiness, Artificial Intelligence, Project Management, Digital Transformation, Saudi Vision 2030, Technical Infrastructure, Organizational Infrastructure, Corporate Culture, RiyadhAbstract
This research aims to examine the readiness of Saudi organizations to adopt artificial intelligence technologies in project management, in light of the accelerating digital transformation led by Saudi Vision 2030. The study adopted a rigorous quantitative approach, including verifying the validity and reliability of the study tool using Cronbach's alpha coefficient and structural validity analysis. Data were also analyzed using descriptive statistics, correlation and multiple regression analyses, and group difference tests. The sample included 114 valid responses, distributed between the government sector (52.6%) and the private sector (47.4%), with a roughly balanced representation of small (29.8%), medium (40.4%), and large (29.8%) organizations. The data also showed that 50% of participants had between 5 and 10 years of professional project management experience.
Descriptive results revealed that the overall level of readiness of organizations fell within the "average to good" range, with an average technical readiness score of 3.80, organizational readiness of 3.77, and cultural readiness of 3.80. The average actual adoption of AI in project management was 3.68. Key technical strengths included advanced cloud infrastructure (4.12) and effective mechanisms for encrypting sensitive data (4.05). However, some technical gaps emerged that require improvement, such as the inefficiency of high-speed communication networks (3.65), poor ongoing technical support services (3.76), and challenges in integrating systems with AI interfaces (3.79).
Results from correlation and multiple regression analyses confirmed a positive and statistically significant relationship between all readiness dimensions (technical, organizational, and cultural) and the level of AI adoption. The results showed that cultural readiness was the most influential predictor of adoption (beta coefficient = 0.34, p < 0.001), followed by technical readiness (beta = 0.28), and then organizational readiness (beta = 0.21). The statistical model explained 52% of the variance in adoption. Regarding the difference tests, the results showed no statistically significant differences in the level of adoption between government and private institutions (p = 0.27), nor between institutions of different sizes (p = 0.09), indicating that the challenges and opportunities are largely similar across different sectors.
This study provides an important scientific and applied basis for decision-makers in the public and private sectors, emphasizing the importance of balancing technical, organizational, and cultural readiness, while highlighting the critical role of corporate culture in enhancing organizations' ability to adopt AI in project management, thus supporting national digital transformation efforts.
References
المراجع العربية:
1. الشهري، خالد عبد الله؛ باسويد، خلود علي؛ غروي، محمد عبد الله. (2023). واقع الذكاء الاصطناعي على إدارة المشاريع في القطاع الحكومي بالمملكة العربية السعودية. مجلة الفنون والآداب والعلوم الإنسانية والاجتماعية،9(4) ،885. https://doi.org/10.33193/JALHSS.94.2023.885
2. غريب الشهري، وسام؛ إبراهيم أحمد عبدالله، سمية. (2023). أثر استخدام وسائل الذكاء الاصطناعي على تحسين جودة خدمات التوريد في القطاع الحكومي: دراسة تطبيقية على وزارة الموارد البشرية والتنمية الاجتماعية بالمملكة العربية السعودية. مجلة العلوم الاقتصادية والإدارية والقانونية، 8(2). https://doi.org/10.26389/AJSRP.A020923
3. الغامدي، سلوى؛ آل ضرمان، فالح عبد الله. (2022). عمليات إدارة المعرفة القائمة على الذكاء الاصطناعي في المشاريع الإنشائية: دراسة تطبيقية في المملكة العربية السعودية. مجلة ابن خلدون للدراسات والأبحاث،2(7)، 1–18. https://doi.org/10.56989/benkj.v2i7.276
4. القحطاني، غادة علي سعد. (2022). واقع استخدام الذكاء الاصطناعي في إدارة الموارد البشرية ومعوقاته ومتطلبات تطبيقه بجامعة الملك سعود من وجهة نظر هيئة التدريس. مجلة العلوم التربوية والنفسية،6(55)، 1–23. https://doi.org/10.26389/AJSRP.Q150622
5. السالم، كوثر علي. (2024). أثر استخدام الذكاء الاصطناعي على أداء المراجعين في بيئة الأعمال السعودية. مجلة العلوم الاقتصادية والإدارية والقانونية،8(4)، 114–126. https://doi.org/10.26389/AJSRP.K140124
المراجع الإنجليزية:
1. Alarefi, M. (2024). The Impact of Artificial Intelligence on Business Performance in Saudi Arabia: The Role of Technological Readiness and Data Quality. Engineering, Technology & Applied Science Research, 14(5), 16802–16807. https://doi.org/10.48084/etasr.7871
2. Alsaedi, A. R., Alneami, N., Almajnoni, F., Alamri, O., Aljohni, K., Alrwaily, M. K., & Eid, M. H. (2024). Perceived Worries in the Adoption of Artificial Intelligence Among Healthcare Professionals in Saudi Arabia: A Cross - Sectional Survey Study. Nursing Reports, 14(4), 3706–3721. https://doi.org/10.3390/nursrep14040271
3. Altaie, N., & Dishar, M. (2024). Knowledge Management and AI Integration in Construction Project Delivery. Civil Engineering Journal, 10(2), 124–139. https://www.civilejournal.org/index.php/cej/article/view/124
4. Datta, S. D., Islam, M. M., Islam, M., Sobuz, M. H. R., Ahmed, S., & Kar, M. K. (2024). Artificial Intelligence and Machine Learning Applications in the Project Lifecycle of the Construction Industry: A Comprehensive Review. Heliyon, 10(5), e26888. https://doi.org/10.1016/j.heliyon.2024.e26888
5. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.
6. Felemban, H., Sohail, M., & Ruikar, K. (2024). Exploring the Readiness of Organisations to Adopt Artificial Intelligence. Buildings, 14(8), 2460. https://doi.org/10.3390/buildings14082460
7. Grant, R. M. (1996). Toward a Knowledge - Based Theory of the Firm. Strategic Management Journal, 17(S2), 109–122.
8. Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
9. Sekaran, U., & Bougie, R. (2019). Research Methods for Business: A Skill - Building Approach (7th ed.). Wiley.
10. Taboada, I., Daneshpajouh, A., Toledo, N., & de Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 13(8), 5014. https://doi.org/10.3390/app13085014
11. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18(7), 509–533.
12. Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
13. Ujaimi, I., Alsaigh, R., Alfelfel, M., & Algallaf, R. (2024). Adoption of Artificial Intelligence in the Construction Industry in Saudi Arabia: Challenges and Proposed Solutions. International Journal of Social Sciences & Humanities Research, 3(11). https://doi.org/10.58806/ijsshmr.2024.v3i11n18
14. Varshney, S. (2025, June 10). Winning the AI Long Game. Business Insider. https://www.businessinsider.com/winning - the - ai - long - game - varshney - 2025 - 6
15. Zabala - Vargas, A. (2023). Big Data, Data Science and AI in Project Management: A Systematic Review. Journal of Construction Engineering and Management, 149(7), 04023055. https://doi.org/10.1061/(ASCE)CO.1943 - 7862.0002374
