AI-Enabled Predictive Risk Analytics for Enhanced Project Decision-Making: Insights from the CPMAI Framework
DOI:
https://doi.org/10.54489/mxs6z105Keywords:
AI-driven risk analytics, Predictive intelligence, Project risk management, CPMAI framework, Decision-support systems, Risk identification and responseAbstract
Abstract
Purpose/Aim: The research aims to assess the effects of AI-enabled predictive analysis on project risk identification, analysis, and response efficiency.
Methods: Both qualitative and quantitative approaches were used, utilizing primary interviews and surveys.
Key findings: The survey results gathered from risk/project managers indicate the use of AI, analytics, and predictive tools, and identify high costs and data quality risks as key issues. The variance in project risk management lies in early-stage risk identification, analysis, and response, as well as in high-accuracy project management. Additionally, project risk management has enabled fast decision-making within the proper budget, leading to project success.
Conclusions and implications: This study demonstrates that AI-powered predictive analytics is crucial for identifying risks at the initial phases of projects, thereby improving decision-making on risk, cost, and time. The study further confirms that despite some setbacks, such as poor data quality and expense, the use of artificial intelligence in project risk management remains beneficial.
References
References
Abayomi, A. A., Uzoka, A. C., Ubanadu, B. C., & Elizabeth, C. A. (2024). A conceptual framework for enhancing business data insights with automated data transformation in cloud systems. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2076-2084. https://doi.org/10.62225/2583049X.2024.4.6.4253
Adepoju, A. H., Austin-Gabriel, B., Eweje, A., & Hamza, O. (2023). A data governance framework for high-impact programs: Reducing redundancy and enhancing data quality at scale. International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), 1141–1154. https://doi.org/10.54660/.IJMRGE.2023.4.6.1141-1154
Afzal, F., Yunfei, S., Nazir, M., & Bhatti, S. M. (2021). A review of artificial intelligence-based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects. International Journal of Managing Projects in Business, 14(2), 300–328. https://doi.org/10.1108/IJMPB-02-2019-0047
Anwar, N., Anderson, J., & Williams, T. (2024). Applying data science to analyze and improve student learning outcomes in educational environments. International Transactions on Education Technology (ITEE), 3(1), 72-83. DOI:10.33050
Bakhshandeh, B., Zaballero, A. G., & Rothwell, W. J. (2025). The Organized and Engaged Supervisor: Optimizing Resources and Managing Employee Experiences. CRC Press.
Cina, E., Elbasi, E., Elmazi, G., & AlArnaout, Z. (2025). The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities. Sustainability (2071-1050), 17(11). https://doi.org/10.3390/su17115148
Coito, T., Firme, B., Martins, M. S., Vieira, S. M., Figueiredo, J., & Sousa, J. M. (2021). Intelligent sensors for real-Time decision-making. Automation, 2(2), 62-82. https://doi.org/10.3390/automation2020004
Cornerstoneprojects.co, (2023). Delays in the construction industry: Our 2022 survey results and how they compare to 2016. Retrieved from: https://www.cornerstoneprojects.co.uk/blog/delays-in-the-construction-industry-our-2022-survey-results-and-how-they-compare-to-2016/ [Retrieved on 11.11.2025]
EIA, (2025). Fewer U.S. solar projects are reporting delays in their expected online date. Retrieved from: https://www.eia.gov/todayinenergy/detail.php?id=66604 [Retrieved on 11.11.2025]
Gartner (2025). Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027. Retrieved from: 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 [Retrieved on 11.11.2025]
Gartner, (2025). Lack of AI-Ready Data Puts AI Projects at Risk. Retrieved from: https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk [Retrieved on 11.11.2025]
Gaube, S., Suresh, H., Raue, M., Lermer, E., Koch, T. K., Hudecek, M. F., ... & Colak, E. (2023). Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays. Scientific reports, 13(1), 1383. https://doi.org/10.1038/s41598-023-28633-w
Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. https://doi.org/10.3390/socsci12090502
Grand View Research, (2025). AI Trust, Risk And Security Management Market (2025 - 2030). Retrieved from: https://www.grandviewresearch.com/industry-analysis/ai-trust-risk-security-management-market-report [Retrieved on 11.11.2025]
Hendren, K., Newcomer, K., Pandey, S. K., Smith, M., & Sumner, N. (2023). How qualitative research methods can be leveraged to strengthen mixed methods research in public policy and public administration?. Public Administration Review, 83(3), 468-485. https://doi.org/10.1111/puar.13528
Liang, W., Tadesse, G. A., Ho, D., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022). Advances, challenges, and opportunities in creating data for trustworthy AI. Nature Machine Intelligence, 4(8), 669-677. https://doi.org/10.1038/s42256-022-00516-1
McKinsey, (2023). Seize the decade: Maximizing value through preconstruction excellence. Retrieved from: https://www.mckinsey.com/capabilities/operations/our-insights/seize-the-decade-maximizing-value-through-pre-construction-excellence [Retrieved on 11.11.2025]
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090
Nii Laryeafio, M., & Ogbewe, O. C. (2023). Ethical consideration dilemma: systematic review of ethics in qualitative data collection through interviews. Journal of Ethics in Entrepreneurship and Technology, 3(2), 94-110. https://doi.org/10.1108/JEET-09-2022-0014
Perifanis, N. A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85. https://doi.org/10.3390/info14020085
Raimundo, R., & Rosário, A. (2021). The impact of artificial intelligence on data system security: A literature review. Sensors, 21(21), 7029. https://doi.org/10.3390/s21217029
Soni, C. (2025). Leveraging Artificial Intelligence for Predictive and Adaptive Decision-Making in Construction Project Management. International Journal of Technology, Management and Humanities, 11(04), 5-15. https://doi.org/10.21590/ijtmh.11.04.02
Tourangeau, R. (2021). Survey reliability: models, methods, and findings. Journal of survey statistics and methodology, 9(5), 961-991. doi: 10.1093/jssam/smaa021
Vergara, D., del Bosque, A., Lampropoulos, G., & Fernández-Arias, P. (2025). Trends and applications of artificial intelligence in project management. Electronics, 14(4), 800. https://doi.org/10.3390/electronics14040800
Vudugula, S., Chebrolu, S. K., Bhuiyan, M., & Rozony, F. Z. (2023). Integrating artificial intelligence in strategic business decision-making: A systematic review of predictive models. International Journal of Scientific Interdisciplinary Research, 4(1), 01-26. https://doi.org/10.63125/s5skge53
Wan, Z., Liao, P. C., Liao, X., & Chong, H. Y. (2024). Enhancing Sustainable Decision Making: The Impact of Emotional Valence, Arousal, and Personality on Risk Recognition During Contract Review. Buildings, 14(12), 3878. https://doi.org/10.3390/buildings14123878
Westenberger, J., Schuler, K., & Schlegel, D. (2022). Failure of AI projects: understanding the critical factors. Procedia Computer Science, 196, 69-76. https://doi.org/10.1016/j.procs.2021.11.074
Yazdi, M., Zarei, E., Adumene, S., & Beheshti, A. (2024). Navigating the power of artificial intelligence in risk management: a comparative analysis. Safety, 10(2), 42. https://doi.org/10.3390/safety10020042
Younas, A., Fàbregues, S., & Creswell, J. W. (2023). Generating metainferences in mixed methods research: A worked example in convergent mixed methods designs. Methodological Innovations, 16(3), 276–291. https://doi.org/10.1177/20597991231188121
Zhang, S., Yu, X., Shi, X., & Zhang, Y. (2023). The influencing mechanism of incidental emotions on risk perception: evidence from event-related potential. Brain sciences, 13(3), 486. https://doi.org/10.3390/brainsci13030486
