AI-Enabled Predictive Risk Analytics for Enhanced Project Decision-Making: Insights from the CPMAI Framework

Abstract

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.

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