چکیده مقاله
AI powered operational and analytical databases markedly enhance decision making efficiency in engineering project management by automating data processing, delivering predictive analytics for risks and costs, and enabling real time monitoring through integrated dashboards A systematic review of empirical studies across construction, mining, oil & gas, and plant EPC sectors shows substantial gains: _AY% reductions in project management and reporting time for routine operations, _0% shorter project durations and fewer scheduling errors in complex projects, T 20% improvements in cost estimation and risk identification accuracy, and up to 2% lower budget overruns Statistically significant evidence confirms that each point increase in AI integration reduces decision latency by 1, minutes p< , Benefits vary by context greatest time savings emerge in standardized environments with strong data infrastructure, while complex projects primarily gain predictive accuracy Success depends on aligning AI capabilities to specific bottlenecks, ensuring data quality and explainability, and overcoming equally critical organizational barriers through training and cultural adaptation
کلیدواژهها
نویسندگان
Amirhossein Yahyavi
Msc student of business administration MBA , Faculty of Management and Finance Sciences, Department of Management, Khatam University, Tehran, Iran
شیوه ارجاع
Yahyavi, Amirhossein,1404,How AI-Powered Operational and Analytical Databases Boost Decision-Making Efficiency in Engineering Project Management,The 8th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
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