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 10 empirical studies across construction, mining, oil & gas, and plant EPC sectors shows substantial gains: 50–83% reductions in project management and reporting time for routine operations, 20–35% shorter project durations and fewer scheduling errors in complex projects, 30–45% improvements in cost estimation and risk identification accuracy, and up to 40% lower budget overruns Statistically significant evidence confirms that each 10 point increase in AI integration reduces decision latency by 1 48 minutes p
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 7th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
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