چکیده مقاله
AI driven optimization revolutionizes decision making in engineering systems and organizational management by leveraging advanced techniques such as machine learning, deep learning, reinforcement learning, and predictive analytics In engineering applications, research demonstrates significant improvements, including ۱۷ ۵۵% enhanced forecast accuracy, ۹ ۵% anomaly detection, up to ۵% energy savings, and ۹% latency reduction, with cost reductions reaching ۲ ۰۷% In organizational contexts, AI methods like natural language processing and explainable AI improve decision accuracy by up to ۱% and accelerate processing by %, while extending critical asset lifespans, such as electric submersible pumps Spanning domains like energy, smart buildings, logistics, and business operations, these advancements highlight AI's transformative potential However, challenges including technical complexities, interoperability constraints, data privacy concerns, and skill shortages persist Drawing from academic studies, this review synthesizes how AI driven optimization enhances efficiency, sustainability, and decision quality across diverse sectors, while noting that business applications often report qualitative gains These findings emphasize AI's role in smarter decision support and the need to address implementation barriers
کلیدواژهها
نویسندگان
Mohammad Bahrami
Msc student of business administration MBA , Department of Management, science and Technology, Amirkabir University of Technology, Tehran, Iran
شیوه ارجاع
Bahrami، Mohammad،1404،Transforming Data into Smarter Decisions Across Engineering Systems and Organizational Management with AI-Driven Optimization،هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات،مشهد
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات21 مرداد 1404 · مشهد