Transforming Data into Smarter Decisions Across Engineering Systems and Organizational Management with AI-Driven Optimization

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.

کلیدواژه ها:

AI optimization ، Reinforcement learning ، Predictive analytics ، Engineering systems ، Organizational management