Intelligent Strategic Decision-Making for Optimized Project Management

This research explores the application of AI-driven strategies in engineering project management, focusing on optimizing decision-making in areas such as risk assessment, resource distribution, and performance tracking. The study reveals that AI-based predictive analytics enhance risk identification accuracy, allowing for proactive measures that reduce project delays. Resource optimization models effectively streamline allocations, minimizing waste and ensuring consistent resource availability. Furthermore, real-time performance monitoring provides project managers with actionable insights, improving responsiveness to emerging challenges and contributing to reliable projects. While these methods demonstrate significant advantages, the study identifies challenges related to technical expertise and data requirements, which may limit AI implementation. The findings underscore AI's role in supporting complex project decisions, suggesting that further research could expand its applicability and establish ethical guidelines for its use in decision-making frameworks.