Leveraging AI for Enhanced Engineering Management: Transforming Decision-Making Processes and Project Efficiency - S, Savin Mayur; N, Chandana (Journal of Global Economy, Business and Finance, 2025)
The rapidly evolving engineering management arena pushes the decision - making process out of its comfort zone with escalating project complexity, vast amounts of generated data, and a need for timely and accurate decisions. Though effective in previous times, traditional methodologies for decision - making often fall short of effectively dealing with the intricate dynamics of modern engineering projects. The potential of Artificial Intelligence to drive transformational changes in the decision - making process within engineering management is discussed in this paper. Models of Artificial Intelligence, such as machine learning algorithms, predictive analytics, and optimization techniques, make it quite feasible for an engineering manager to progress effectively toward better decision - making based on evidence. The study begins with a trace of the limitations of traditional decision - making approaches and the challenges inherent to engineering managers in project planning, resource allocation, and risk management. Through case studies and simulations, the paper demonstrates how AI - driven decision - making can result in efficient resource use, project risk reduction, and optimized project scheduling. Case studies presenting successful implementations of AI in engineering projects outline improvements in project timelines, cost efficiency, and management effectiveness. The paper also raises issues related to the ethical concerns and challenges that might emanate from the application of AI in engineering management, such as concerns for personal data protection and bias in AI models. The main conclusion is that while AI has enormous benefits, it must be meticulously introduced with an apparent understanding of its limitations and potential risks. This paper discusses the transformative potential of Artificial Intelligence AI in engineering management, specifically in decision making processes. By utilizing AI models such as machine learning, predictive analytics, and optimization techniques, engineering managers can enhance decision- making, optimize resources, and mitigate risks. The paper explores the limitations of traditional decision making methods and presents case studies that demonstrate the benefits of AI driven decision making in various engineering projects. Additionally, it addresses the ethical concerns and challenges associated with AI implementation, emphasizing the need for a balanced and informed approach.