The Role of Artificial Intelligence in Enhancing Operational Efficiency and Cost Optimization in Engineering-Driven Enterprises

The business environment of engineering-driven enterprises is characterized by complex projects, strict deadlines, and financial constraints, which means that operational efficiency serves as a major success factor. Conventional project management and resource distribution techniques often become ineffective because they fail to account for the complexity of the task dependencies and resource constraints, thereby contributing to significant cost overruns and schedule slippage. These multi-objective optimization problems can be solved with advanced computational capabilities offered by the integration of Artificial Intelligence (AI), which provides a paradigm shift. This paper proposes an AI-based framework using a Genetic Algorithm (GA) to optimize both the cost and duration of a project simultaneously. A typical priority-based heuristic scheduling technique serves as the baseline, and the performance of the proposed GA model is thoroughly evaluated using a quantitative and simulation-based methodology. According to the simulation results, the AI-based solution is statistically significant and reduces project expenses by 18.2% and project time by 23.5% when compared to the conventional option. Additionally, by providing a Pareto front of optimal alternatives and demonstrating improved resource use, the AI model enables decision-makers to make flexible, data-driven strategic decisions.