Integration of Artificial Intelligence in Activity-Based Project Costing: Enhancing Accuracy and Efficiency in Project Cost Management

Abstract

Activity-Based Project Costing (ABPC) has long been recognized as an effective method for managing project costs. However, the increasing complexity of modern projects demands more sophisticated approaches. This study explores the integration of Artificial Intelligence (AI) into ABPC to enhance cost estimation accuracy and project management efficiency. By utilizing machine learning algorithms and big data analysis, it has been developed an AI-ABPC model capable of predicting project activity costs with higher precision, identifying hidden patterns in historical data, and providing real-time cost optimization recommendations. A case study of 50 large-scale construction projects showed that the AI-ABPC model improved cost estimation accuracy by 30% and reduced cost analysis time by 40% compared to traditional ABPC methods. These findings pave the way for a revolution in project cost management, enabling faster and more accurate decision-making in dynamic project environments. The implementation of AI in ABPC not only enhances project financial performance but also fosters innovation in overall project management practices.