AI in Construction Management: Preparedness and Potential

Paper i proceeding, 2026

To address the constant challenges related to project delays and low productivity in the construction industry, this paper explores the opportunity of integrating machine learning based predictive models to improve decision-making in construction project management. In collaboration with NCC, a case study of the Ingelkärr–Stenkullen transmission line project was conducted to develop a hybrid forecasting model that combines Monte Carlo simulations with neural–network–based machine learning. The initial results showed high predictive accuracy (R²=0.92) and updates weekly with the actual progress, enabling adaptive learning. The proposed framework shows strong potential to transform industry practices by significantly improving risk forecasting, optimizing resource management, and increasing responsiveness to uncertainty, thereby offering a pathway to more efficient and resilient project management in construction.