Abstract The integration of Artificial Intelligence (AI) into construction project management is reshaping how projects are planned, executed, and controlled, particularly in the context of the Fourth Industrial Revolution (4IR). This study explores the influence of AI on key project management functions, namely cost management, schedule control, and decision-making within the South African construction industry. A qualitative desktop research approach was employed, relying on a systematic review of peer-reviewed journal articles, industry reports, and policy documents published between 2017 and 2025. Thematic analysis was used to synthesise and interpret the literature. The results indicate that AI technologies such as machine learning, predictive analytics, computer vision, and BIM-integrated systems significantly improve project cost estimation, financial monitoring, and resource allocation. In scheduling, AI enhances planning accuracy by enabling real-time progress tracking, predictive delay identification, and adaptive rescheduling through data captured from digital tools such as drones and sensors. Furthermore, AI strengthens decision-making and risk management by providing data-driven insights, early warning systems, and scenario-based forecasting, thereby reducing uncertainty and improving project control. However, the study also highlights that AI adoption in South Africa remains in an emerging stage and is not yet widely implemented across the construction sector. Challenges such as limited digital infrastructure, high implementation costs, fragmented project data, shortage of skilled professionals, and resistance to technological change continue to hinder widespread adoption, particularly among small and medium-sized enterprises. The study concludes that AI has the potential to significantly enhance construction project performance by improving efficiency, accuracy, and risk responsiveness. To fully realise these benefits, the South African construction industry must prioritise digital transformation through investment in technology, skills development, and supportive institutional frameworks that encourage innovation and integration of AI-driven systems.
NKOSI et al. (Thu,) studied this question.