Towards generative AI-driven construction scheduling: challenges, opportunities, and agentic AI framework

The construction industry faces challenges in achieving optimal time performance which directly affects scheduling reliability. This study presents a state-of-the-art analysis of the challenges, influencing factors and emerging artificial intelligence (AI) approaches in construction scheduling. It highlights the limitations of existing scheduling approaches which fail to address the inherent uncertainties including human dynamics, resource constraints and external factors. Bridging the gap, this study explores how Generative AI (GenAI) can incorporate dynamics in scheduling uncertainties with a specific focus on Agentic AI. This paper synthesises a conceptual three-layered framework of data, reasoning and decision layers to support adaptive and explainable construction scheduling. Future research should focus on enhancing human collaboration and incorporating Explainable AI (XAI) for transparency in AI-driven scheduling models. These advancements improve the accuracy, flexibility, and user acceptance of scheduling systems. The framework provides a foundation for future research on intelligent, transparent and adaptive construction scheduling systems.