AI-Enabled Construction Management: A Framework for Scalability, Digital Integration, and Sustainability

Abstract

The increasing complexity of construction projects requires management systems capable of integrating heterogeneous information, supporting distributed decision-making, and scaling across projects with different operational conditions. This paper develops a conceptual framework for AI-enabled construction management by adapting insights from research on dialogue systems, conversational artificial intelligence, knowledge-aware multimodal systems, human feedback, and retrieval-generation architectures. Although the supplied literature primarily concerns conversational and dialogue-based artificial intelligence rather than construction management, its underlying principles provide a  useful theoretical foundation for designing intelligent construction-management interfaces. The proposed framework integrates four functional layers: data and knowledge integration, AI reasoning and retrieval, human-AI interaction, and scalable decision support. Sustainability is incorporated as a cross-cutting performance dimension through resource efficiency, operational coordination, waste reduction, and lifecycle-oriented decision support. The analysis indicates that scalability depends not only on computational capacity but also on the ability of AI systems to preserve contextual knowledge, incorporate human feedback, and coordinate heterogeneous information. The framework further identifies digital integration and human oversight as essential conditions for reliable AI adoption. The paper contributes a research-oriented socio-technical perspective in which AI is treated not merely as an automation technology but as an intelligent coordination layer between construction data, managerial knowledge, operational actors, and sustainability objectives.