Adopting Large Language Models in the Construction Industry: Drivers, Barriers, and Strategic Implications from China

The rapid advancement of AI, especially large language models (LLMs), brings opportunities and challenges to industries. In construction, LLMs can enhance project coordination, support decision-making and reduce workload, but adoption is limited by hallucination, data security and domain complexity. This study investigates the current state of LLM adoption in China’s construction industry through a four-step approach, including a comprehensive literature review to identify potential drivers and barriers, questionnaire design and data collection for empirical analysis, and the application of the Entropy Weight Method (EWM) to quantify and rank the relative importance of each factor. The findings reveal that the top drivers originate at the company level, including strategic partnerships, internal research teams, and staff training—highlighting the central role of organizational readiness in enabling LLM integration. Conversely, the most critical barriers are embedded in the construction domain itself, including knowledge gaps, workflow integration, and data heterogeneity, which reflect structural limitations in the sector. Although LLM implementation remains in its early stages, survey responses show widespread optimism among stakeholders regarding its future potential. The study proposes several actionable strategies for both construction firms and policymakers to facilitate effective LLM adoption. Moreover, the identified drivers and barriers are not exclusive to construction but are also relevant to other digitally transforming sectors—such as manufacturing, healthcare, and energy—offering broader implications for AI adoption in complex, project-based environments.