Generative AI for Sustainable Development in the Construction Industry: Exploring Organizational Adoption Patterns

While Generative AI (GenAI) is a critical enabler for achieving key United Nations Sustainable Development Goals (SDGs) in the construction industry, organizations remain hesitant to implement it due to limited awareness of key barriers and the absence of clear adoption strategies. This study aims to (1) identify key barriers and propose strategies for GenAI adoption in the construction industry, and (2) examine how the significance of barriers and the effectiveness of strategies vary across organizations of different types and sizes. Through a systematic literature review and pilot interviews, this study identified 23 barriers and 17 strategies, which were then evaluated using statistical analysis and post‐survey interviews. The findings reveal that “Unclear Legal Liability,” “Lack of Explainability,” and “Lack of Accuracy/Precision” are the most significant barriers, while there is an industry‐wide preference for a passive “wait‐and‐see” stance, prioritizing external, government‐led initiatives over driving internal change for GenAI adoption. The study further uncovers notable differences in barrier significance and strategy effectiveness across organizational contexts. This study provides a structured classification of barriers and strategies, along with comparative insights across organizational types and sizes. Practically, it supports construction organizations in adopting GenAI responsibly and advancing progress toward SDG commitments.