Abstract: In recent years, AI technology, leveraging massive construction data from the building industry, has played a significant role in process optimization, safety management, process control, as well as component recognition and structural optimization design based on deep learning (CNN, GAN). Although existing research has covered directions such as technology application paths, deployment of construction robots, and data-driven management, there are still issues such as insufficient deep mining of large-scale project data, inadequate integration of design and construction, poor adaptability for small and medium-sized enterprises, and unintegrated research on multi-source safety monitoring data. Addressing the aforementioned issues, this paper constructs a quantitative analysis system covering the entire lifecycle of design, construction, and operation and maintenance using typical project data. It explores the implementation path of AI for small and medium-sized enterprises and proposes a four-dimensional hierarchical control scheme based on data-driven, technology adaptation, safety collaboration, and talent cultivation. The research shows that AI has achieved remarkable results in improving construction efficiency, ensuring safety, optimizing management, and controlling costs. However, it still faces challenges such as high technology costs, shortage of professional talents, and data security risks. In the future, with the popularization of AI technology, it will become a strategic investment to enhance the core competitiveness of the industry, promoting the development of the building industry towards intelligence, efficiency, and safety, and providing theoretical and practical guidance for the digital transformation of enterprises.