The "Digital Transformation Development Report of Manufacturing Industry (2025)" recently released by the China Academy of Information and Communications Technology shows that currently, the coverage of digital transformation in China's manufacturing industry has significantly increased, entering the stage of large-scale popularization. Among them, digital intelligence technologies such as artificial intelligence and digital twins will be fully embedded in the entire manufacturing chain, and core software and hardware products will iterate towards standardization and modularization, transforming the ecosystem and expanding. Strengthening intelligent manufacturing is not only helpful in solving the development problems of the manufacturing industry, but also a core measure to accelerate the construction of a strong manufacturing country and cultivate and develop new quality productivity. The deep integration of artificial intelligence and manufacturing is essentially the close integration of new production factors such as data, computing power, and algorithms with traditional manufacturing processes, constructing an intelligent manufacturing system with autonomous perception, collaborative decision-making, and real-time evolution. Currently, the digital transformation of enterprise equipment has significantly improved autonomous perception capabilities. As of January this year, China has built more than 35000 basic level, 8200 advanced level, and 500 excellent level intelligent factories, and cultivated 15 leading level intelligent factories, providing solid hardware support for capturing manufacturing data and monitoring operation processes. At the same time, the aggregation of computing power and data resources promotes efficient collaborative decision-making. The construction of intelligent computing centers effectively alleviates the bottleneck of computing power, while big data platforms transform data into valuable assets and enhance the ability of human-machine collaboration. In addition, with the continuous increase in the proportion of artificial intelligence applications, the production efficiency of the manufacturing industry has been further improved. Looking at the world, major developed economies have identified intelligent manufacturing as the core strategy for reshaping the manufacturing industry, but each has its own focus on its development path. The United States relies on its absolute advantages in original innovation in artificial intelligence and the field of chips, focusing on promoting disruptive applications of AI in high-end manufacturing fields such as aerospace and biomedicine through technology spillovers from tech giants. In contrast, European countries represented by Germany, relying on their profound industrial accumulation, pay more attention to the standardization and interaction of industrial data, privacy protection, and the formulation of AI ethical norms. They are committed to achieving intelligent upgrading of manufacturing systems through cross-border collaboration and standard guidance. Compared with foreign countries, China has the most complete industrial system in the world, providing rich application scenarios for artificial intelligence technology. For example, in the field of new energy vehicles, AI empowers multiple stages of automotive manufacturing, such as stamping, welding, painting, and final assembly, effectively improving production efficiency; In the field of consumer electronics, the application of artificial intelligence drives the synchronous improvement of production efficiency and product quality, helping to significantly reduce the defect rate of high-end products. More importantly, the new national system can be guided by national strategies and supported by large-scale industrial funds to concentrate efforts on tackling common technological challenges, quickly building a "point to area" promotion pattern, and forming an ecosystem that deeply integrates policy chains, industrial chains, and capital chains. However, the integration of artificial intelligence and manufacturing in China still faces the dilemma of insufficient high-quality industrial corpora and training data. The shortage of versatile talents has increased the difficulty of applying artificial intelligence technology in the manufacturing industry. Some enterprises have unclear understanding of human-machine collaboration, resulting in the potential of intelligent agents not being fully realized in practical scenarios. In response to challenges, precise measures need to be taken. Strengthen the position of enterprises as the main body of innovation and innovate the talent training system. Guide the upgrading of enterprise hardware infrastructure and the construction of technological foundations, support leading enterprises to take the lead, accelerate the digital transformation of industrial equipment, and build high-quality corpora. Build a dedicated AI suite adapted to industrial scenarios, promoting the integrated application of cutting-edge technologies such as digital twins and generative AI in industrial design, process optimization, and management. Encourage enterprises to form innovation alliances with universities and research institutes, and cultivate composite talents who possess both engineering thinking and digital skills in practical situations. Expand high-value application scenarios and accelerate the industrialization process. Focus on strategic emerging industries such as integrated circuits, aerospace, and new energy vehicles, select a number of typical application scenarios for demonstration and promotion, and solve the problem of "not daring to use, not knowing how to use". Build an artificial intelligence innovation application demonstration zone to promote enterprises to explore new intelligent manufacturing models such as flexible manufacturing, black light factories, and virtual factories. Encourage various entities to develop "small models" targeting specific niche fields, forming a collaborative ecosystem of "universal large models as the foundation and specialized small models to solve difficult problems", and reducing the application threshold for small and medium-sized enterprises. Optimize the funding guarantee system and create a good industrial ecology. Fully leverage the guiding role of policy funds such as the National Manufacturing Transformation and Upgrading Fund, establish a reserve of high-quality projects, and leverage more social capital to invest in the field of intelligent manufacturing. Promote various inclusive financial policies to reduce the cost of computing power usage for small and medium-sized enterprises. Accelerate the improvement of basic systems such as data property rights, circulation and trading, and security governance, establish cross departmental and cross regional collaborative supervision mechanisms, and provide a stable, transparent, and predictable policy environment for the deep application of artificial intelligence technology while preventing low-level redundant construction of industries. Liu Lindong and Zhang Xun are respectively the Vice Dean and Professor of the School of Science and Technology Business at the University of Science and Technology of China; Associate Researcher (New Press)