As the wave of artificial intelligence sweeps across the globe, manufacturing is becoming a new competitive highland. In the 2026 National People's Congress and Chinese People's Political Consultative Conference, "Creating a New Form of Intelligent Economy" will be included for the first time in the government work report, becoming an important direction for the future implementation of artificial intelligence applications. Recently, reporters have conducted interviews in multiple parts of Zhejiang, a major manufacturing province, and learned that artificial intelligence is moving from laboratories to production lines, deeply embedded in research and development design, production and manufacturing, and enterprise management, driving profound changes in traditional manufacturing. The "two-way rush" between artificial intelligence and manufacturing is accelerating. Artificial intelligence accelerates the profound reshaping of the manufacturing industry in the workshops of multiple enterprises in Zhejiang. The reporter saw that the current integration of artificial intelligence and manufacturing is moving from single point pilot to full process penetration, reconstructing the value creation logic of key links such as research and development, production, operation, and service, and achieving multiple goals such as efficiency improvement, cost reduction, quality optimization, and scene expansion. ——Promote fundamental changes in production models. Entering the Hangzhou Zhongce Rubber High Performance Radial Tire Intelligent Factory, dozens of Automatic Guided Vehicles (AGVs) shuttle precisely along the predetermined route, and the robotic arm completes the tire vulcanization process with micrometer level accuracy. Jiang Zhiqiang, General Manager of Chaoyang Company of Zhongce Rubber Group, said that the AI workshop can produce one tire at an average speed of every 3.1 seconds without interruption, increase production efficiency by 300%, and reduce product defect rate to 0.5%. ——Optimize and upgrade resource allocation. In the past, problems such as opaque production processes, chaotic inventory, and extensive equipment management have long plagued the manufacturing industry. Relying on its self-developed full process data-driven management platform, Zhejiang Ruiying Sensing Technology Co., Ltd. has improved its response speed in material procurement, inventory management, and logistics distribution by more than 50%. Shi Xiaohu, the head of the IT department at Ruiying, stated that empowering the business chain with AI technology can accelerate the construction of a modern management system that is transparent in business processes, controllable in production processes, and measurable in business results. ——Refactoring the link between consumption and production. Nowadays, more and more enterprises are using AI algorithms to analyze user behavior data and launch personalized customized products, such as intelligent mass customization in the clothing industry and scenario based functional configuration in the home appliance industry. Qisheng Technology Co., Ltd., located in Jiaxing, Zhejiang, has developed and constructed a sleep intervention algorithm model, and used the produced smart bed products as early screening carriers to provide consumers with sleep quality monitoring. The chairman of the company, Tang Guohai, stated that the empowerment of AI technology is driving manufacturing enterprises to transform from product providers to solution providers. The production landscape in different types of factories clearly points to the same trend: artificial intelligence is evolving from the application of technology in individual workshops to an important force that affects the operation of the entire manufacturing system. According to a report by International Data Corporation (IDC), the penetration rate of intelligent agents in Chinese industrial enterprises has increased from 9.6% in 2024 to 47.5% in 2025. Standing at a historical turning point, the Ministry of Industry and Information Technology has clearly set a goal of promoting 500 typical application scenarios of "AI+manufacturing" by 2027, which also marks a key leap from "single point pilot" to "industry wide universal" AI application in China's manufacturing industry. Although the deep integration of artificial intelligence and manufacturing is rapidly entering the application deployment stage, interviewed entrepreneurs and experts in related fields also stated that there are still key pain points such as model security, data barriers, application costs, and talent supply that need to be overcome in the "last mile" of AI entering the factory. Yu Huanjie, the development manager of Ruiying Technology, said that currently, some companies have "AI anxiety" and regard AI as a "panacea" that can solve industrial problems. "Existing large models perform well in semantic understanding and statistical correlation, but there are still limitations in understanding physical rules and spatial reasoning. In the industrial field, the production chain is long and the data and knowledge involved are complex. AI may produce directive errors, causing irreversible systemic risks to the production process. ”Additionally, there are challenges in the matching accuracy of AI. Chen Yu, Director of Furuidek (Zhejiang) Intelligent Systems Co., Ltd., said that companies have encountered a situation where the performance of models trained in laboratory environments deteriorates after deployment to real and changing industrial scenarios. This is because there are differences between real data and training data, and this dependence on specific physical scenarios leads to a need to improve the matching degree between AI solutions and manufacturing processes. The "data barrier" and computing power cost affect the deployment optimization of AI in the manufacturing industry. Ji Yaohua, Chairman of Hangzhou Chicheng Digital Technology Co., Ltd., frankly stated that the reason why manufacturers find it difficult to scale up the application of AI is that their data foundation is still weak. The production data of manufacturing enterprises often involves core secrets and intellectual property, and there are barriers to data sharing across enterprises and even between different branches within the enterprise. Without access to high-quality data, the further development of industrial AI models is constrained, and AI applications are difficult to achieve global optimization. The cost of computing power also constrains the large-scale application of AI technology in the manufacturing industry. Many interviewed business leaders expressed difficulty in bearing the high cost of computing power. We have calculated that if we build our own computing power server, the cost of just the graphics card would be 12 million yuan. Now we can only choose to use public cloud services, but it will also create new problems such as data security and network latency, "said Shi Xiaohu. It is worth noting that the shortage of AI composite talents hinders the pace of integrated development. The interviewee stated that there is still a gap in the supply of intelligent manufacturing talents in China, with a shortage of high-end compound R&D talents and insufficient adaptation of frontline skilled talents to intelligence, which hinders the process of integrated development. Cao Hui, Deputy General Manager of Qisheng Technology Co., Ltd., stated that from the perspective of enterprise recruitment, some highly educated individuals lack work experience and have obvious deficiencies in equipment operation, demand conversion, and other aspects; Some skilled professionals who understand manufacturing are not very familiar with AI, making it difficult to translate business needs into AI application scenarios. Accelerating the construction of an "artificial intelligence+manufacturing" supply system. Recently, eight departments including the Ministry of Industry and Information Technology, the Cyberspace Administration of China, and the National Development and Reform Commission issued the "Implementation Opinions on the Special Action of" Artificial Intelligence+Manufacturing "to accelerate the integration and application of artificial intelligence technology in the manufacturing industry. During the visit, multiple interviewees expressed that empowering the manufacturing industry with artificial intelligence is not a multiple-choice question, but a mandatory one. Systematic changes should be made in strategic planning, ecological construction, talent cultivation, and governance systems to break through integration barriers and continue to maintain the global leading advantage of empowering the manufacturing industry with artificial intelligence. On the one hand, at the government level, we need to focus on pilot projects, set models, promote promotion, and create a first-class policy support environment. Zhang Tianren, Chairman of Tianneng Holdings Group, stated that it is necessary to systematically plan from the national strategic level, introduce special policies, support the construction of industrial cluster level artificial intelligence empowerment platforms, reduce technology application costs, solve the digital dilemma of small and medium-sized enterprises, and accelerate the popularization, penetration, and deep empowerment of artificial intelligence in industrial clusters. Ji Yaohua and others suggest that the government and industry associations should establish public service platforms, provide basic resources such as computing power, data, algorithms, etc., and lower the threshold for innovation. For example, by focusing on manufacturing data platforms, vertical models, and industry shared knowledge bases, we can jointly build industry level "intelligent agent brains", jointly establish industry data standards, promote high-quality supply of data resources, and provide core data support for "artificial intelligence+manufacturing". On the other hand, we need to strengthen the cultivation of AI composite talents. Cao Hui suggests that we should base ourselves on the actual needs of the industry, break down barriers between universities, research institutes, and enterprises, deepen the integration of industry and education, and integrate science and education, optimize talent training programs, add courses related to the integration of artificial intelligence and manufacturing, accurately cultivate compound talents who are familiar with both manufacturing production processes and artificial intelligence technology, and achieve the simultaneous connection between talent training and industry demand. Xu Guanju, Chairman of Chuanhua Group, said that the construction of a "artificial intelligence+manufacturing" composite talent training and certification system will focus on cultivating "industrial AI architects" who understand the industry and AI. Develop standards for identifying the abilities of composite talents in the era of artificial intelligence, clarify the training direction and evaluation criteria for "industrial AI architects", carry out full chain talent training, and accelerate the large-scale supply of composite talents. (New Society)