Digital News

　　As the new wave of sci-tech revolution and industrial transformation accelerates, the deep integration of artificial intelligence (AI) with traditional industries has emerged as a new driving force for their transformation and upgrading. In August 2025, the State Council issued the Opinions on Deepening the Implementation of the “AI Plus” Initiative, proposing the development of the “AI Plus” industry and providing fundamental guidance for the transformation and upgrading of traditional industries. Deeply analyzing the impact of AI Plus on traditional industries, identifying their shortcomings and deficiencies, and proposing corresponding countermeasures are crucial for advancing new industrialization and accelerating the development of a manufacturing power.

　　Remarkable Achievements of “AI Plus” in Driving the Transformation and Upgrading of Traditional Industries

　　Recently, fueled by government policies, technological innovations from businesses, and market demand, “AI Plus” has steadily been woven into various facets of traditional industries, driving their shift from being factor-driven to intelligence-driven.

　　Address the development bottlenecks of traditional industries, and facilitate improvements in both production efficiency and quality. The integration of AI with traditional industries has driven the automation and intelligence of production processes, enhanced production efficiency and product quality, and lowered enterprises’ production costs. In production, the integration of industrial robots with AI-driven visual inspection technology has enabled the shift from manual inspections to intelligent monitoring. According to the World Robotics Report 2024, China accounted for 51% of global industrial robot installations in 2023, with an application density of 470 units per 10,000 employees. In automotive manufacturing, industrial robots equipped with advanced visual recognition and precise control technologies efficiently complete complex tasks such as welding and assembly. In the home appliance sector, AI has helped reduce costs and boost efficiency. In the raw materials sector, AI enhances production processes and improves product quality through data aggregation and in-depth analysis. In the power sector, large models have improved fault detection rates while reducing false detections. In semiconductors, AI applications have shortened R&D cycles and reduced defect rates.

　　Expand market space and achieve the intelligent upgrade of products and services. “AI Plus” has driven the shift of traditional industries from functionality-focused to intelligence-driven models, sparking new demand and creating innovative application scenarios. In the consumer sector, smart homes now offer seamless device connectivity and scenario control. In 2024, China’s smartphone production grew by 8.2% year-on-year, with over 70% of models featuring AI chips, making smart photography and voice assistants standard features. In the production sector, the adoption of smart equipment is steadily increasing. CNC machines, powered by intelligent algorithms, are optimizing processing paths, while automotive parts manufacturers are integrating embodied intelligent robots to enhance precision assembly efficiency. Meanwhile, driven by innovations in service models, traditional industries are shifting from selling only products to offering both products and services. For instance, in the heavy machinery sector, companies not only sell tunnel boring machines but also provide remote operation and maintenance services.

　　Reshape the industrial ecosystem and strengthen collaboration across the industry chain. The integration of AI with traditional industries has broken down information barriers within industry chains, promoting collaboration across upstream and downstream segments and optimizing resource allocation. This has driven the transformation of the industry chain from a “linear connection” to a “networked collaboration.” In supply chain management, AI-driven demand forecasting models improve the accuracy of market predictions and enhance rational production planning and inventory management capabilities. Leveraging big data and other technologies, industrial Internet platforms have laid a solid foundation for upstream and downstream data connectivity across the industry chain. At the cluster level, the regional “industrial brain” is being rapidly deployed. Additionally, AI technology has spurred cross-industry innovation. For example, the convergence of automotive and information technology has accelerated the wide adoption of intelligent driving technologies. Meanwhile, AI has also promoted the cross-border integration of traditional and emerging industries, speeding up the reconstruction of industrial ecosystems. For instance, the fusion of traditional agriculture with digital technologies has led to the rise of smart agriculture, unlocking new growth opportunities for the sector.

　　Three Obstacles Faced by “AI Plus” in Driving the Transformation and Upgrading of Traditional Industries

　　During the transformation and upgrading of traditional industries through “AI Plus,” there are still three major obstacles in both the scope and depth of its application.

　　First, there are obstacles between technological supply and industrial demand, primarily reflected in the following two aspects. 1. There is a gap in core foundational technologies. AI applications in China are primarily focused on scenario implementation, while basic research and high-end hardware still lag behind those of developed countries. 2. The adaptability of AI technologies to industrial scenarios remains low. While general-purpose large models have developed rapidly in recent years, the development of industry-specific large models has been relatively slow. It is challenging to integrate process knowledge and data characteristics from traditional sub-sectors into model training. Meanwhile, the high deployment cost of small models, combined with limited funds and technology, makes it difficult for small and medium-sized enterprises (SMEs) to develop lightweight models for specific processes. Furthermore, the stability and reliability of AI applications in industrial scenarios still require further validation, particularly in industries with high demands for production continuity and stability, such as aerospace and energy. System failures or erroneous decisions in these sectors could lead to accidents and substantial economic losses.

　　Second, there are obstacles between resource allocation and transformation needs, primarily reflected in the following three aspects. 1. There is an imbalance in the supply structure of computing power. While China ranks among the top globally in terms of overall computing power, it faces challenges such as poor alignment between supply and demand, limited application depth, and regional development disparities. 2. Data resources are fragmented. Data in traditional industries is dispersed across various devices and systems, with inconsistent standards and formats. The conflict between data security and sharing is prominent, and institutional support mechanisms for cross-enterprise data flow are somewhat inadequate. The dilemma of enterprises “being reluctant or unable to share data” remains unresolved. 3. There is an imbalance in the supply and demand for “AI Plus” talent. In terms of the talent training system of colleges and universities, the integration of AI with traditional disciplines remains insufficient, and there is still a shortage of talent with cross-disciplinary expertise and practical skills. Some employees in traditional industries lack sufficient familiarity with AI technologies, making it more difficult for them to adapt to the operation and maintenance of intelligent devices and management systems. As a result, large-scale and systematic training is required.

　　Finally, there are obstacles between policy support and corporate needs, primarily reflected in the following three aspects. 1. Policy coordination needs improvement. Currently, policies concerning “AI Plus” are fragmented across various departments, which, to some extent, leads to engagement with multiple departments, numerous procedures, and long cycles when “AI Plus” empowers traditional industries. 2. There is a lack of strong support for SMEs in traditional industries to advance “AI Plus.” Leading enterprises find it relatively easier to advance “AI Plus,” while SMEs face limited resources, such as funding, technology, and talent, in advancing it. They encounter numerous challenges in areas such as technology selection, system integration, and application development, which result in a slower pace of transformation and upgrading for SMEs in traditional industries through “AI Plus.” 3. The development of standards and governance frameworks is relatively slow. Technical standards and ethical guidelines for AI applications in traditional industries are still in the exploratory phase and remain incomplete, with algorithm transparency and interpretability still requiring further refinement.

　　Building a Collaborative Empowerment System

　　Promoting “AI Plus” to Drive the Transformation and Upgrading of Traditional Industries

　　To broaden the scope and deepen the application of “AI Plus” in the transformation and upgrading of traditional industries, it is crucial to follow a problem-oriented approach and a systematic way of thinking, and build a collaborative empowerment system across three dimensions: technological innovation, resource integration, and policy optimization.

　　First, strengthen technological innovation and enhance industry adaptation capabilities. This can be achieved through three key efforts: 1. Use AI Plus to drive key technological breakthroughs. Leverage the strengths of the new system for mobilizing resources nationwide to focus efforts on achieving breakthroughs in core technologies in key fields such as AI chips, industrial software, and sensors. Implement an open competition mechanism to select the best candidates, support universities, research institutes, and enterprises in jointly establishing innovation alliances, and advance the industrialization of high-end chips and industrial control software. 2. Accelerate the development of industry-specific large models. Build a collaborative development framework for general-purpose and industry-specific small models. Establish a special fund under the central government to encourage collaboration between leading enterprises and research institutions to develop dedicated large models for sub-sectors, aiming for full model coverage of key fields in traditional industries as soon as possible. Promote the AI as a Service (AIaaS) model to lower the barriers for SMEs to adopt AI technologies. 3. Promote the organic integration of technology and application scenarios. Launch a scenario innovation initiative for integrating AI with traditional industries, select representative application scenarios, and provide them with financial and technical support. Establish a collaborative mechanism where “enterprises present their needs, universities provide solutions, and the government builds the platform.”

　　Second, optimize resource allocation and strengthen support capabilities. This can be achieved through three key efforts: 1. Vigorously promote the development of the computing power industry. Effectively optimize the allocation of computing power resources between the east and west, and enhance the ability to transform computing power into productive capacity for the transformation and upgrading of traditional industries. Focus on developing green computing power and accelerating breakthroughs in core technologies within the computing power sector. 2. Unlock the value of data elements. Clarify the ownership rights of data resources, the rights to process and use data, and the rights to operate data products. Introduce classification and grading standards for data in traditional industries. Establish national-level data centers for traditional industries in key fields, and make public data resources, such as equipment parameters and process standards, readily accessible. Encourage enterprises to engage in data transactions for traditional industries through data exchanges. 3. Strengthen the development of interdisciplinary talent. Universities should optimize the interdisciplinary curriculum for the integration of AI and traditional industries, incorporating AI course packages into relevant majors to enhance both the quantity and quality of interdisciplinary talent development. Launch digital skills enhancement initiatives to ensure full coverage of digital skills for workers in enterprises above the designated size in traditional industries. By fostering policy innovation, attract high-end global talent in the integration of AI with manufacturing to innovate and start businesses in China, accelerating the transformation and upgrading of traditional industries.

　　Third, improve the policy system and optimize the implementation environment. This can be achieved through three key efforts: 1. Strengthen the synergy of policies. By combining fiscal, technological, and industrial policies, support the AI-driven development of traditional industries and shorten the approval cycle for projects integrating AI and traditional industries. 2. Increase support for SMEs. Establish a dedicated fund for “AI Plus” transformation to support SMEs in equipment upgrades and model deployment. Foster a tripartite collaboration model among the government, enterprises, and banks to provide low-interest loans and interest subsidies for SMEs advancing “AI Plus.” Establish regional “AI Plus” service platforms to offer free technical consultations as well as test and verification services. 3. Improve the standards and governance framework. Accelerate the development of technical standards for AI applications in traditional industries and establish national standards for intelligent equipment, data security, and other areas. Establish a system for the registration and auditing of AI algorithms, and review the transparency of algorithms in AI systems involved in safety-critical operations. Explore the “ethical sandbox” mechanism and conduct AI ethics pilots in high-risk industries such as automotive and chemicals, balancing the relationship between “AI Plus” innovation and safety.

　　Transforming and upgrading traditional industries is key to advancing new industrialization, and “AI Plus” is a crucial driving force in achieving this goal. China is now at a critical juncture of technological innovation, scenario expansion, and ecosystem development. It is crucial to fully implement the “AI Plus” initiative, tackling technological, resource, and policy bottlenecks and promoting the high-end, intelligent, and green transformation of traditional industries. This will provide foundational support for the strategy of manufacturing power and Chinese modernization.