China Push forward with AI Plus to help traditional industries upgrade

Against the backdrop of the accelerating evolution of a new round of scientific and technological revolution and industrial transformation, promoting the deep integration of AI with traditional industries has become a new driving force for the transformation and upgrading of traditional industries. In August 2025, the State Council issued the "Opinions on Deepening the Implementation of the 'Artificial Intelligence Plus' Action Plan," proposing the development of the "AI Plus" industry, providing a fundamental guideline for the transformation and upgrading of traditional industries. In-depth analysis of the effectiveness of "AI Plus" in empowering traditional industries, identifying shortcomings and deficiencies, and proposing corresponding countermeasures are of great significance for promoting new industrialization and accelerating the development of a manufacturing powerhouse.

"AI Plus" has achieved remarkable results in promoting the transformation and upgrading of traditional industries.

In recent years, driven by relevant government policies, technological innovation by enterprises, and market demand, "AI Plus" has been gradually integrated into all aspects of traditional industries, promoting their transformation from factor-driven to intelligent-driven approaches.

It addresses bottlenecks in the development of traditional industries and promotes improvements in production efficiency and quality. The integration of AI with traditional industries has promoted the intelligentization and automation of production processes, improved production efficiency and product quality, and reduced production costs for enterprises. In the production process, industrial robots combined with AI visual inspection technology have achieved a transition from manual inspection to intelligent monitoring. According to the "World Robotics Report 2024," my country's industrial robot installations accounted for 51% of the global total in 2023, with an application density of 470 units per 10,000 employees. In the automotive manufacturing sector, industrial robots utilize advanced visual recognition and precision control technologies to accurately and efficiently complete complex tasks such as welding and assembly. In the home appliance sector, the application of artificial intelligence has reduced costs and increased efficiency. In the raw materials sector, artificial intelligence optimizes production processes and improves product quality through data aggregation and in-depth analysis. In the power sector, large models increase fault detection rates and reduce false positives. In the semiconductor sector, the application of artificial intelligence shortens R&D cycles and reduces defect rates.

Expanding market space and achieving intelligent upgrades for products and services. "AI+" is driving the transformation of traditional industries from functional to intelligent, stimulating new demands and generating new application scenarios. In the consumer sector, smart homes enable device connectivity and scene-based control. In 2024, my country's smartphone production will increase by 8.2% year-on-year, with over 70% of models equipped with AI chips, and features such as smart cameras and voice assistants becoming standard features. In the production sector, the penetration rate of intelligent equipment continues to grow. CNC machine tools use intelligent algorithms to optimize machining paths. Automotive parts manufacturers are introducing embodied intelligent robots, improving the efficiency of precision assembly. Meanwhile, with the innovation of service models, traditional industries are shifting from single-product sales to a "product + service" model. For example, in the large machinery sector, in addition to selling tunnel boring machines, they also provide remote operation and maintenance services.

Reshaping the industrial ecosystem and enhancing the collaborative capabilities of the industrial chain. The integration of artificial intelligence with traditional industries eliminates information barriers within the traditional industrial chain, promotes collaboration between upstream and downstream chains, and optimizes resource allocation, driving the transformation of the industrial chain from "linear serialization" to "networked collaboration." In supply chain management, the application of AI-driven demand forecasting models helps companies improve the accuracy of market demand forecasts and their ability to rationally plan production and inventory management. Industrial Internet platforms, leveraging technologies such as big data, provide a critical foundation for data sharing across the industrial chain. At the industrial cluster level, regional "industry brains" are being implemented at an accelerated pace. Furthermore, AI technology is promoting cross-industry innovation. For example, the integration of automotive and information technology has popularized intelligent driving technology. Furthermore, AI is driving cross-sector integration between traditional and emerging industries, accelerating the reconstruction of the industrial ecosystem. For example, the integration of traditional agriculture and digital technology has fostered smart agriculture, expanding agricultural development potential.

"AI+" Facing Three Bottlenecks in Transforming and Upgrading Traditional Industries

While "AI+" is helping to transform and upgrade traditional industries, the breadth and depth of its application remain three bottlenecks.

First, there is a bottleneck between technology supply and industry demand, primarily manifested in two areas: First, there are shortcomings in underlying core technologies. AI applications in my country are primarily focused on practical scenarios, and there is still a gap between basic research and high-end hardware compared to developed countries. Second, AI technology is poorly adapted to industrial scenarios. In recent years, while general-purpose large-scale models have developed rapidly, the development of large-scale industry models has lagged behind. This makes it difficult to incorporate process knowledge and data features from specific traditional industry sectors into model training. Furthermore, the high deployment cost of small models makes it difficult for small and medium-sized enterprises, facing funding and technical constraints, to develop lightweight models for specific processes. Furthermore, the stability and reliability of AI applications in industrial scenarios require further verification, especially in industries such as aerospace and energy and power, where production continuity and stability are paramount. System failures or erroneous decisions could lead to accidents and economic losses.

Secondly, there are bottlenecks between resource allocation and transformation needs, manifesting themselves in three key areas: First, there's an imbalance in the supply and structure of computing power. While my country's overall computing power ranks among the highest globally, it faces shortcomings such as poor supply-demand integration, insufficient application depth, and uneven regional development. Second, there's fragmented data resources. Traditional industry data is scattered across devices and systems, with inconsistent standards and formats. This creates a significant conflict between data security and sharing, and institutional safeguards for cross-enterprise data flow are somewhat inadequate. The dilemma of companies being reluctant or hesitant to share remains unresolved. Third, there's an imbalance between the supply and demand of "AI+" talent. From the perspective of university talent training systems, the cross-integration of AI and traditional disciplines is still insufficient, and there remains a shortage of talent with cross-disciplinary expertise and practical skills. Some employees in traditional industries lack familiarity with AI technology, making it difficult to adapt to the operation and maintenance of intelligent equipment and management systems. Large-scale, systematic training is needed.

Finally, there are bottlenecks between policy support and business demands, manifesting themselves in three key areas: First, policy coordination needs to be improved. Currently, policies related to "AI+" are scattered across various departments. This, to a certain extent, results in multiple departments, complex processes, and long implementation cycles when "AI+" empowers traditional industries. Secondly, support for small and medium-sized enterprises in traditional industries to promote "AI+" is somewhat insufficient. While leading enterprises have a relatively easy time promoting "AI+", small and medium-sized enterprises have limited access to the necessary funds, technology, and talent. They face numerous difficulties in technology selection, system integration, and application development, resulting in a relatively slow adoption of "AI+" to support the transformation and upgrading of small and medium-sized traditional enterprises. Thirdly, the development of standards and governance systems is relatively lagging. Technical standards and ethical norms for AI applications in traditional industries are still in the exploratory stage and are not yet fully developed. The transparency and explainability of algorithms are also somewhat lacking.

Building a Collaborative Empowerment System to Promote "AI+" to Empower the Transformation and Upgrading of Traditional Industries

To expand the breadth and depth of "AI+" application in the transformation and upgrading of traditional industries, it is necessary to adhere to a problem-oriented and systematic approach and build a collaborative empowerment system across three dimensions: technological innovation, resource integration, and policy optimization.

First, strengthen technological innovation and enhance industry adaptability. Second, empower breakthroughs in key core technologies through "AI+". Leveraging the advantages of the new national system, we will focus our efforts on mastering core technologies in key areas such as AI chips, industrial software, and sensors. We will precisely implement the "unveiling the challenge and appointing the leader" mechanism, supporting universities, research institutes, and enterprises in jointly forming innovation alliances to elevate the industrialization of high-end chips and industrial control software. Second, we will accelerate the development of large-scale industry models. We will establish a collaborative development framework for general-purpose and small-scale industry models, establish a special central government fund, and encourage leading enterprises and research institutions to collaborate on developing specialized large-scale models for specific industries, aiming to rapidly achieve full model coverage of key areas in traditional industries. We will promote the AIaaS (Artificial Intelligence as a Service) model to lower the barrier to AI adoption for small and medium-sized enterprises. Third, we will promote the organic integration of technology and application scenarios. We will launch an "AI + Traditional Industries" scenario innovation initiative, select representative application scenarios, and provide financial and technical support. We will establish a collaborative working mechanism where "enterprises raise demands, universities provide solutions, and the government builds platforms."

Second, we will optimize resource allocation and enhance support and guarantee capabilities. First, we will vigorously promote the development of the computing power industry. We will effectively optimize the allocation of computing power resources between eastern and western regions, improve the ability to transform computing power resources into productive forces for the transformation and upgrading of traditional industries, vigorously develop green computing power, and accelerate breakthroughs in key core technologies in the computing power sector. Second, we will activate the value of data. We will clarify the rights to hold data resources, process and use data, and operate data products, and introduce standards for the classification and grading of data in traditional industries. We will build national-level traditional industry data centers in key areas, opening up public data resources such as equipment parameters and process standards. We will encourage enterprises to trade traditional industry data through data exchanges. Third, we will strengthen the cultivation of interdisciplinary talents. Universities should optimize the interdisciplinary disciplines of "AI + Traditional Industries" and add AI course packages to relevant majors to increase the quantity and quality of interdisciplinary talents. We will launch digital skills development initiatives to ensure that workers in traditional industries (large-scale enterprises) are fully covered in digital skills. Through policy innovation, we will attract high-end talent in the "AI + Manufacturing" field from around the world to innovate and start businesses in China, thereby promoting the transformation and upgrading of traditional industries.

Third, we will improve the policy framework and optimize the implementation environment. First, we will strengthen policy synergy. Through a combination of fiscal and taxation policies, technology policies, and industrial policies, we will help AI empower the development of traditional industries and shorten the approval cycle for "AI + Traditional Industries" projects. Second, we will strengthen support for small and medium-sized enterprises. We will establish a special fund for "AI +" transformation to assist small and medium-sized enterprises in equipment upgrades and model deployment. Promote a tripartite cooperation model among government, enterprises, and banks, providing low-interest loans and interest subsidies to small and medium-sized enterprises implementing "AI+." Build a regional "AI+" service platform, offering free technical consultation and testing and verification services. Third, improve standards and governance systems. 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 filing and audit system for AI algorithms, reviewing the transparency of AI algorithms related to production safety. Explore "ethical sandbox" mechanisms, conduct AI ethics pilots in high-risk industries such as automotive and chemical industries, and strike a balance between innovation and safety in "AI+."

The transformation and upgrading of traditional industries is key to advancing new industrialization, and "AI+" is a crucial driving force for achieving these goals. my country is at a critical juncture of technological innovation, application expansion, and ecosystem development. We must thoroughly implement "AI+" initiatives, address technical, resource, and policy bottlenecks, and promote the high-end, intelligent, and green development of traditional industries, providing fundamental support for the strategy of building a manufacturing powerhouse and China's modernization drive.