In recent years, with the rapid development of artificial intelligence technologies, China has been accelerating the implementation of its AI strategic framework. On February 10, 2026, the State-owned Assets Supervision and Administration Commission of the State Council (SASAC) convened a meeting to further deploy the “AI+” special initiative among central state-owned enterprises.
The meeting emphasized that central enterprises should strengthen investment-driven development, actively expand effective investment in computing power, promote the coordinated development of computing power and electricity, improve full-chain data governance capabilities, and continuously consolidate the foundational infrastructure of the artificial intelligence industry.
The meeting stressed that central enterprises must firmly grasp the development trends of artificial intelligence technology and industry. Taking the formulation and implementation of the 15th Five-Year Plan as an opportunity, they should identify their positioning and advantages in the field of artificial intelligence, establish scientific and effective industrial cooperation and management mechanisms, and further play a strategic supporting and demonstration role in national development. Central enterprises are expected to become key providers of intelligent computing infrastructure, important drivers of AI applications across industries, and organizers of systematic industrial deployment, thereby better serving the overall national development agenda.
The meeting also pointed out that central enterprises must strengthen their sense of responsibility and urgency in developing the artificial intelligence industry. They should actively adapt to the global wave of technological and industrial transformation, seize new opportunities in AI development, and enhance independent innovation in key core technologies, particularly breakthroughs in large-model technologies. Efforts should be made to transform more research outcomes from experimental prototypes into marketable products and industrial applications.
At the same time, enterprises are encouraged to cultivate application scenarios by deeply integrating artificial intelligence with their core businesses and industrial needs, exploring high-compatibility, high-value, and highly reliable application areas in order to promote large-scale deployment of AI technologies.
In fact, SASAC has been actively promoting the “AI+” initiative among central enterprises in recent years. These enterprises have focused on key sectors such as energy, manufacturing, and telecommunications, collaborating with leading companies to create more than 1,000 application scenarios.
For example, China FAW, Dongfeng Motor Corporation, and China Changan Automobile have introduced intelligent robots into automobile manufacturing processes, enabling robots to operate directly on factory production lines and increasing assembly efficiency by approximately 30 percent. In terms of data resource development, central enterprises have led the construction of 11 industry-level trusted data spaces and established four major data industry consortia covering transportation, energy, green development, and finance. In July 2025, SASAC officially launched the AI open-source platform “AI Huanxin” for central state-owned enterprises. Since its launch, the platform’s user base has increased tenfold, providing free public access to 2,200 domestic intelligent computing chips, more than 4,700 models, and over 1,200 datasets, thereby significantly supporting the growth of the AI industry ecosystem.
Artificial intelligence technologies are also being increasingly integrated into the actual production processes of central enterprises. China National Petroleum Corporation, for instance, has developed the Kunlun Large Model which is the first industry-level large model in China’s energy and chemical sector to receive national approval. Its parameter scale has evolved from 33 billion to 70 billion and eventually to 300 billion parameters, and it now supports more than one hundred industrial application scenarios. One example is the seismic forward and inverse modeling large model, which has improved the efficiency of solving seismic wave equations by ten times and shortened exploration project cycles by more than 20 percent, significantly enhancing oil and gas exploration efficiency.
As AI applications continue to deepen, China has also accelerated its top-level policy planning. In August 2025, the State Council proposed six key areas of action including technology, industry, consumption, public services, governance, and international cooperation. According to the plan, by 2027 the penetration rate of intelligent terminals and intelligent agents is expected to exceed 70 percent, and by 2035 China aims to fully enter an intelligent society. Experts believe that central enterprises possess large-scale application scenarios in key sectors such as energy, transportation, telecommunications, and finance, making them important testing grounds and incubators for AI technology deployment. As such, they play a core role in implementing national strategies, driving technological innovation, and building industrial ecosystems.
In practical implementation, several central enterprises have already established representative application cases. For example, China Mobile has developed an AI-enabled public cloud full-process threat response system for billion-scale cloud threats. By integrating capabilities from foundational models such as Jiutian and DeepSeek, it has created a security cloud-brain intelligent operations platform capable of managing thousands of security devices across the entire network and processing more than seven billion pieces of security data daily. As a result, the average handling time for security incident tickets has been reduced by 82.5 percent, the automated processing rate for security alerts has reached 99 percent, and the false alarm rate has been reduced to 0.2 percent.
State Grid has also applied artificial intelligence to the inspection and maintenance of power transmission and transformation equipment through the use of drones, intelligent substation inspection systems, and robotic power operation technologies, improving the efficiency of fault analysis and handling by approximately 50 percent.
China CRRC has focused on “AI + equipment manufacturing,” building 13 core application scenarios across three major areas: research and design, production and manufacturing, and operation and maintenance services. In the aerodynamic drag simulation of high-speed trains, for example, an intelligent simulation large model has reduced computational time from 24 hours to around 10 seconds, greatly improving research and development efficiency.
Despite these significant achievements, the large-scale deployment of artificial intelligence in central enterprises still faces multiple challenges. First, there are still difficulties in integrating AI technologies with complex industrial scenarios. Many enterprises report that general-purpose large models still need improvement in supporting vertical industry models. At present, companies mainly rely on small models or distilled lightweight large models.
While these models have relatively lower deployment costs, they still suffer from limited generalization capability, weaker interpretability, and the persistence of hallucination problems, which make it difficult to fully meet the requirements of production-level applications. At the same time, general solutions provided by technology companies often fail to adapt to specific industry needs, while industry experts within enterprises may struggle to translate vague business pain points into precise technical requirements, creating a gap between technological supply and industrial demand.
Secondly, the high cost of AI deployment remains another constraint. In traditional manufacturing sectors in particular, a large number of legacy machines were not originally designed for data collection and often lack sensor interfaces or rely on closed communication protocols. Retrofitting such equipment with sensors and gateways for digital transformation involves high costs, long implementation cycles, and significant technical challenges. Moreover, industrial AI applications typically require equipment upgrades, production line transformations, large-scale data collection and processing, and the integration of industry knowledge, which increases investment costs and raises concerns among enterprises regarding the return on investment.
In addition, the insufficient supply of high-quality data has also limited the further development of AI applications. Many central enterprises still face challenges in data governance, including inconsistent data standards, incomplete sharing mechanisms, and unresolved data security concerns. These issues lead to a shortage of high-quality datasets, limited cross-industry data circulation, and underutilization of the value of data as a production factor. At the same time, the shortage of interdisciplinary talent has become increasingly prominent. Many enterprises lack professionals who possess both deep industry knowledge and expertise in artificial intelligence. According to research estimates, by 2025 the demand for AI talent in Beijing alone will reach about 540,000, with a shortage of approximately 210,000 interdisciplinary professionals.
In response to these challenges, central enterprises plan to develop long-term strategies centered on the “AI+” initiative in order to better transform artificial intelligence technologies into real productive forces. They will strengthen national-level platform development and resource coordination, support central enterprises in leading the establishment of AI innovation platforms and high-quality datasets, and promote collaboration among industry, academia, and research institutions to jointly tackle key technological challenges.
Efforts will also be made to accelerate market-oriented reforms of data elements by unifying data standards, improving data quality, strengthening security supervision, and promoting data sharing and circulation. Finally, talent development and incentive mechanisms will be improved through cooperation between central enterprises and universities to cultivate interdisciplinary AI professionals, while establishing evaluation and incentive systems for compensation and technology commercialization that better align with the characteristics of the AI industry, thereby laying a solid foundation for the long-term development of China’s artificial intelligence sector.
Source: news cn, SCMP, China Daily, scio gov cn, xinhua, bijiannet
