Introduction

Manufacturing is the foundation of a nation and the basis for a strong country. As a strategic technology leading a new round of technological revolution and industrial transformation, artificial intelligence (AI) is evolving from a technical tool into a crucial engine for driving quality, efficiency, and dynamism changes in the manufacturing industry. Leveraging AI in the transformation and upgrading of manufacturing is essential for advancing high-quality development from “Made in China” to “Intelligent Manufacturing in China.”

Historical Context of Manufacturing

Manufacturing is the main battlefield for deep integration of technological and industrial innovation and serves as the primary carrier for producing key equipment and applying new technologies. Globally, three industrial revolutions have driven the transformation and upgrading of manufacturing. The first industrial revolution led to the rise of machine manufacturing represented by steam engines and textile machinery, the second revolution spurred the prosperity of modern communications, steel, oil, and automotive industries, and the third revolution birthed industries like computers, the internet, and integrated circuits. Currently, the power of AI-driven technological revolution and industrial transformation is expected to be on par with previous industrial revolutions, enabling comprehensive empowerment of high-quality development in manufacturing. AI exhibits a strong “leading goose” effect, widely applicable to industrial development, converting technological variables into industrial increments.

Current State of AI in Manufacturing

China’s industrial system is complete, with significant comparative advantages in product manufacturing. The deep integration of AI technology in the industrial sector has effectively birthed a number of emerging high-end manufacturing industries. By 2025, the number of AI companies in China is expected to exceed 6,200, with the core industry scale surpassing 1.2 trillion yuan. Chinese companies have launched over 300 humanoid robots, accounting for more than half of the global total. Additionally, new generation smart terminals like AI smartphones, computers, and intelligent manufacturing equipment are rapidly entering the global market. By 2025, smart watches and smart toys from China are projected to be sold in over 170 countries and regions. Furthermore, AI, as a key enabling technology, significantly promotes the development of emerging manufacturing industries such as customized production, 3D printing, and biological manufacturing, reshaping the industrial form and development landscape of manufacturing.

Impact of AI on Traditional Manufacturing

AI profoundly impacts traditional manufacturing through technology diffusion and industrial chain extension. Industries with high correlation, strong synergy, and complete industrial chain support are the first to undergo transformation and upgrading, even forming new paths for industrial development. Representative examples include the autonomous vehicle and drone industries. The traditional automotive industry has long relied on mechanical systems like engines and gearboxes; however, with AI technology empowerment, the focus of the autonomous vehicle industry has shifted from engines to intelligent control systems, providing a “curve overtaking” opportunity for the development of China’s automotive industry. Similarly, the drone industry has rapidly developed various small drones for logistics, performances, and low-altitude operations, forming a multi-format integrated low-altitude economic development pattern. In the first two months of this year, the value added of smart vehicle equipment manufacturing and smart unmanned aerial vehicle manufacturing in China increased by 46.3% and 26.6%, respectively. On the other hand, AI deeply empowers areas like food processing, home appliances, and equipment manufacturing, continuously demonstrating its cost-reduction and quality-improvement effects throughout the entire chain of research and development, production, and management. By 2025, the application rate of AI technology in large-scale manufacturing enterprises in China is expected to exceed 30%. As the digital transformation of manufacturing progresses steadily, China has established over 35,000 basic-level, more than 8,200 advanced-level, over 500 excellent-level, and 15 leading-level intelligent factories.

Differences Between Digitalization and Intelligence

Compared to digitalization, intelligence can be more deeply embedded in manufacturing. The application of digital technology focuses on promoting informatization and platformization in transaction or circulation links, but it is challenging to apply in manufacturing processes such as data collection, production equipment command, and production process control. AI technology can achieve precise transformation and upgrading in critical manufacturing links like production processes, equipment scheduling, and production auxiliary systems. For instance, AI technology increasingly excels in intelligent manufacturing and customized production, enhancing resource allocation efficiency in areas like new material development, supply chain management, and inventory management.

Challenges and Opportunities

China has made significant progress in empowering manufacturing with AI, but it also faces several bottlenecks. One major advantage of developing AI in China is the abundance of application scenarios; however, there are constraints in the industrial ecosystem that limit AI’s empowerment in manufacturing. Restrictions related to core technologies, raw materials, components, and high-quality training data make it difficult for some manufacturing scenarios to be implemented. Empowering manufacturing development with AI requires a foundation of intelligent devices and facilities, mapping and simulating the real world through the Internet of Everything. However, the construction of intelligent devices and facilities in China lags behind, with insufficient support from infrastructure and equipment for the intelligent development of manufacturing. Existing general algorithms and computing architectures struggle to meet the growing demands of specialized scenarios and high-level computing requirements, limiting AI’s deep empowerment of manufacturing. In the future, promoting the transformation and upgrading of manufacturing through AI empowerment can focus on the following aspects.

Building an Integrated Ecosystem

Establish an industrial ecosystem that deeply integrates AI with the real economy. Scalable and clustered ecosystems are the foundation for continuously promoting deep integration of AI with the real economy. Further leverage the “leading goose” effect to tackle key technological shortcomings and strengthen efficient supply of computing power, algorithms, and data. Accelerate breakthroughs in key areas, advancing the development of industries with mature AI technologies, high industrial relevance, strong synergy, and substantial existing data accumulation, such as industrial robots, autonomous vehicles, and drone industries. Additionally, encourage localities to develop AI industries suited to their conditions, continuously promoting industrial upgrades, inter-regional industrial transfers, and cross-regional industrial chain collaboration.

Intelligent Upgrades in Manufacturing Equipment

Focus on key links such as research and design, production, quality inspection, and operation and maintenance services to accelerate the intelligent upgrade of production equipment, production lines, workshops, and factories. Promote the application of technologies and equipment like intelligent robots, smart sensors, digital twins, and flexible manufacturing, driving traditional production lines toward automation, intelligence, and lean transformation, thereby comprehensively enhancing production efficiency, product quality, and green safety levels. Encourage enterprises to prioritize the intelligent transformation of high-energy-consuming and outdated “dumb equipment,” achieving real-time data collection and interconnectivity in key processes, introducing automated control systems, and promoting the transition from single-step automation to full-process intelligence.

Strengthening Safety Measures

Reinforce key core technology breakthroughs in industrial software, smart sensors, etc., to build a self-controllable industrial safety barrier. Establish a sound risk prevention and control system for AI safety, reasonably and prudently regulate AI software, computing facilities, and data resources, and encourage manufacturing enterprises to conduct data security and algorithm model safety management certification. Explore deploying “safety barriers” between AI models and industrial control systems, conducting third-party safety assessments on algorithms applied to key equipment and facilities to effectively prevent production safety accidents caused by “AI hallucinations.” Extend network security governance from office management to industrial production, implementing all-weather risk monitoring for network physical systems at production sites. Adhere to the principles of technology for good and collaborative governance, and improve the governance rules and policy system for AI that adapts to the intelligent upgrade of manufacturing, ensuring a safe and controllable governance ecosystem to support the deep empowerment of high-quality development in manufacturing.