Driving the Synergy of AI Industrialization and Intelligent Transformation

Currently, the development of artificial intelligence (AI) is transitioning from technological breakthroughs to industrial shaping. China’s AI industry has a strong technical supply capability, but large-scale application still faces the “last mile” problem.

The 2026 Government Work Report proposed to “create a new form of intelligent economy,” indicating that AI development is moving from technological breakthroughs, product iterations, and scenario expansions to industrial shaping and system restructuring. To develop AI, it is essential to promote the industrialization of technology, products, platforms, and terminals, while also pushing AI deeper into manufacturing and other real economies to drive industrial intelligence. This requires a connection between technical supply and application demand, promoting mutual traction and coordinated efforts between AI industrialization and intelligent transformation, allowing AI’s technical advantages to continuously transform into industrial and developmental advantages.

AI industrialization and intelligent transformation are two key dimensions for strengthening the intelligent industry system and shaping the development pattern of the intelligent economy. The former focuses on transforming technology, products, platforms, and terminals into industrial supply capabilities, while the latter emphasizes promoting AI into real scenarios such as research and development, production, supply chain collaboration, and brand marketing, converting it into actual productive forces.

Relying solely on the AI industry itself makes it difficult to truly strengthen the intelligent economy. Without broad application scenarios, AI industrialization can easily stagnate at the levels of technical self-rotation, product internal competition, and hype-driven phases, making it hard to solidify into long-term industrial capabilities. Similarly, relying solely on industrial intelligence is also challenging. Industrial intelligence is not simply about adding equipment, connecting systems, or making modifications; it requires stable technical supply, product supply, and service supply to continuously support the formation of replicable and scalable systematic upgrading capabilities.

Coordinated efforts do not merely parallel the supply side and application side; they form a bidirectional shaping and cyclical reinforcement operating mechanism in the real industrial process. The closer technical supply is to industrial demand, the more vitality AI industrialization has, and the more continuous iteration can be formed through scenario feedback, allowing industrial intelligence to deepen and solidify.

This synergy is first reflected in the bidirectional traction between technical supply and scenario demand. AI industrialization cannot be an insular process; technology must effectively enter real industrial development scenarios to accelerate iteration through problem discovery and value verification. Scenarios can only continuously enhance application levels and optimize or even reshape industrial processes based on higher quality algorithms, data, toolchains, and terminal supplies. The deeper the integration of technology and scenarios, the stronger the support between industrialization and intelligence.

Secondly, the synergy is also reflected in the mutual promotion of project transformation and industrial diffusion. Once technology enters scenarios, it can transition from technology validation to product refinement, from single-point trials to stable services, and from sporadic applications to industry solutions. Once projects possess sustainable transformation capabilities, they will solidify product capabilities, service capabilities, and market capabilities, which in turn promotes the large-scale development of the AI industry itself. Moreover, the deeper layer of synergy is manifested in the mutual shaping of entity growth and industrial ecology. The bidirectional connection between supply and application not only drives the growth of technical service entities, solution entities, and scenario transformation entities but also promotes traditional enterprises to complete capability upgrades, organizational restructuring, and business model updates.

To promote the synergy of both ends, high-quality supply must genuinely target high-value scenarios. AI industrialization should enhance supply capabilities around the real demands of industrial intelligence. High-end chips, basic software, industrial algorithms, multimodal models, high-quality datasets, as well as intelligent terminals and open-source ecosystems, ultimately need to be assessed on their ability to enter industrial sites, solve practical problems, and support large-scale applications. High-value scenarios should also drive high-quality supply during project advancement. Industrial intelligence must continuously raise new questions, form new demands, and test new technologies in real scenarios. It is essential to cultivate diverse entities to form a stable transformation chain, strengthening AI companies while also nurturing solution service providers, scenario transformation entities, and platform collaboration entities, while promoting traditional enterprises to enhance their capacity for intelligence.

To drive the synergy of AI industrialization and intelligent transformation, it is necessary to build a solid industrial foundation with robust technical supply and to use rich application scenarios to drive technical iteration, project transformation, and entity growth, accelerating the formation of a development pattern that promotes production through intelligence and nurtures intelligence through production. The more thoroughly the two ends are connected, the stronger the endogenous driving force for intelligent industry development will be, allowing AI to continuously transform into a new driving force for high-quality development.