The Zhejiang Provincial Department of Economy and Information Technology has recently released the "Implementation Plan for AI-Empowered Digital Transformation of the Manufacturing Industry in Zhejiang Province (2026-2030)." The plan clearly outlines that Zhejiang will focus on the digital and intelligent transformation of manufacturing, targeting business process reengineering and key capability leaps as its primary directions to accelerate the deep integration of artificial intelligence technology.
The plan sets forth five key tasks. First, it aims to solidify the foundational support for digital and intelligent transformation by promoting data governance and the construction of high-quality datasets. This involves driving collaborative data collection, cleaning, labeling, testing, and utilization across industrial chains in key sectors such as integrated circuits, apparel and textiles, consumer electronics, and modern home furnishings. The plan also seeks to advance pilot programs for product master data standards and industry-specific high-quality datasets, while accelerating the creation of trusted industrial data spaces. Additionally, it focuses on building a strong foundation for computing power and model infrastructure by supporting the development of high-performance algorithm models tailored to manufacturing needs, nurturing vertical industry models, and developing a "cloud-edge-device" model system. This will enhance Model-as-a-Service (MaaS) capabilities and promote the application of general-purpose and specialized models. The plan also calls for establishing a safety line of defense for AI applications by strengthening the security protection of industrial model algorithms, improving AI transparency and explainability through knowledge base optimization, training corpus correction, and content generation labeling, and reducing the risk of hallucinations to enhance risk prevention in AI applications.
The second key task is to drive intelligent upgrades across critical business processes. In the area of R&D and design, the focus will be on creating new intelligent R&D models through product digital design, product virtual verification, and process digital design. In production and manufacturing, the plan emphasizes the application of smart manufacturing equipment like industrial robots and intelligent devices, focusing on production scheduling, quality control, and safety production. This includes deepening the integration of AI with manufacturing systems to achieve dynamic optimization of production schedules, adaptive adjustment of key process parameters, and multi-equipment collaborative operations. In marketing and services, the plan targets precision marketing, remote maintenance, and customer service by building a full-cycle intelligent service system, using AI algorithms for precise user profiling and demand forecasting, and matching customer needs through personalized recommendations and intelligent pricing. For operations management, the focus is on utilizing AI for business decision-making, supply chain collaboration, and green energy efficiency to achieve full-process integrated operations management based on large models.
The third task involves deepening the tiered application of AI among leading enterprises. Leading enterprises are encouraged to create benchmark models by building industrial intelligent entity platforms. The plan promotes the large-scale deployment of collaborative and inspection robots in production operations and inspection maintenance. It also explores multi-agent task scheduling and group collaboration, aiming to create "AI+ Future Factory" benchmarks that coordinate "intelligent models + digital twins + industrial intelligent entities," and to cultivate new models for AI-native factories. The plan also encourages "Eagle" and "Chain-Lord" enterprises to take the lead in forming innovation consortia. These consortia will focus on core R&D, production, supply, sales, and service links, collaborating with upstream and downstream partners for joint breakthroughs and scenario validation, thereby creating a set of replicable "full-chain AI application" solutions. Leading enterprises are also guided to open up application scenarios and data resources to small and medium-sized enterprises (SMEs), collaborating with platform companies and AI application service providers. For SMEs, the plan promotes the widespread adoption of AI. Digital Level 2.0 enterprises are encouraged to deploy key business systems and carry out cross-system integration, introducing industrial intelligent entities and lightweight AI tools as needed. Digital Level 3.0 and above enterprises are supported to innovate high-value integrated applications in complex scenarios like product digital twins and integrated design and manufacturing, developing and deploying intelligent entities to create a batch of replicable, "small, fast, light, and accurate" AI products. Digital service providers are also urged to explore lightweight service models such as "try-before-you-buy," "subscription services," and "pay-for-performance" to lower the barrier to AI application.
The fourth task is to explore the quality improvement and upgrade of key industrial clusters. This includes empowering the application of the next-generation information technology industry by deeply integrating AI with sectors like integrated circuits, intelligent IoT, and high-end software. It also involves driving the intelligent upgrade of the high-end equipment industry, focusing on areas like new energy vehicles and parts, and high-end ships and marine equipment. The plan aims to advance the high-end development of the modern consumer and health industry, targeting biopharmaceuticals and medical devices, modern textiles and apparel, and modern home furnishings and smart appliances, using AI to meet diverse consumer needs. Finally, it seeks to promote the green and intelligent integration of the green petrochemical and new materials industry, accelerating the green and low-carbon transition and the R&D of new materials through AI.
The fifth task is to build a favorable ecosystem for AI empowerment. This involves strengthening core technology breakthroughs and standard development by encouraging industry-academia-research collaboration, focusing on joint breakthroughs in core technologies like industrial base model libraries to enhance the industrial base. The plan also aims to cultivate a batch of typical products and solutions targeting common difficulties in AI application in manufacturing and to accelerate the development of standards and specifications for AI application in the manufacturing sector. Furthermore, it seeks to strengthen public platform empowerment and the promotion of results by accelerating the development of industrial intelligent entity platforms, promoting the intelligent upgrade of manufacturing digital transformation promotion centers and industrial internet platforms, and expanding "platform + intelligent entity" integrated services. Leveraging events like the World Internet Conference Wuzhen Summit, the plan will promote AI application demonstration enterprises and solutions. It also aims to explore the export of results through mechanisms like the "Belt and Road" initiative and BRICS cooperation, creating overseas smart factories. Finally, the plan emphasizes cultivating more composite talent by deepening school-enterprise cooperation, developing AI application courses and teaching materials with regional characteristics, and promoting the construction of industry-specific AI R&D centers and training bases.
The plan includes an appendix listing 62 provincial-level key industrial clusters and their typical "AI + Manufacturing" application scenarios.
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