The "16th Five-Year" Plan outlines the optimization and upgrading of traditional industries as the primary focus for building a modern industrial system, highlighting their foundational role and strategic importance. Currently, emerging and future industries account for approximately 20% of China's manufacturing sector, while traditional manufacturing makes up nearly 80%. In many industrial cities and resource-based regions, traditional industries remain the backbone of the local economy, serving as crucial pillars for stabilizing growth, employment, and public finances. Promoting high-quality economic development during the "16th Five-Year" period does not simply involve adding new industries but requires the coordinated development of traditional, emerging, and future sectors. By reshaping concepts, upgrading technology, advancing digital-intelligent transformation, restructuring value chains, and optimizing organizations, traditional industries can be revitalized with new momentum and vitality.
Digital transformation is a key pathway for traditional industries to enhance quality, upgrade, and rebuild competitiveness. It also serves as a vital foundation for deeply integrating with emerging and future industries and embedding into the new development paradigm. Previously, efforts to promote digital transformation in traditional industries focused largely on improving efficiency and reducing costs. However, with the rapid penetration of artificial intelligence into industrial sectors, corporate practices in R&D, production organization, management, and marketing are undergoing comprehensive reshaping. This year's government work report introduced for the first time the concept of "building new forms of smart economy," advocating for the commercial and large-scale application of AI in key industries. This signals a shift from past emphasis on "digital" upgrades to a new stage of "digital-intelligent" transformation characterized by AI integration, data-driven decision-making, and systemic restructuring. Entities that effectively combine data, algorithms, computing power, and application scenarios are more likely to gain a competitive edge. For local industrial development, this represents not only a technological upgrade but also a critical window of opportunity. It is essential to embrace digital-intelligent trends, embed AI throughout enterprise production and management processes, leverage data for decision-making, and restructure systems to propel traditional industries from localized efficiency gains to holistic performance leaps, thereby enhancing the overall competitiveness of the modern industrial system.
**I. Advancing Internal Digital Transformation to Enhance Endogenous Digital-Intelligent Capabilities**
Industrial digital transformation involves more than implementing new systems or altering processes; it encompasses both internal capability reconstruction and external resource integration. Internally, digitalization primarily entails rebuilding capability systems, typically progressing through three stages: management digitalization, production process digitalization, and R&D digitalization. Management digitalization addresses operational decision-making, process coordination, and cost control. Production process digitalization enhances the visibility, controllability, and leanness of manufacturing. R&D digitalization signifies the deep integration of data, algorithms, and models into the innovation system, shifting R&D activities from experience-driven to data-driven and model-driven approaches. This represents a higher stage of core capability digitalization and determines an enterprise's ability to continuously iterate technologies, innovate products, and respond to market demands. Past efforts to promote digital transformation in traditional industries primarily focused on management and production aspects, essentially making improvements within existing frameworks without fundamentally addressing the critical capability reconstruction needed for integration with emerging and future industries. Compared to traditional sectors, emerging and future industries are characterized by faster R&D iteration, more frequent product updates, higher process requirements, and stronger cross-disciplinary collaboration. To effectively integrate, traditional industries must push digital-intelligent transformation further into the R&D domain, embedding AI into product design, process simulation, testing, verification, and solution optimization. Local governments should refine their tools and methods for promoting enterprise digital transformation, shifting guidance emphasis from management and production digitalization to R&D digitalization and full-process intelligentization. This includes introducing specialized support measures and increasing the introduction of professional service providers, such as information consultants, to comprehensively enhance the endogenous digital-intelligent capabilities of traditional industries.
In recent years, the Dongguan government has undertaken numerous proactive and effective initiatives to deepen corporate digital transformation. In 2025, Dongguan introduced measures to accelerate AI empowerment for high-quality manufacturing development, explicitly outlining the compilation of an application guide for "AI + manufacturing" enterprises. Focusing on key industrial chains, the measures promote deep AI integration across R&D design, pilot testing, production manufacturing, maintenance services, and management. In 2026, Dongguan further proposed supporting enterprises in building open, shared digital design platforms, aiming to increase the adoption rate of digital R&D design tools to over 90%. This pushes internal digital-intelligent capabilities from operational aspects into the innovation sphere, fostering sustainable development capabilities oriented towards emerging and future industries.
**II. Optimizing External Platform Networks to Reshape Resource Allocation Methods for Traditional Industries**
While internal digitalization primarily addresses efficiency and capability issues, external digitalization, in the context of accelerating platformization and networking, increasingly determines an enterprise's ability to embed into higher-level industrial networks. In the traditional industrial economy, enterprises were often viewed as relatively independent production entities, with competition centered on cost, efficiency, and scale. However, with the development of the digital economy, many enterprises are transitioning from being links in an industrial chain to nodes within a network. Platforms, data, traffic, and user connections are becoming key factors influencing competitiveness. Unlike traditional economic models, the digital economy era often exhibits a power-law distribution of resources, traffic, and users, where a few critical nodes aggregate vast amounts of data, transactions, and users. An enterprise's value is determined not only by its production capacity and products but also by its position and connectivity within platform ecosystems, data networks, and user networks. Consequently, the nature of competition has shifted from individual capability rivalry to competition based on network position and connectivity.
When promoting digital-intelligent transformation for traditional industries, local governments should look beyond internal digital upgrades and consider how to embed local industries into the digital economy system. Efforts should focus on helping local industries access higher-level platform networks, data networks, user networks, and industrial ecosystems, securing more advantageous positions within the new resource allocation framework. Yiwu's transformation of its traditional small commodity trade exemplifies this approach, restructuring the entire external connection system around "market + platform + service + network." On one hand, Yiwu leverages government-promoted comprehensive international trade information service platforms and market procurement trade networking platforms to digitize and streamline services like customs clearance, logistics, settlement, tax refunds, filing, and cargo consolidation, lowering the barrier for merchants to enter global trade networks. On the other hand, through digital trade platforms like Chinagoods, Yiwu uses trade data integration as a core driver to connect previously dispersed resources—merchants, goods, buyers, logistics, warehousing, payment, and finance—within the physical market. This integrates the entire chain from production design and display transactions to logistics, warehousing, and financial services, building a comprehensive trade service ecosystem that helps numerous traditional enterprises embed into global digital networks and unlock new growth avenues for traditional trade. Supported by this system, Yiwu has seen simultaneous growth in foreign trade scale and participant activity in recent years. In 2025, its import-export volume surpassed 800 billion yuan for the first time, with exports growing 24.1% year-on-year, and the number of active foreign trade enterprises with actual import-export records increased significantly to 13,385.
**III. Unlocking the Value of Data Factors: Shifting from "Producing Data" to "Utilizing Data"**
Many regions have addressed data collection and process migration to the cloud in their push for traditional industry digital-intelligent transformation. However, data digitization does not equate to data being utilized as a productive factor. A significant indicator of profound digital-intelligent transformation for traditional enterprises is their ability to further develop and utilize production data, transaction data, quality data, supply chain data, and user data. This involves using data integration and mining to empower R&D design, market analysis, supply chain collaboration, financial credit enhancement, and value-added services. In practice, challenges such as data fragmentation across entities, lack of standardization, insufficient development capabilities, and high data security and compliance requirements often prevent the full release of data value in most local enterprises.
Local governments should strengthen guidance for traditional enterprises to deepen the exploration of data factor value. This includes promoting the opening of anonymized government data to more enterprises, accelerating the development of comprehensive service platforms that support digital-intelligent transformation, and aggregating market resources like transformation service providers, software suppliers, and system integrators. These platforms can offer services such as digital diagnostics, solution recommendations, and service provider matching. In recent years, Shunde district in Foshan has taken proactive steps. Leveraging its local manufacturing base, Shunde has gradually established industry data platforms for sectors like industrial design, plastics, steel, machinery, equipment, and enterprise credit. These platforms aggregate data previously scattered within individual enterprises, transforming it into data resources that support industry operations, market analysis, and resource allocation. Furthermore, Shunde pioneered the development of a government-led e-commerce data privacy computing platform. Under the premise of ensuring security and compliance, this platform enables manufacturing enterprises to access multidimensional data, such as consumer profiles and market trends, which can be used to optimize product positioning and business decisions. This effectively turns data from a byproduct of operations into a new input, a new asset, and a new source of revenue for upgrading traditional industries.