As the opening year of the 15th Five-Year Plan, Dongguan has unveiled its urban development strategy centered on smart innovation and quality products, establishing an AI-driven direction for technology-focused industrial growth. In May, the city released its global smart manufacturing hub construction plan, aiming to solidify its manufacturing base while building a worldwide influential intelligent manufacturing center.

The shift from traditional manufacturing to intelligent manufacturing raises a pivotal question: what has artificial intelligence brought to Dongguan's conventional industries? On one hand, inspection efficiency has multiplied and smart factory output has surged; on the other, challenges like data fragmentation, scattered application scenarios, difficult-to-replicate solutions, and workforce restructuring have emerged as pressing concerns. To explore both sides of this coin, our reporters visited multiple industrial clusters across Dongguan, interviewing corporate leaders and industry experts to uncover the dual realities of this transformation.

Dongguan's competitive manufacturing sector remains its defining strength globally, with over 220,000 industrial enterprises, a comprehensive industrial system, and supply chain support covering more than 80% of industrial categories. The city hosts four national-level advanced manufacturing clusters, ranks among the top three nationally in the number of enterprises above designated size, and maintains a top-five position in foreign trade volume nationwide. This vast industrial base provides AI with authentic scenarios, real data, and genuine demand, driving change directly on production lines.

At the Opt Machine Vision exhibition hall in Chang'an, R&D Director Gao Hongchao demonstrated how AI counts electrode tab layers with remarkable precision. "Ask AI to count the layers of a pole piece. For a 25-layer cell, if it counts 24 or 26, that signals a problem," he explained. This algorithm has reduced missed detection rates to one in 100,000, cutting line inspection staff by over 30%. Meanwhile, Hsu Fu Chi has invested over 500 million yuan to build smart factories, achieving a 200% production capacity increase and 20% cost reduction. DeRUCCI, leveraging 5G, AI, and digital twins, has improved new product development efficiency by 22% and shortened production cycles by 45.2%.

Beyond production, AI is making waves in design. Jiang Jun, general manager of Humen Fashion Design City, shared a recent example where Pakistan's Consul General in Guangzhou requested a custom cultural T-shirt symbolizing Sino-Pakistani friendship. Designer Li Jiaxin used AI tools to generate a print solution in minutes, and when the Consul General asked to incorporate an image of a young Pakistani boy, AI quickly updated the design. "He praised it repeatedly, saying it was excellent," Jiang noted. On whether AI threatens design careers, Jiang was definitive: "AI cannot replace a designer's creativity, but designers who don't understand AI may fall behind." To adapt, the Humen Garment Association is actively connecting member companies with AI resources to enhance efficiency and reduce costs, according to Executive Secretary Tu Heng'e.

However, efficiency gains come with complex realities. For many small and medium-sized enterprises, AI presents a straightforward cost-benefit calculation: purchasing computing power requires significant investment, and the question of returns looms large. Chen Guoqiao, secretary general of the Dongguan Custom Home Furnishing Industry Association, pointed out that while leading home furnishing companies show high AI adoption rates, numerous SMEs remain stuck at trial stages. "Design software, ERP, MES, and warehousing systems lack unified interfaces, causing data isolation, duplicate entry, and order errors," he said. Data fragmentation compounds the challenge of scenario fragmentation. Cao Ling, senior R&D director at Opt Machine Vision, acknowledged that while AI deploys quickly in consumer contexts, industrial applications face hurdles including complex working condition precision, scarce defect samples, and lengthy rollout cycles. "Breaking through in one area is easy, but full-chain collaboration is difficult," she explained.

Design sectors also struggle with replication issues. Du Yufeng, marketing director at Mars Project, stated that many trendy toy companies simply apply general large models, resulting in products with "weak competitiveness and short lifecycles." Peter Lee, founding chairman of the Hong Kong Original Brand Development Association, echoed concerns that AI-generated products flooding the market with homogeneity could lead to aesthetic fatigue or even consumer resistance. Beyond technology, workforce structures are being reshaped—simple inspection and transport roles are shrinking while new positions in equipment maintenance and AI result verification emerge. Meng Jihua, assistant president of Kuai Mo Home Group, acknowledged that AI still cannot replace veteran craftsmen for tasks like natural wood defect identification or flexible leather wrapping, leading the company to adopt a "AI preliminary screening, technician final judgment" human-machine collaboration model.

How is Dongguan addressing these challenges? A clear pathway is taking shape: policy guidance, data integration, scenario implementation, and talent upgrading. In July, Dongguan issued its "AI+" city implementation plan for 2026-2030, accompanied by supporting policies encompassing computing power, data, models, major platforms, clusters, talent, scenarios, and security, systematically arranging the entire chain of technological innovation, industrial development, and application. To break data silos, industry consensus, group standards, and shared platforms are essential. Chen Guoqiao suggested that industry associations lead leading enterprises, software providers, and equipment manufacturers in joint discussions to establish group standards and unify common interfaces. To overcome scenario fragmentation, pilot bases and collaborative research are pivotal. Xiao Xuxin, manager of the Binhai Bay National AI Application Pilot Base, emphasized: "The base's mission is to bridge the industry's last mile, enabling rapid scenario implementation." To address talent shortages, human-machine collaboration and job upgrading are crucial. Kuai Mo positions AI as a tool for standardized, repetitive work while human experts handle core creativity, special crafts, and deep customer service. Yunshengtong similarly uses AI for initial screening, with senior technicians making final judgments on complex defects. As Meng Jihua noted, "AI hasn't replaced people; it's pushing employees toward higher-value service and creative roles."

AI's empowerment of Dongguan's manufacturing is not a binary outcome between two sides but rather two facets of the same industrial evolution. While AI delivers efficiency revolutions and design democratization, the accompanying concerns of data fragmentation and scenario scattering remain narrow gates that must be traversed. Ultimately, what determines Dongguan's trajectory isn't one specific large model or smart production line, but whether policy, platforms, enterprises, associations, and talent can be united into a cohesive force—allowing technology to be refined in real workshops, transforming veteran experience into reusable models, and feeding those models back into the entire industry. Only by passing through the narrow gate can one reach the wide-open vista beyond.