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EnglishIn the early morning, the alumina workshop of Weiqiao Pioneering Group is filled with the roar of machinery.
A young inspection worker holds a smartphone, snapping photos of equipment and the work area. A few seconds later, safety hazards pop up on the screen, highlighting areas that need to be addressed. In the past, details in workshop inspections often required experienced senior workers to spot and fully identify issues;
now, AI’s image recognition capabilities can automatically detect hazards, often finding problems better than some new employees.
On the other side, IT operations staff in the information center are also witnessing a quiet transformation.
A few months ago, the information center team struggled daily with dozens of phone calls and hundreds of emails, answering trivial questions like “How do I clear my browser cache?” After the “Information Center AI Assistant” went live, two-thirds of these requests could be answered correctly by the AI assistant, while the remaining issues were automatically routed, assigned, and tracked, making IT support for a large traditional manufacturing group with 100,000 employees remarkably smooth and efficient.
Such stories are not isolated cases.
In just the past two months, employees at Weiqiao Pioneering Group have already incubated more than 800 AI assistants on the company’s DingTalk platform: Some use it to query electrolytic cell test data at any time, eliminating the need to constantly monitor their computers; others use it to automatically identify on-site safety hazards, preventing potential losses.
To outsiders, artificial intelligence seems to belong in Silicon Valley offices, in labs crowded with PhDs, or in Wall Street trading floors. It appears far removed from Binzhou, from aluminum smelting, and from line workers who may not have high levels of formal education.
But in China, the opposite is happening.
While some parts of the world still set high barriers to AI adoption, DingTalk, China’s national-level enterprise service app, has already given birth to 1.41 million AI applications—and a significant portion of these AI applications come from frontline workers and ordinary business staff.
They may not see themselves as creating “high-tech”; they simply harbor the most basic desire to solve problems. Yet with DingTalk’s support, technology’s impact on productivity becomes strikingly tangible.
In the most grassroots, everyday, and traditional roles—in what many see as the “most rustic” manufacturing environments—practices that could transform the world are emerging.
This contrast is no accident.
The Legend and Transformation of Weiqiao
In China’s private enterprises, Weiqiao Pioneering Group is undoubtedly a legend. Founded in 1951, the company grew step by step from a small oilseed processing plant in Weiqiao Town, Zouping County, Weifang City, Shandong Province, into a global leader in both the cotton textile and aluminum industries. It ranks first among Shandong’s private enterprises and has been listed among the Fortune Global 500 for 14 consecutive years.
Today, at 74 years old, the company is once again embracing a wave of intelligent transformation, driven by grassroots employees’ spontaneous exploration and sparking vibrant grassroots AI innovation. This shift—seemingly unexpected yet entirely natural—is inextricably linked to Weiqiao Pioneering Group’s cultural foundation.
Zhang Bo, chairman of Weiqiao, is the central figure behind this transformation. He has repeatedly told the media that the renewal of traditional manufacturing depends on breakthroughs in managers’ thinking; “Otherwise, it’s hard for employees to truly implement changes, especially in private enterprises.” As a result, throughout Weiqiao Pioneering Group’s digital and intelligent transformation, the entire leadership—from Zhang Bo down—has provided systematic support for grassroots innovation.
In the past, the biggest pain point in digitally transforming traditional industries was the natural communication gap between technical and business departments. Programmers know how to code but lack an understanding of traditional industry needs. Frontline business employees, on the other hand, are highly experienced and skilled in their work, but often have relatively low levels of formal education and lack the ability to develop smart applications—or even articulate their needs in a way that IT staff can understand.
To address this, Weiqiao Pioneering Group established an internal team of several hundred “digitalization specialists.” Most members of this team are senior employees selected from the business front lines—often core business personnel or workshop team leaders—who not only excel in their professional roles but also have a keen interest in technology. After just a few days of training on DingTalk, they gain the basic skills needed to develop AI applications.
And as DingTalk lowers the barrier to AI application development to today’s level, this pain point is naturally being resolved through the collective wisdom of the people.
For example, Master Ma from the alumina branch used DingTalk AI Tables to create a “Hazard Identification Ledger.” Inspection workers simply take a photo and upload it, and the AI points out risk areas and even provides recommendations for corrective action. To date, this system has uncovered more than 800 safety hazards.
Master Sun from the information center developed the “Information Center AI Assistant” mentioned at the beginning of this article. The initial version took just two days to build and deploy, yet it automatically resolves two-thirds of incoming requests, freeing up at least 50% of the information center’s technical staff’s daily workload.
In the past, electrolytic aluminum workshop workers had to rely on “quick sample” test data and on-site readings of temperature, flame, and voltage to understand the condition of the electrolytic cells. But the environments for accessing these two types of data were always in conflict: Quick-sample data could only be viewed on a computer in the office, while on-site readings had to be taken directly in the factory.
Frontline employees had long wanted a mobile app that would allow them to view monitoring data anytime—but they couldn’t find a way to make it happen. With the emergence of DingTalk’s AI capabilities, Master Cui from Weiqiao’s aluminum branch built a “Smart Data Query Assistant” on DingTalk in just three days.
Today, this assistant has become the most frequently used system across Weiqiao’s entire electrolysis workshop. Production departments can now carry out their tasks in the factory while simultaneously checking quick-sample test data on their smartphones whenever needed.
In the past, Weiqiao’s entrepreneurial journey belonged largely to Zhang Bo and his father. Now, 74 years later, DingTalk has become the fertile ground for grassroots innovation at Weiqiao, and Zhang Bo has passed his entrepreneurial spirit on to the most basic-level employees.
In the past, digital transformation implied high barriers: It required specialized talent with expertise in AI, large-scale investment, and lengthy implementation cycles. Even at Weiqiao, which employs 100,000 people, only a few dozen IT engineers were actually capable of developing such systems.
But today, DingTalk’s AI Tables, AI Assistants, and other intelligent product capabilities allow ordinary employees to get started with ease. As Master Sun from the information center puts it: “Among our 100,000 employees, at least 80,000 now have the ability to use AI to solve business problems.”
Even though these innovations are not “earth-shattering,” they represent highly practical “small revolutions.”
In Silicon Valley, AI is at the forefront of scientific research and investment; in the factories of Weifang, China, AI has become a daily tool for workers. This is the true value of the collaboration between Weiqiao and DingTalk: No longer relying on a small group of experts, but harnessing AI to maximize “the wisdom of the people,” unleashing tremendous productivity from the grassroots level, and using quantitative change to drive qualitative transformation.
Technological Equity and Organizational Redesign
Underlying this transformation is a fundamentally different design philosophy between two generations of enterprise service software.
Earlier industrial software systems were typically “top-down” integrations, emphasizing systematization, centralization, and specialization—and requiring a steep learning curve. DingTalk, by contrast, dramatically lowers the barrier to technological adoption, making AI readily accessible and enabling every employee to contribute their own insights and ideas.
This shift—from traditional industrial software systems to DingTalk—represents a fundamental “equity in technology.”
Even today, AI’s capabilities in specialized domains still fall short of what a programmer could achieve by building a custom system, menu, or feature. But when DingTalk’s AI capabilities become everyone’s “third hand,” providing easily accessible intelligent technology products to those with ideas and helping them solve real-world problems… then ordinary frontline employees can accomplish at least 60%, or even 80%, of what a dedicated developer could do.
And when the broadest base of grassroots employees meets cutting-edge AI technology, the energy of transformation is unleashed exponentially. In just two months, 800 AI assistants have emerged at Weiqiao. Each seemingly small tool points to a larger truth: AI is no longer confined to laboratories; it has penetrated the daily routines of China’s most grassroots manufacturing sector.
Today, Weiqiao’s Information Center AI Assistant, Hazard Identification Ledger, and Smart Data Query Assistant may seem fragmented and scattered, but together they form an embryonic framework: data is available on demand, needs can be addressed instantly, and processes can be closed-looped in real time.
This means that knowledge and decision-making authority within the organization are gradually being decentralized.
When the power of 80,000 people is mobilized, the organizational structure must inevitably adjust in reverse: Problem identification and resolution become more precise, innovation chains grow shorter, efficiency increases, and managers cease to be mere “approval machines”—instead becoming “soil creators” who foster innovation...
While the full implications of this shift may not yet be apparent today, the underlying logic is irreversible.
According to Ma Fahong, director of the information center at Weiqiao Pioneering Group, in addition to the more than 800 AI assistants, Weiqiao has also established over 600 knowledge bases internally. The widespread adoption of AI applications has given employees a profound sense of participation and accomplishment, while also mobilizing the broadest possible base of talent to collect and identify business scenarios—a strong foundation for the company’s future deep deployment of AI.
Building on this foundation, Weiqiao has begun to analyze its actual application scenarios, identifying high-frequency, high-value use cases and preparing to intensify its technical efforts in the future to further develop and refine these applications—pushing an AI system that might currently score 50–60 points up to 70, 80, or even 90 points. The goal is to transform these AI assistants from passive tools into true data partners capable of autonomous planning and execution.
Once AI truly becomes a tool deeply embedded in grassroots business operations—rather than a “black box” controlled by a small group of experts—organizational redesign becomes a matter of time. Weiqiao may well become one of the first manufacturing companies in the country to be driven “from the bottom up” by AI.
Lessons from the Weiqiao Model
Weiqiao’s experience may seem accidental, but it contains an inherent inevitability.
Why?
Because it directly addresses a critical pain point in China’s manufacturing transformation: Employees generally have low levels of formal education, there is a shortage of technically proficient personnel, and the demand for digital and intelligent transformation is extremely complex.
In the past, recruiting enough talent who understand both business and digital transformation was nearly impossible, since intelligent technologies were still in their early stages, and everything required exploration. The supply of such talent could never keep up with the demand. As a result, the information center’s few dozen staff were perpetually overwhelmed by the massive demands of 100,000 employees.
With the advent of DingTalk AI, the threshold for “technical expertise” has been lowered to almost zero. Weiqiao’s grassroots employees, even those with only a high school education, can spontaneously seek AI’s help in their business scenarios and even build AI applications themselves to solve their most pressing problems. This democratization of technology directly unlocks the transformative potential of 80,000 people.
Thus, Weiqiao is not an isolated case but a model for others.
China is home to thousands of traditional manufacturing enterprises similar to Weiqiao, all facing nearly identical challenges and struggling to break through in their transformation efforts. Weiqiao’s story shows them that the path forward already exists: By leveraging platforms like DingTalk, lowering the barriers to AI application, and mobilizing the power of “grassroots” innovation.
This is Weiqiao’s broader lesson for the industry.
It is not just a story of one company’s transformation; it is a mirror reflecting China’s manufacturing sector. Today, it is Weiqiao; tomorrow, it could be any textile mill, aluminum plant, steel factory, or even a more traditional, more humble workshop.
In the words of Zhang Bo, chairman of Weiqiao Pioneering Group, artificial intelligence will not overturn traditional manufacturing—but companies that are quick to adopt AI and boost production efficiency are likely to navigate economic cycles more successfully and emerge as winners in the new competitive landscape.
Even Weiqiao itself has set its sights on an even grander future.
As one of the world’s largest aluminum and textile manufacturers, Weiqiao is making large-scale bets on new energy—from lightweight materials for electric vehicles and integrated wind-solar power systems to batteries and the entire electrolytic aluminum value chain. Such a transformation means that Weiqiao must maintain highly efficient collaboration with thousands of upstream and downstream partners.
In its interactions with other companies along the supply chain, Weiqiao’s frontline employees are also considering how to apply AI more effectively, extend its reach, and turn AI assistants into bridges for communication between upstream and downstream partners.
This vision is not far-fetched. When an industry leader proactively connects the links in the chain, what it drives is often not a single breakthrough but an upgrade of the entire industry.
Therefore, Weiqiao’s AI experiments today are not just about its own efficiency revolution; they may also spark a “chain reaction” in the new energy race.
This signals that China’s AI will not remain confined to PowerPoint presentations in big factories or high-frequency trading systems on Wall Street—it will truly take root in the deepest layers of the industrial chain, allowing millions of ordinary factory workers to share in the benefits of intelligent transformation.
Seventy-four years ago, Weiqiao was just a small cotton mill in Zouping, Shandong; today, it is a global leader in aluminum and textile manufacturing.
Chairman Zhang Bo says that the renewal of traditional manufacturing depends not only on machines and capital but on breakthroughs in managers’ thinking. Under his leadership, Weiqiao has not only built the Hongqiao HQCloud industrial internet platform and strengthened its digital foundation; it has also opened a pathway—through its partnership with DingTalk—for “100,000 people to use AI.”
When an established manufacturing company meets new technology, and the most ordinary jobs embrace cutting-edge tools, the gears of history begin to turn.
In the past, only a few engineers in the information center supported the company’s digital transformation; today, 80,000 frontline employees may be creators and users of AI.
This power is the most unique aspect of China’s private economy. It does not belong exclusively to a small group of “high-IQ elites”; instead, it emerges from tens of millions of ordinary people who find ways to solve problems in their daily, seemingly mundane work.
This may be the true answer to the question of how AI should be applied in China: It does not belong solely to Silicon Valley laboratories or to high-frequency trading systems on Wall Street; it appears in the hands of the most grassroots, most ordinary factory workers. Chinese-style AI is certainly not a “technology island”; it represents a new form of productivity shared by all. When the barrier to AI adoption is lowered sufficiently, the collective wisdom of the people erupts like a spark, igniting transformation across entire industries.
From IT operations to hazard inspections, from test data queries to cross-departmental collaboration, every inch of land and every position can be touched by intelligence. When a high-school-educated worker in an aluminum smelting plant can create an AI assistant with their own hands, the world needs to rethink its understanding of China. Weiqiao’s story offers the most powerful answer to the future of China’s manufacturing sector.
Weiqiao is paving the way for the rest of China—and the world—to follow; and what Weiqiao represents today may well be the future of China’s manufacturing industry.
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