Second-generation factory leaders take the helm, using Feishu AI to propel their factories to "Wuhu" success
On July 25, over six hundred Feishu AI enthusiasts from across the country gathered at the Feishu AI Mastery Conference in Beijing, bringing real-life challenges from their work and daily routines.
Authentic Insights from Frontline Practitioners
During the first half of the event, six frontline practitioners took the stage one by one to share concrete, battle-tested AI application cases.
Midway through the presentations, a young man walked onto the stage wearing a safety helmet and canvas shoes. He is Xiao Pan—the "second generation" leader of an auto parts factory in Wuhu, Anhui province—affectionately called the "young boss" by his coworkers.
After returning home to take over the family business, the first formal AI project he implemented at the factory used AI to activate cameras for detecting workers not wearing safety helmets. His story begins with that very helmet.
From "Wuhu Takeoff" to "The Sky Is Falling"
Before returning to the factory, Xiao Pan had dreamed of effortlessly becoming a wealthy second-generation heir, watching his business "Wuhu" take off. But reality shocked him: "When I came back, I felt like the sky had collapsed."
What he saw was this: operations still relied on paper-based documents; repetitive tasks in Excel were handled manually; employees’ understanding of AI mostly stopped at seeing it as just a chat tool.
So he positioned himself as a Field Deployment Engineer (FDE): understanding factory workflows while turning on-site problems into tasks executable by AI.
Bringing AI into Factories: No Room for Armchair Theory
Xiao Pan quickly realized that introducing AI tools was only the first step. The real challenge lay in integrating AI into employees’ daily routines.
How could he encourage AI-novice employees to give it a try? How could he help them understand the boundaries of AI capabilities? And how could individual explorations evolve into plant-wide habits?
From data digitization and embedding AI agents into chat groups, to establishing training and incentive mechanisms, Xiao Pan gradually carved out a practical path for AI adoption in traditional factories.
Digitalize First, Then Intelligentize
In the actual transformation process, Xiao Pan didn’t rush to develop intelligent agents. Instead, he prioritized solving foundational data issues.
By the end of 2024, he used AI-assisted coding to build small programs that automatically completed repetitive Excel tasks, and gradually migrated paper-based process cards and other forms into Feishu's multi-dimensional tables.
In his view, the prerequisite for a factory to embrace AI is achieving digitization and information integration. Multi-dimensional tables transformed scattered information from paper and spreadsheets into dynamic data streams, enabling subsequent AI agents to read content and track processes.
Therefore, the first step in AI implementation isn’t generating answers—it’s getting real operations online.
Filling Talent Gaps Matters More Than Improving Efficiency
Xiao Pan believes that most small and medium-sized factories today face a more urgent need than efficiency improvement: filling long-standing vacancies in specialized roles.
His chosen starting point was quality management. Since the automotive parts industry must comply with IATF 16949 standards, but the factory had failed to hire a qualified quality systems engineer for two years, he decided to build a "Quality Expert" AI agent.
The agent has three core functions:
Review documents: Automatically checks and flags issues against industry standards;
Answer questions: Employees get instant responses without flipping through manuals;
Train newcomers: Handles basic inquiries, freeing senior staff to focus on complex tasks.
Now, whenever shop floor workers encounter problems, they first consult the AI agent. It handles preliminary reviews and suggestions, while critical decisions regarding compliance and quality are still reviewed and confirmed by professionals.
Once, when R&D staff submitted system documentation for feedback in a group chat, the "Quality Expert" not only generated a review report listing required corrections, but also incorporated the quality manager’s comments and automatically produced a revised version for colleagues to verify.
In this collaborative model, the AI agent performs initial screening, professionals make final judgments, and the entire process is traceable, prompted, and double-checked—truly integrated into the quality management system.
Embedding AI Agents into Messaging Groups
After building the agent, Xiao Pan didn’t hide it behind a separate interface. Instead, he embedded it directly into the instant messaging groups employees use every day.
When employees ask questions, the AI responds based on its knowledge base, while domain experts can chime in to supplement or correct answers within the same thread. Full discussion histories are preserved, and AI periodically summarizes frequently asked questions and high-quality cases, which are then fed back into the knowledge base after human review.
This approach solves a longstanding knowledge-transfer problem in traditional factories: much craftsmanship and experience reside solely in the minds of veteran workers, disappearing once a problem is solved. Now, every human-AI collaboration becomes reusable organizational knowledge.
Old expertise becomes contextual input for the agent’s future work.
Meanwhile, because the agent is publicly visible in group chats, its capabilities and limitations are transparent. When answers are accurate, trust builds; when errors occur, experts can correct them promptly. This open interaction also allows R&D, quality, and management teams to stay aligned on issue progress and outcomes in one shared space, reducing cross-departmental information gaps.
Lowering Barriers to Drive Full Participation
Despite having advanced from paper forms to AI agents in messaging groups, Xiao Pan still faced the most fundamental question: Even if the tools are ready, will employees actually use them?
As the driving force, he needed not only to solve problems and build tools, but also to lower the usage barrier enough for frontline workers to be willing to try.
In small factories, unused tools are common. Xiao Pan summarized three key insights:
First, tools provided by leadership must be plug-and-play—ideally activated with a single command. Any noticeable barrier may deter users. In his words, they must be “foolproof.”
Second, tools developed by peers spread faster than those mandated from above. Employees are more likely to think: “If my coworker can build it, maybe I can too.”
Third, mechanisms should be designed top-down, but behaviors must emerge bottom-up. Management provides resources, training, permissions, and incentives, while employees identify use cases, create tools, and share results.
To support this, he plans to build an enterprise skill library, turning validated AI tools into shared resources for all, and using monthly usage leaderboards and incentive programs to spotlight effective tools.
He also admits that while leaderboards reflect usage frequency, they don’t fully capture actual value creation.
Tools Are Just the Beginning—Organizational Readiness Is Key
At this stage, Xiao Pan gained deeper insight into AI implementation: success shouldn’t be measured merely by how many AI agents were built.
More important is the organization’s supporting capacity: whether training is adequate, data is connected, responsibilities are clear, and individual experiences can be turned into reusable team methodologies.
Tool delivery is just the beginning. "I’m merely lighting a spark. Most of the actual work still needs to be carried out by employees themselves."
Getting factory workers comfortable with AI cannot rely on a single training session or demo.
Xiao Pan’s approach is clear and pragmatic: first, use Feishu multi-dimensional tables to turn paper-based information into dynamic data; then introduce AI agents into chat groups for employee queries and expert validation; have AI summarize full interactions, followed by human review to enrich the knowledge base; finally, drive broader adoption and tool creation through training, a skill library, and incentive mechanisms.
Xiao Pan lights the spark; Feishu connects data, communication, AI agents, and knowledge accumulation into a fully operational workflow.
This transformation, sparked by a single safety helmet, aims not at creating a smarter boss—but at building an entire factory capable of implementing AI with Feishu.
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