After this year's Spring Festival, "DeepSeek" ignited society-wide attention to generative AI (GenAI). Semir internally took advantage of this momentum to "press the accelerator": a letter from the chairman to all employees set the tone first, with posters reading "AI is not a choice, but a necessity" appearing in restaurants and elevator entrances; the HR center partnered with the digital center to embed the organizational loop of "learning-practice-incentive-review-dissemination" into the daily work of 3,000 employees.

In the words of Bai Yun, Senior Director of Semir's HR Center, this is a systematic advancement from culture to mechanism, "hoping that 3,000 colleagues can also engage in AI usage."

Unlike most enterprises that use AI technology to drive individual efficiency improvements, Semir's approach is more like an organizational-level reconstruction. At Semir, one can "see what an AI organization might look like": employees spontaneously form cross-departmental teams from the bottom up, working with IT to solve business problems; HR has systematically adapted everything from course training to certification, promotion, and salary incentives. "After AI improves efficiency, HR has the responsibility and obligation to help employees consider what other more valuable things employees can do," says Bai Yun.

These changes can be summarized in one sentence: this is an AI organization that grew on the DingTalk platform.

So how did this clothing company "grow on the DingTalk platform"? How did it realize the transformation of AI technology from a tool into organizational capability? And how did it rely on a replicable mechanism to precipitate "individual efficiency" into "value chain reconstruction"?

**Getting Started: Beginning with Point-by-Point Attempts**

Semir's AI journey began in June 2023. Lu Lina, Senior Director of Semir's Digital Center, recalls: "We directly established an AI project team, which was a company-level project from the start."

In fact, Semir's entry that year was quite restrained—small entry points, scenario-based, high-value scenario pilots. At that time, text-to-image precision was insufficient to support "marketing blockbusters," so Semir's team first implemented it in segments with lower precision requirements such as ordering materials and e-commerce innovation pages; simultaneously, they worked with colleagues with strong business backgrounds and research enthusiasm for tools to continuously train and improve model performance in vertical apparel scenarios.

The real turning point occurred after this year's Spring Festival. "Not only did management attach great importance to it, but ordinary employees were also greatly impacted by AI," Bai Yun said. Cultural mobilization came first: chairman's letter + posters + internal promotion, forming universal awareness and atmosphere; mechanism incentives followed closely: company-wide 100-day check-in, golden idea collection, and business-integrated challenge competition "three-piece set" launched simultaneously.

The first-half challenge competition selected three representative achievements, with the most attention-getting being the "High-throughput Million T" project. It is understood that this project, based on "high-throughput fabrics + high-throughput patterns," promotes fabric rolling planning, combines intelligent AI matching with current fashion trending elements for rapid image generation and styling, new technology 3D updates improving efficiency by 85%, rich fashionable prints meeting personalized demands, achieving flexible quick response through reprocessing. Additionally, producing based on sales, combined with factory warehouse shipping speed increase of 93%, creates a full-link quick response system from R&D to delivery, achieving small-batch quick response and near-zero inventory operations.

In fact, this is not single-point efficiency improvement, but "tool + process parallel" value chain reconstruction.

When this momentum took root downward, it didn't fall into the stereotype of "generation gap." Lu Lina's judgment is: it's not age, but mindset and curiosity. Semir organized "pioneer forces" in various business units, called "Digital Intelligence Pioneers" in 2024, evolving into multiple forms like "Sharp Knife Battalion," "Club," and "Spark Prairie Fire" in 2025, with AI embrace evolving from super individuals to super organizations.

"These are not hard requirements from the company, but spontaneously formed by the organization." Semir's AI organization "sparks" nourish each other with business scenarios, forming Semir's first growth curve for AI diffusion.

More importantly, unlike many companies that merely view AI technology as a tool for single-point efficiency improvement for individuals or single workflow segments, Semir has upgraded AI from a "productivity" tool to a means of "production relations" reconstruction.

In other words, many companies only advocate "AI is powerful, we must use AI," but don't make organizational-level adaptations, such as incentive mechanisms, skill mastery graded certification for using AI technology to transform work processes, etc.

Some enterprises turn AI into the "efficiency dividend" of a few people, but with unchanged production relations, excellent individuals might "take tools and leave," becoming "super individuals" that organizations cannot retain.

Semir's answer is systematic adaptation: learning resources, incentive policies, promotion channels, and job portraits all aligned with AI.

Specifically, Semir has created a promotion and certification system for "AI super individuals." Bai Yun clarifies, "From this year (2025) to next year's employee promotions, we will make AI certification an indicator for preference. Having such capabilities, combined with performance achievement, will be prioritized for promotion recommendations by business units; when it comes to salary adjustments next year, those with certification will be given appropriate priority for raises."

Since July 2025, Semir launched digital talent certification, receiving a total of 508 applications from various first-level organizations of the parent company, with 56% of applicants passing certification.

Semir also promoted AI talent portrait updates and AI "super individual" talent level certification: incorporating digital capabilities into portraits as "driving factors"; HR and business jointly advancing intermediate talent certification.

According to Lu Lina, "When promoting talent certification last year, even business unit general manager level people actively signed up and passed certification." Semir management internally formed an AI practice sharing mechanism, with business unit general managers conducting closed-loop reviews of effective practices.

In this mechanism, AI is no longer just a productivity tool, but is designed into production relations: avoiding "outsourcing" learning burden to employees while establishing real promotion and salary incentive "anchors."

**DingTalk in Hand: Workstation as Entry Point**

In Semir's AI organization practice, they use DingTalk as the "main operating system" for the AI organization.

"As long as you have DingTalk in hand, all these things can be done," says Lin Jianxia, Semir's AI Innovation Leader. In implementation form, Semir constructed four puzzle pieces that mesh with each other.

The first puzzle piece is the learning zone. The online knowledge base breaks down full-link scenarios: planning insights, design R&D, marketing promotion, terminal retail, operations each linked to "general tools + special tools"; corresponding courses go online through "Sen Academy," combining "general knowledge courses + position-specific courses," with short chapters supporting fragmentation; mandatory courses are pushed "thousand people, thousand faces" by position; learning has points that can be redeemed at restaurants and convenience stores.

The second puzzle piece is the benchmark case library, which is relatively simple: precipitating excellent scenarios from various business units, with "AI Sen Doctor" IP regularly promoting in company-wide groups, forming continuous exposure and reuse.

The third is the AIGC small forum/community: derived from the 2023 "AI Design Competition," iterated to an internal community with "topic circles." Semir has 3,000+ employees, with 1,700+ people voluntarily scanning codes to join. Here, there are both insights on "clever use of AI tools" and compilations of various "toolkits," becoming a hub for inspiration/reuse.

The most important puzzle piece is the AI form (multi-dimensional table) work order system, which is the pilot core of Semir's AI organization: problem-distribution-reply-tracking all systematized.

If we need a simple description: the backend uses AI to automatically tag "problems/expectations/opinions/complaints" and automatically assigns them to corresponding staff; the system provides AI feedback as an "emotional buffer zone"—first giving users a receipt that "someone heard," then waiting for manual follow-up explanations; complex processes like judge scoring, re-evaluation, and thousand-person thousand-face permissions are also handled by the same AI form system.

This realizes "learning-practice-precipitation-reuse-feedback-re-iteration" closed loop at one entry point; more critically, each step comes from business frontlines: tool lists in learning zones are mostly precipitated from "useful things tested by frontline employees in various scenarios," then reused by all employees.

If the above represents the mechanism (infrastructure) supporting the AI organization, then infrastructure must be used for business combat; the most obvious characteristic of Semir's AI organization is reconstructing business production relations: AI is not for showing off technology, but for achieving implementation.

Observing Semir's AI organization implementation, one can analyze from "High-throughput Million T" to "AI Training," which can serve as a perspective window into value chain approaches.

The "High-throughput Million T" project is a typical sample of Semir's move "from efficiency to value chain driving." Lu Lina has a precise summary: tools + processes, then paralleling them, ultimately reconstructing the value chain.

Looking at "AI Training," this embeds AI into terminal shopping guide capabilities to build links with revenue growth, achieved through co-creating training products with DingTalk.

According to Lin Jianxia's description, they first launched version 1.0 in scenarios like training, matching, and customer retention, setting multiple round weights (such as guidance, product comparison, experience segments), continuously optimizing through data feedback to form templates; through templates, they then activated tasks, pushing training to designated store shopping guides, after which guides submit videos on mobile devices, with AI automatically scoring according to set expert models and providing improvement suggestions.

The first round of co-creation was selected in the Bala franchise system, firstly because this system had years of offline "super shopping guide quarterly training + video homework check-in" practice, with AI perfectly "bridging gaps"; secondly, its evaluation system and business rules are complete, facilitating template training and implementation.

Lin Jianxia reveals that currently 500+ shopping guides are actually using it; seeing results, Mini Bala and Semir brands successively came to expand scenarios, "but AI products can't be used directly, they must adapt to your own business rules."

The enlightenment of "AI Training": anchored on business rules, achieving "scenario standardization-business templating-scale replication," and allowing business units to conduct secondary training based on category differences.

Like "High-throughput Million T," this embeds AI into key business backbones rather than "sticking to the surface."

**Marketing and Operations: Finding Real Customers**

Semir's practice on the marketing side shows: finding the right internal customers is more important than tools.

For example, text-to-image: from "generating background images/extreme shooting environment replacement in concept stage" to combining holiday hotspots with "children's new style" to create series posters; more concrete expression of functional points (such as heating, moisture resistance, wind resistance).

Another example is content middle platform + AI mixed editing proliferation, meaning content is centrally processed by the middle platform, while terminal shopping guides and consumers can help brands do secondary word-of-mouth promotion with low barriers.

Interestingly, Lin Jianxia says that when brand marketing departments applied this mixed editing tool, feedback was "very poor"—AI mixed editing efficiency and brand video quality requirements didn't match; instead, operations promotion's needs for multi-channel distribution and video volume changes matched better with the tool's efficiency points.

So, "the same tool has completely different customers in different business departments, you must find the right people," Lin Jianxia says. AI capabilities must be embedded in business segments, finding the "right" people is more important than the capability's own strength.

On the customer service side, Semir differentiated between To B and e-commerce To C: logistics customer service scenarios have "focused problems, demands are to quickly find results," adding AI customer service in the middle can significantly reduce pressure; but e-commerce customer service must be responsible for brand service temperature and sales conversion, so the team is "more cautious."

These customer-specific trade-offs show Semir's sensitivity to AI implementation KPIs and business balancing: not "use if possible," but "use only if it can bring verifiable business value."

In the AI era, the role of traditional IT departments or teams is also being rewritten. Lu Lina splits the team into two groups: one does digital infrastructure upgrades, building application and data integration capabilities around products, retail, and supply chains, overlaying algorithms; the other acts as "evangelists," promoting "everyone can use AI," spreading tools and methods to business frontlines.

"Previously, IT departments (or digital centers) made tools based on business needs, now business systems can make tools themselves," Lin Jianxia says. This means the digital center's role becomes "evangelism and platform": providing platform tools (such as AI forms/multi-dimensional tables, AI assistants), co-creation templates (such as AI training), methodologies and training (such as general courses, case libraries, certification), letting frontline business use "existing bricks" to build "their own walls."

The HR department is responsible for "making overall closed loops from culture, incentives, and training."

AI organization's more important value segments also include: certification as governance, turning individual sparks into organizational assets, such as Semir's AI talent certification, which is a key mechanism for realizing "turning individual sparks into organizational assets."

Currently, Semir's AI talent certification levels are divided into beginner, intermediate, and advanced, covering technical certification for three major channels: AI, BI, and system applications (including but not limited to RPA, AI forms): intermediate application talent is currently Semir's identification focus. After general courses + question banks meet standards, they must pass "application case presentation review"; cases must clearly answer "what scenario, what problem, what tools used, what ROI"; reviews emphasize business value: must be "used for work."

Advanced talent certification requires certified individuals not only to use digital tools to create implementable value, but also to form systematic application capabilities for organizational applications through building workflows or systems, with actual application outputs.

That is, identifying business colleagues who can "build an efficiency system through tools at hand."

Semir's AI talent certification scale and intensity are not light, with AI category registrations numbering over 300. According to Bai Yun, this year's AI case submissions ultimately certified 150 AI talents.

To ensure fairness, judges are organized into four groups on a "cross-business unit, blind selection" basis, with 5 judges in each group from at least 4 different business unit business or technical experts, "each group assigned 70 cases"—the judge workload is evident: "60+ people, each presenting for 10 minutes is 600 minutes." Judge perspectives look at both technical rationality and case completeness and full-link effectiveness.

More importantly, this entire certification system is handled on AI forms: case submission, attachment upload, judge scoring, re-evaluation circulation, permission isolation and "thousand people thousand faces" all "completed in one form."

This not only realizes process-based and transparent review processes, but also enables "excellent cases to immediately enter knowledge base-re-dissemination-re-reuse," forming a growth flywheel.

**Platform and Co-creation: Why DingTalk**

On the "platform choice" issue, Semir's standards are pragmatic: speed matching and ecosystem connection.

First, the foundation for growth—the AI platform itself—must have high-frequency updates. After DingTalk 8.0 release, Lin Jianxia says AI forms (originally "multi-dimensional tables") "iterate very quickly, effectively adapting to our demands."

DingTalk's meeting AI recording supports "languages from over 100 countries, plus dialects," facilitating communication with overseas business units or customers.

Third is ecosystem coordination, one entry point connecting learning zones, AI/RPA/multi-dimensional table zones, benchmark case libraries, frontend connecting suppliers, backend connecting agents, with upstream and downstream "all in the same collaborative ecosystem."

Finally is co-creation orientation, such as AI training, "focusing on core application scenarios, co-creating with leading customers, vertical polishing," while Semir advances in "single scenario completion-cross-business unit replication-expansion coverage" manner.

These modules are very friendly to B-end developers. The digital center coordinates with tools like "Tongyi Lingma" on the code side, can directly integrate applications internally, and then replicate on a large scale.

So Semir is not "buying a bunch of tools," but "organizing business production on a unified platform."

This is precisely the meaning of "growing on DingTalk": learning, dissemination, practice, feedback, review, replication and then reuse polishing all completed on the same network, with organizational nerve endings and business capillaries completely connected through the DingTalk platform.

If we extend the timeline, Semir's AI practice roughly went through three steps:

First, point-like efficiency improvement (2023): piloting tools like "text-to-image" in segments like ordering materials and e-commerce innovation pages;

Second, organizational mobilization (first half of 2025): chairman's letter + posters + three mechanisms (check-in/golden ideas/challenge competition), learning systems and "pioneer-sharp knife battalion" forming networks.

Third, value chain reconstruction (from 2025 to now): represented by "High-throughput Million T" and "AI Training," embedding AI into core processes of product development, supply chain, and terminal training; using certification systems and AI forms as governance foundations, turning business improvements into reusable organizational assets.

AI is not replacement, but empowerment: Semir hasn't stopped "empowerment" at slogans, but written it into mechanisms: promotion, salary increases, certification, courses, communities, cases, work orders, and forms, meshing layer by layer.

Precisely because of this, employee creativity can smoothly transform into organizational structured capabilities and continue to feed back into business growth.

Looking back from today at the years since GenAI emerged, "DeepSeek's" social-level popularity is just an external trigger. What really made Semir become an "AI organization grown from DingTalk" is its organizational engineering: using DingTalk workstation to handle learning, collaboration, applications, and governance; using AI forms to connect rules, processes, and human feedback; using challenge competitions/golden ideas/check-ins to activate bottom-up innovation; using certification to turn "individual sparks" into "organizational assets"; using "evangelists + infrastructure" dual-wheel drive, frontline business changing roles from making demands to making tools themselves.

The essence of this engineering is not "how many tools were used," but whether the organization reconstructed production relations around AI.

Semir's AI organization practice provides the industry with a replicable path: first solidify atmosphere and incentives, then thicken learning and cases, then refine processes through the DingTalk platform, ultimately achieving AI becoming the "cells and flesh" of the value chain.

When "organization online, business online, feedback online" is truly realized, AI is no longer a passing trend, but sinks into the daily operations and annual rings of enterprise sustainability.