Sen Maolin Jianxia: How AI Transformed the Work of Thousands

Lin Jianxia is a post-80s woman—slim, sharp, and with a strong, energetic voice. When talking about DingTalk's AI tools, she becomes as excited as meeting an old friend, enthusiastically sharing every detail. Colleagues affectionately call her "Sister Xia." In their eyes, she may not be a tech expert, but she makes everyone feel that AI isn't mysterious or distant at all.

Yet she doesn’t fit the stereotype of a technical genius. Just three years ago, she was still a regular business employee at Semir, spending over a decade rotating through roles in sales, planning, and product management—fields almost entirely unrelated to "technology." No one could have imagined that today she would become the company’s “AI practice expert,” personally bringing a groundbreaking tool into the daily workflows of thousands of colleagues.

Lin Jianxia has an unusually strong drive to put AI into action. She says it’s not that she’s special, but rather that opportunity happened to find her—and she was happy to take on challenges beyond her KPIs.

This “selfless” effort stems from a burning belief deep inside her: she firmly believes AI leads to the future. She was deeply struck by the concept of "shared imagination" in Yuval Noah Harari’s book *Sapiens*: humans formed collective consensus through language and storytelling, marking the beginning of civilization. She believes AI is now creating a new kind of “shared imagination.” “The AI spreadsheets and assistants we use today don’t require years of coding just to write a ‘Hello World’ program—they’re tools anyone can pick up and use.”

Her diverse work experience has given her profound insight into the limitations of traditional workflows. But without a technical background, how did she manage to grasp these new tools and transform working methods? And how did she bring AI to her colleagues and bridge information gaps?

Here is Lin Jianxia’s story—

Three years ago, I was transferred to Semir’s Digital Center and, by chance, began leading AI promotion across the entire company. I’m not from a technical background—I’ve learned everything through sensitivity to digitalization and personal interest in AI, step by step.

What truly made me realize the power of AI was early 2023, when ChatGPT 3.5 went viral. At the time, my daughter’s school needed a holiday video, and as a parent committee member, I had to write scripts for 25 parents. It was a tedious task involving research, sentence drafting, and copywriting. On a whim, I borrowed a colleague’s phone and asked ChatGPT. To my surprise, within seconds, the content I wanted was generated.

I was stunned. AI felt like magic. I started wondering: will AI someday become as universal and accessible as language itself?

During my years at Semir, I worked in sales, planning, and product management, so I deeply understand the pain points of traditional ways of working. That’s when I began asking: can AI help us solve these problems?

My first real application of AI at work came from a “virtual try-on” request.

As a clothing company, shooting product displays has always been expensive. In the past, we could only hire models to shoot around 20% of key styles—time-consuming and costly. In 2023, the general manager of Balabala proposed an idea: skip hiring models altogether and use flat-lay images, letting AI simulate how clothes would look when worn. Today this seems common, but back then few had tried it.

I still remember the first time I saw the demo: the flat image appeared perfectly draped on a model—visually intuitive and magical. Business colleagues immediately got excited, imagining how makeup, styling, and photoshoots could all be eliminated.

But the tech team quickly poured cold water on it—standard items like shoes or cups were manageable, but clothing involves too many shape variations. We lacked both training experience and imagination for the models.

Throughout 2023, we were in constant tension. Senior leadership wanted quick results, but the generated images often looked unrealistic. We spent a full month running repeated tests, validating with multiple vendors, conducting A/B comparisons on everything from data preparation to model training. The final conclusion was clear: the technology wasn’t mature enough to meet business needs in the short term, and the project had to be paused.

I wasn’t ready to give up. I directly asked a business colleague: “Forget other factors—if this tool became available tomorrow, would you dare to use it? Would it actually deliver value?” After careful thought, he admitted he wouldn’t feel confident using it yet.

So we reached a consensus: we shouldn’t adopt AI just for the sake of using AI. It must demonstrably improve performance, reduce costs, and increase efficiency.

Still, this attempt taught me that AI-driven productivity gains are exponential—this is definitely a trend, just needing more time. This experience also laid the foundation for our later rapid adoption of AI-powered virtual try-ons and similar workflows.

In 2024, during a training camp organized by DingTalk, I first encountered AI spreadsheets—then called “multidimensional tables.” I was immediately amazed: every meeting and trivial process across the entire company could be managed within a single table.

After trying it myself, I instantly recognized its power and wanted to get colleagues in other departments using it as soon as possible. Once, in an elevator, I ran into an old colleague who mentioned he was juggling several projects. I immediately thought: this AI spreadsheet could help him. I said right away, “Find some time, I’ll walk you through it.”

We used to rely heavily on Excel—jamming timelines and filters into one sheet, then splitting it among different people. But the data was “dead”: it required manual batch updates, which were cumbersome, error-prone, and couldn’t sync in real time.

I’ve personally suffered because of this. One vivid memory was from an order conference: garments were hung, price tags attached, but pricing and policies kept changing. If updated prices weren’t communicated properly, it led to customer confusion and even impacted orders.

Back then, we filled out endless spreadsheets and constantly updated them. We created communication groups where multiple teams edited Excel files in real time. But no matter what, someone always had to manually notify others, and with too many messages, things inevitably slipped through the cracks. One year, uncommunicated policy changes for a few styles led to direct customer complaints. During the review, we realized the root cause was “version inconsistency in spreadsheets.”

Besides order conferences, we now use AI spreadsheets in “time control” scenarios. Fashion lifecycle timing is extremely strict—planning, ordering, and launch dates are all fixed deadlines; any delay impacts downstream processes. Previously, we relied entirely on people monitoring timelines, always chasing deadlines. Now, with AI spreadsheets managing everything centrally, deadline reminders and notifications run automatically.

Soon, departments including administration, procurement, digital center, and supply chain adopted the tool—even accommodating individual form-filling habits. Some prefer filling tables, others favor forms, but all data converges into a single sheet.

A colleague once asked me: “Why can’t events happening in June appear together on the calendar?” I explained: the key is mindset shift—you can’t think in two-dimensional Excel logic anymore. Instead, treat “quarter” as a field. Once he changed his thinking, everything clicked.

Taking it further, we layered on automation—setting start/end times and reminders for tasks. Instead of a cluttered calendar full of notes, the system now automatically pushes notifications and closes loops. One single table manages the entire process—from planning to execution.

Looking back, the company’s exploration of AI began with “interest groups.” When new technologies emerged, there was no standard playbook, so leadership encouraged everyone to experiment freely.

However, they gave the digital center a special mission: as a central platform department, we should coordinate resources and consolidate scattered efforts into a company-wide virtual project team.

This clarified my role: to build a “bridge” so teams across the organization can understand and effectively use AI—making AI tools as accessible and essential as electricity or water.

I’ve taken the MBTI test multiple times, and my results vary each time. I’m a Gemini—when needed, I can be outgoing; other times, I happily retreat to drink tea and read alone. Some teammates describe me as a doer with extraordinary enthusiasm, while others say my energy feels very “positive.” If someone organizes an event, I actively participate and help create a fun atmosphere. Even if no one responds for half a day, I don’t mind—I’ll still jump in. My mindset is relaxed and open.

This attitude carries into my work.

This March, I delivered an AI tools training session in Wenzhou. Originally just a basic intro arranged by the retail training team, I proactively added content on AI spreadsheets. Word spread quickly—not only retail managers, but also colleagues from HR, logistics, and admin wanted to attend. The originally planned small classroom wasn’t big enough, so we moved to a large meeting room that could hold over 100 people. With online participants, more than 400 attended.

It’s common after trainings for colleagues to approach me voluntarily. One person who stood out was Sun Nan—a recent graduate rotating in product planning. Her daily work involved collecting size feedback from “sample wearers” and compiling trial statistics for new seasonal items. Right after the session, inspiration struck: this tool could solve her problem! She immediately messaged me on DingTalk, hoping I’d help assess feasibility.

That weekend, I built a demo for her. She was thrilled: “It really works!” From then on, she dove deeper, gradually enhancing the tool’s functionality. We gave her the initial direction, and she refined it based on real business needs—the tool evolved alongside her efforts.

To support more employees like Sun Nan who want to learn AI, my team and I built a tool called “Da Sen Tree Hollow.” Initially available only to the AIGC group, it later went public in the company-wide chat due to growing demand.

“Da Sen Tree Hollow” is quite interesting. I added an “emotional buffer zone”: when colleagues submit requests and we’re too busy to respond immediately, AI sends an automated reply to ease their frustration before we officially “take the order.” So far, we’ve received nearly 500 user feedback entries.

After the Wenzhou training, some colleagues found the tools incredibly useful but struggled to get started. They kept asking me if I was free for a call. With so many inquiries, the internal training academy reached out and asked if I could recommend a “general-purpose” course. I immediately suggested AI spreadsheets and proposed organizing another dedicated training session.

We scheduled a three-hour introductory class—and to my surprise, response was overwhelming, with over 300 sign-ups.

The impact was immediate: more departments began adopting AI spreadsheets.

This experience taught me something important: voluntary training sessions first gather those eager to learn, then convert knowledge into practical use, turning AI tools into genuine frontline productivity.

At first, I wasn’t familiar with these tools either. I started by watching DingTalk’s live courses, asking official support when stuck. On weekends, I took extra classes, then discussed with business colleagues to answer their questions.

With more trainings, I gained experience: don’t rush. Demonstrate even the smallest steps—like “creating a multidimensional table”—and teach naming conventions, since many Excel habits don’t apply here.

I identify areas where I initially struggled and make them focus points for practice. Seemingly simple features often lead to the pattern: “learn it and forget it.” Without immediate hands-on practice, people forget right after class. So I encourage learners to find a partner and practice together.

Before class, I send out fun questionnaires to give people a sense of the tool—once filled, they can see real-time changes in the group, creating excitement. By the time class starts, they already feel engaged and can intuitively understand why learning this tool matters.

After class, I compile commonly used tools into a learning hub—a cloud document serving as a knowledge base containing all AI tools related to business processes.

I remember once blurting out to my boss: “These tasks aren’t technically part of my job anymore—but I’m still genuinely happy doing them.” He paused, then smiled and said: “You know what? When you said that, your eyes were literally shining.”

Now, our internal AIGC forum has grown from dozens to over a thousand members, with colleagues joining voluntarily. Everyone shares AI applications and recommends tools, and different brands inspire each other. Outsiders have remarked that the level of activity rivals that of a professional AI community.

My original motivation hasn’t changed. Whether in sales or planning, my role was always bridging information gaps. Now, promoting AI is exactly the same role. When information flows smoothly, everyone can act correctly and efficiently.

I lead a team of three members and tend to delegate heavily. We hold weekly meetings to align progress, but otherwise fully trust them to move forward independently. Our DingTalk group is named “Boil Up, Young Warriors”—it sounds energetic and driven.

This year, business demands surged, so we set up multiple virtual teams for different projects. Members come from various departments. I named one project group “Ding Sanduo and His Friends”—our “AI Spark Igniting the Prairie” team. I even used AI to generate a group photo from everyone’s pictures as the avatar, strengthening team identity.

In the apparel industry, consumer feedback is critical. Who understands customers best? Not designers or strategy teams—it’s frontline sales staff. They’re in stores every day, observing how customers browse and listening to honest reactions during try-ons. Sadly, these voices often get distorted as they pass through layers of reporting.

In the past, the product team conducted market research only four times a year, requiring nationwide trips with limited samples and slow turnaround. By the time insights reached headquarters, they were already diluted. If customers said “the fit is too loose,” by the end it might just read “some issues,” leading to vague decisions.

This is where DingTalk’s AI tools shine. Now, a casual remark like “this top feels tight around the waist” can be uploaded instantly, converted into text by AI, and automatically categorized: fabric issue? fit problem? emotional feedback? Different departments see it in real time—information gaps vanish on the spot.

All my sense of purpose comes down to one simple principle: does this create value for the company?

Last year, when I first engaged with AI, the pace was relatively relaxed. This year, everything changed. My team and I are constantly chasing demands because business units now genuinely depend on these tools.

With more requests, discernment becomes crucial. Sometimes features hyped externally receive completely different feedback internally.

Different opinions are inevitable. But seeing positive feedback and praise from business teams gives me a real sense of value—not just helping others, but also achieving personal fulfillment.

Of course, I’m not particularly emotionally intelligent. I often joke with teammates that I’ve “hit rock bottom in EQ” again today. For example, when a leader assigns a task, I might bluntly reply, “This is way too much pressure.”

Luckily, the company culture is supportive. Sometimes when I tell my boss, “I can’t do this,” she doesn’t push me—instead, she sits down and helps me clarify: what’s most important right now? What are the core priorities? I often get lost in details, and she reminds me to focus on value-based prioritization. I’m deeply grateful for that.

The company’s overall investment in AI is increasing. In April this year, Semir’s chairman sent an internal letter calling on all employees to embrace AI from the top down. AI-themed events, competitions, and company-wide AI check-in campaigns have been rolling out one after another.

More people in the digital center are now working on AI. In the past, everything fell on our shoulders: securing resources, budget, manpower, and overseeing product direction and tech development.

This year, things improved: platform development has dedicated owners, and product managers and engineers now handle their respective responsibilities. I no longer have to fight battles alone over resources, budget, and staffing. I can finally focus deeply on education and exploring specific use cases. Scattered dots are slowly connecting. I feel myself getting closer and closer to the goal: truly integrating AI into everyday work practices.

After three years of close engagement with DingTalk’s AI tools, I’ve come to deeply understand one thing: “A gentleman is not born different—he excels at leveraging tools.” Ultimately, it’s not AI itself that determines value, but how we choose to use it.

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