An AI Spreadsheet That Supports Tens of Millions of Transactions Takes Over 2025 Double 11

Why is DingTalk AI Table first gaining popularity in the e-commerce industry?

The intelligence of tables is not unique to DingTalk, but why is DingTalk AI Table first becoming popular in the e-commerce industry? Because the real challenge in implementing it in e-commerce lies not in the technology itself, but in who truly understands e-commerce.

New technologies often start in industries with the highest data density and shortest feedback cycles. The e-commerce industry handles trillions of transactions annually, thousands of SKUs, and hundreds of marketing events. Especially during Double 11, real-time data and feedback demands from both buyers and sellers are often 100 or even 1,000 times higher than normal. For example, during Double 11 in 2024, Alibaba's total transaction volume across the entire network reached nearly 1.44 trillion yuan—1.5 times the daily transaction volume.

In the past, however, the e-commerce industry relied on countless Excel spreadsheets, CRMs, and ERPs stitched together: cumbersome to use, slow in feedback, inconsistent data standards, and extremely high error rates. What should have been an efficiency-enhancing tool instead became a hidden drain on resources.

First, e-commerce data is highly fragmented—fields differ across systems, standards are inconsistent, and permissions are isolated. Getting an AI table to connect to this heterogeneous data and update in real time is almost like overhauling a company's "data infrastructure." Second, processes are unstructured. Different industries have different workflows; apparel and fast-moving consumer goods, for example, are entirely different categories. During Double 11, tasks such as promotion scheduling, influencer collaborations, inventory allocation, customer service alerts, and after-sales compensation involve information scattered across group chats and emails. Most decisions rely on human judgment. Without deep e-commerce expertise, it is impossible to rebuild a merchant's back-end system.

DingTalk, however, is uniquely positioned because it is backed by the Alibaba ecosystem. It is one of the few platforms in the global AI form field that can directly connect to the underlying data structures of e-commerce. It understands retail—connecting in real time to shelves, inventory, user feedback, and marketing channels—and it also understands the flexible and diverse needs of Chinese merchants. All of this means that DingTalk's AI table inherently understands "e-commerce" better than AI tables from other platforms.

So when DingTalk succeeds in using a single "AI table" to manage Double 11 in 2025, the deeper logic behind this move is to transform the outdated back-end operating system of China's e-commerce industry—using AI to empower humans to make smarter decisions, rather than letting people be bogged down by complex collaborative tasks while vast data goldmines sit untapped.

While AI tables are reshaping the way the e-commerce industry works, the e-commerce industry is also driving the evolution of DingTalk AI tables. Today's AI tables have evolved into lightweight agents that can think, execute, and collaborate. They are no longer viewed as traditional SaaS tools but as the entry point to an entirely new business operating system.

Currently, brands including Semir, Intime Department Store, and the emerging fashion brand AlmondRocks are using DingTalk AI Table to prepare for Double 11.

This August, DingTalk CEO Wu Zhao emphasized DingTalk's approach to AI transformation: First, build around AI and create AI-native products; second, help AI understand the real world so that it can quickly take over routine tasks, leaving humans to focus on decision-making; third, stay humble and truly integrate into every industry.

DingTalk AI Table follows these three principles: build an AI-native product, solve real problems, and help enterprises achieve real results.

AlmondRocks: The "data mid-platform" for small and medium-sized brands

AlmondRocks is a Chinese original designer fashion brand that focuses on "comfortable, stylish, and affordable" clothing. It started with socks and has since expanded into loungewear, base layers, and other categories. It is both a "design brand" and a "content brand"—relying primarily on Xiaohongshu seeding, Douyin livestreams, and influencer collaborations to drive growth. It is a typical omnichannel-operated brand.

The brand's founder, Zhang Qi, has long been troubled by "operational efficiency." Each year, they collaborate with more than 6,000 influencers, yet only 4–5 employees are responsible for managing all the work. Data from each platform is scattered: pricing tables are in Excel, logistics orders are in WPS, and influencer scripts are in WeChat documents. A single businessperson has to juggle seven or eight spreadsheets a day, and errors are the norm.

After adopting DingTalk, they moved all influencer information—pricing, sample shipments, logistics, feedback, content output, and conversion data—into an AI table. What used to require employees to manually enter dozens of fields now requires only 5 or 6 fields, with the AI table automatically pulling the rest of the data. In addition, the AI table can automatically generate a heatmap of influencer performance, using algorithms to identify which influencers are worth long-term collaboration. More importantly, business, legal, and operations teams from different departments can work on a single table, with all information updating in real time.

For example, if DingTalk detects in real time that a particular pair of socks is selling well, back-end staff can quickly see the inventory turnover rate, channel sales distribution, and price elasticity sensitivity, allowing them to quickly amplify the product and turn it into a hit. What would normally take three days to decide can now be completed in a single day.

Emerging brands like AlmondRocks number in the tens of thousands within China's e-commerce ecosystem, and many of them do not have dedicated IT staff. DingTalk AI Table has given them "their own data mid-platform." As founder Zhang Qi puts it, DingTalk AI Table is more like a smart employee: "It is the intelligent core that drives data-driven decision-making and the core competitive advantage for winning every e-commerce battle."

Intime Department Store: A collaboration revolution for a thousand-person organization

AlmondRocks has shown that DingTalk AI Table can give small and medium-sized brands stronger operational capabilities. Intime Department Store has demonstrated that when DingTalk AI Table enters a large organization with thousands of employees, it can also significantly enhance collaboration.

Intime Department Store is one of China's most traditional retail department store brands, with more than 60 stores nationwide. Li Kai is the head of content operations at Intime Department Store. In 2024, he decided to use a single DingTalk AI Table to synchronize the actions of all 62 stores nationwide during group-buying livestreams.

The first thing Li Kai did was to get all offline stores working on the same AI table: Each store fills in information about the prices, inventory, and coupon packages of the products participating in the group buy. The system automatically aggregates, checks, and generates a master table, identifying abnormal fields, inventory gaps, and even price conflicts. Building such a complete livestream business system was accomplished by Li Kai alone using DingTalk AI Table. "You could say the AI table has turned me into an MCN company," he says.

Before each livestream, the DingTalk AI Table automatically sends reminders and proactively advances the project. Within two hours after the livestream ends, it automatically outputs GMV, redemption rates, and ROI comparisons. In traditional department stores, a workflow like this would require hundreds of rounds of communication, weeks of preparation, and dozens of versions of Excel. Now, Li Kai leads just one person, completes everything in five days, and has increased the number of group-buying livestreams from three per month to an average of 10 per month.

Semir: From user feedback to product redesign

Semir, a leading domestic apparel company, is using the AI table directly to drive product redesign.

For a long time, traditional apparel brands have been stuck at a crossroads: On one hand, the market continues to decline—China's total apparel retail sales in 2024 grew by only 2.1% year-on-year, the lowest level in nearly a decade; on the other hand, consumer tastes are changing rapidly—social media has shortened the fashion cycle, and a single viral video can shift the direction of a season's hit products. As a result, the core of competition among apparel brands is shifting from "channel capacity" to "market sensitivity." Whoever can capture consumer feedback fastest can create more hit products.

Before introducing DingTalk AI Table, a customer service representative could handle at most 400–500 pieces of user feedback per day. The work was tedious and repetitive—screenshots, audio recordings, and reviews had to be copied into Excel, then categorized and summarized. Different platforms (Tmall, Douyin, Xiaohongshu) used different standards and fields. During Double 11, customer service representatives were often overwhelmed by messages.