Read More《天猫双11的第十七年，和被AI重写的新地图》
Positive Reviews: AI Empowers the Entire E-commerce Value Chain, Ushering in a New Era of Efficiency-driven Growth
The biggest highlight of Tmall’s Double 11 in 2025 is not the traditional GMV numerical competition, but the comprehensive breakthrough of AI technology from “conceptual narrative” to “practical efficiency improvement”. This “silent transformation” led by Taobao has redefined the underlying logic of e-commerce growth through the application of AI in three major scenarios: traffic distribution, user experience, and merchant operation. When the traffic dividend reaches its peak and the low-price competition becomes unsustainable, AI has become the core variable in reconstructing the matching efficiency of “people, goods, and scenarios”, exploring a new path of “technology-driven and efficiency-first” for the industry.
In terms of traffic distribution, AI’s in-depth understanding of the product library and semantic analysis ability have significantly improved the accuracy of “people-goods matching”. Taobao has used generative AI to clean, enrich, and index a product library of up to 2 billion items, overcoming the limitations of traditional “keyword matching” in searches. For example, when users describe their need for dealing with small flying insects in the sewer in natural language, the system can directly recommend relevant products, increasing the search relevance by 20%. The recommendation system can analyze users’ behavioral intentions (such as the camping interest behind the purchase of a kerosene stove) to achieve “interest expansion”, increasing the click-through rate of the information stream by 10%. This “in-depth decoding” of user needs not only activates potential consumption scenarios (for example, the pet industry optimized product information based on AI analysis of users’ concern about “raw material ingredients”, resulting in an over 40% increase in the turnover of the information stream), but also transforms e-commerce from “passively waiting for needs” to “actively exploring needs”, creating incremental space for platforms and merchants.
On the user experience level, AI has been upgraded from a “tool” to an “intelligent assistant”, truly solving the pain points in consumption decision-making. Taobao has integrated AI capabilities into familiar shopping processes (such as the search box and home page recommendations), launching functions like AI Universal Search, AI Product Selector, and AI Shopping List, covering the entire process of “finding – selecting – replenishing goods”. For example, AI Universal Search can generate solutions for “wedding gifts” based on natural language needs, along with reasons and product details; AI Product Selector filters candidate products according to users’ preferences, reducing the cost of trial and error; AI Shopping List automatically generates customized shopping lists based on users’ historical behaviors and seasonal changes, avoiding impulse consumption. The core value of these functions lies in liberating users from “information overload” and transforming shopping from a “cumbersome decision-making process” into an “efficient experience process”. Data shows that AI Universal Search has solved nearly 50 million consumption needs, and AI Shopping List has generated 2 million customized lists, which confirms users’ strong demand for “intelligent decision-making services”.
For merchants, the popularization of AI tools has significantly lowered the operation threshold and improved decision-making efficiency. Taobao’s AI Business Manager (including the “Promotion Assistant” and six “AI employees”) covers all aspects such as data monitoring, marketing optimization, and customer service. The Promotion Assistant can diagnose real-time indicators such as store traffic and conversion rates and directly provide improvement suggestions; the AI Data Analyst can generate category analysis reports within minutes, replacing the cumbersome process of traditional manual data sorting; the intelligent customer service, Dian Xiaomi 5.0, has reduced the rate of transferring to human service by half and increased the intelligent conversion rate by 35% through context understanding and automated processing. The feedback from small and medium-sized merchants is the most convincing. Peng Huaian, the owner of Naisen Furniture Store, saved a lot of time thanks to AI tools and no longer needed to work overtime during Double 11. Behind the fact that 5 million merchants use the AI Business Manager and 300 million people solve problems through Dian Xiaomi is the qualitative change of AI from being “icing on the cake” to a “must-have tool”, especially providing small and medium-sized merchants with an opportunity for “technological equality” to compete with large merchants.
Overall, the AI practice during Double 11 in 2025 has verified the core logic of “useful AI”: technology must be rooted in business scenarios to solve real pain points (users’ decision-making efficiency and merchants’ operation costs), rather than pursuing “disruptive innovation”. This practical approach has not only brought growth in confirmed GMV and effective revenue to Taobao itself, but also sent a clear signal to the industry – the value of AI in the e-commerce field lies in reconstructing the connection mode of “people, goods, and scenarios” through the accumulation of small – scale efficiency improvements, ultimately unleashing systematic growth potential.
Negative Reviews: Hidden Concerns and Long – term Challenges of AI Implementation Still Need Attention
Although the achievements of AI application during Double 11 in 2025 are remarkable, the potential risks and long – term challenges behind it cannot be ignored. Issues such as technological dependence, privacy boundaries, sustainability of effects, and competitive imbalance may become key bottlenecks restricting the further development of AI – powered e-commerce.
Firstly, over – dependence on AI may lead to the lack of “humanized service”. Although Taobao’s AI tools have improved efficiency, in some scenarios, mechanical algorithm – based recommendations may not replace the warmth of human service. For example, although the intelligent customer service, Dian Xiaomi, can handle high – frequency questions, in complex after – sales disputes or emotional needs (such as when users are in a low mood due to product quality issues), AI’s “rational response” may cause user dissatisfaction. In addition, if the “automated recommendation” of the AI Shopping List overly relies on historical data, it may solidify users’ consumption habits and limit their possibility of exploring new products, thereby weakening the platform’s “discovery value”.
Secondly, the boundaries of user privacy and data security need to be strictly controlled. The accurate recommendation of AI relies on in – depth analysis of users’ behaviors and preferences. Taobao’s AI tools (such as the AI Shopping List) need to integrate users’ browsing, collection, and purchase records. Although the news emphasizes that “user privacy is not involved”, in actual operation, the boundary between “user behavior data” and “privacy information” may be blurred. For example, analyzing the label of “camping enthusiast” for users may involve implicit information such as geographical location (camping sites) and social relationships (accompanying people). If data management is improper, it may cause users’ concerns about privacy leakage and even face compliance risks.
Thirdly, the sustainability of AI effects is questionable. The current effects of AI application during Double 11 (such as a 20% increase in search relevance and a 1.5 – fold increase in merchant efficiency) are mostly based on short – term experiments or centralized verification during promotional events. Can these effects be maintained in the long term? For example, the “interest expansion” of generative recommendation may lead to a decline in recommendation accuracy due to changes in users’ interests (such as camping enthusiasts shifting to other hobbies later). If the “blue – ocean insight” of the AI Business Manager is imitated by a large number of merchants, it may quickly turn into a new “intense competition battlefield”. In addition, the “generalization ability” of AI models also faces challenges. When users’ needs become more complex (such as “wedding gifts for friends that need to be both practical and ceremonial”) or merchant operation scenarios become more diverse (such as niche category merchants), can the existing AI tools still maintain high efficiency?
Finally, AI may exacerbate the “Matthew effect” and squeeze the living space of small and medium – sized merchants. Although Taobao emphasizes that AI tools “lower the threshold for small and medium – sized merchants”, in reality, leading merchants may use AI tools (such as precise advertising and user operation) more efficiently due to their richer data accumulation and stronger technology adaptation ability, further expanding their advantages. For example, the “blue – ocean insight” of the AI Business Manager requires merchants to have a certain ability to understand data. Some small and medium – sized merchants may not be able to fully benefit from it because they “don’t know how to use or use it poorly”. In contrast, leading merchants may quickly seize new markets through AI tools, compressing the “survival space” of small and medium – sized merchants.
Advice for Entrepreneurs: Anchor on “User Needs” and Let AI Be an “Efficiency Lever” Rather Than a “Replacement Tool”
The AI practice during Double 11 in 2025 provides important inspiration for entrepreneurs: AI is not a “disruptor” but an “efficiency lever”. Entrepreneurs need to focus on user needs, combine their own business scenarios, and rationally apply AI technology, avoiding blindly chasing “technological gimmicks”. The following are specific suggestions:
- Focus on “Real Pain Points” and Avoid AI for the Sake of AI: Taobao’s success lies in the fact that AI has solved the real pain points of users (low decision – making efficiency) and merchants (high operation costs). Entrepreneurs need to first clarify the core contradictions in their own businesses (such as users’ “choice difficulty” and merchants’ “low – efficiency data processing”), and then introduce AI tools targeted. For example, e – commerce SaaS entrepreneurs can develop an “AI Product Selection Assistant” to help small and medium – sized merchants quickly analyze market trends; local – life entrepreneurs can launch an “AI Recommendation Engine” to optimize service recommendations based on users’ geographical locations and consumption habits.
- Balance “Technological Efficiency” and “Human Touch”: AI can improve efficiency, but it cannot replace humanized service. Entrepreneurs need to leave room for “human intervention” in technology application. For example, e – commerce customer service tools can set up a mode of “AI first + human backup”, automatically transferring to human service in case of complex problems or emotional needs; intelligent recommendation systems can add a “manual adjustment” function, allowing users to modify their recommendation preferences independently to avoid the “information cocoon”.
- Attach Importance to Data Compliance and User Trust: The accuracy of AI depends on data, but user trust is the foundation for long – term development. Entrepreneurs need to establish strict data collection, use, and protection mechanisms, clearly inform users of data usage (such as “only for optimizing recommendations”), and provide functions such as “data deletion” and “privacy settings”. For example, social e – commerce platforms can adopt the mode of “anonymous processing + user authorization” to obtain behavioral data while protecting privacy.
- Take Small Steps and Verify the Sustainability of AI Effects: The effects of AI tools need to be verified in the long term, avoiding “promotion – style” short – term investment. Entrepreneurs can adopt a cycle mode of “gray – scale testing – data review – iterative optimization”. For example, when launching a new AI function, first open it to 10% of users for testing, collect data such as click – through rates, conversion rates, and user feedback, analyze its long – term value (such as the retention rate after three months), and then decide whether to fully promote it.
- Pay Attention to the Technological Adaptability of Small and Medium – sized Merchants: Small and medium – sized merchants are an important part of the e – commerce ecosystem, but their technological capabilities are limited. When developing AI tools, entrepreneurs need to lower the usage threshold (such as providing a “one – click generation” function and a visual operation interface) and provide supporting training (such as video tutorials and customer service guidance). For example, merchant service tools can design an “AI Operation Assistant” to guide merchants through dialog – based interactions (such as “Which type of products do you want to increase the sales of?”) instead of requiring them to input complex instructions.
Conclusion: The AI practice during Double 11 in 2025 marks the official start of the transformation of e – commerce from “traffic – driven” to “efficiency – driven”. For entrepreneurs, AI is not a technology that “must disrupt everything”, but a tool to “solve old problems in a more efficient way”. Only by being rooted in user needs, balancing technology and humanity, and attaching importance to long – term value can AI truly become the “growth engine” for entrepreneurship.
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