Summary of Key Points
In 2026, the penetration rate of e-commerce AI agents reached 43%, with many products experiencing significant growth and an increase in corporate adoption cases (for example, a women's clothing store saw a 30% rise in net profit, and Jihong's efficiency improved by 60%). However, sales during the 618 shopping festival only increased by 0.9%, indicating that consumers have a low acceptance of AI shopping assistants. The true value of AI agents lies not in their omnipotence but in their ability to free up repetitive tasks and assist with decision-making. The key to successful implementation lies in meticulous tuning, optimization of skill sets, and multi-agent collaboration; failures often result from skipping the tuning process or over-reliance on AI for decision-making.
I. Are AI Agents Useful in E-commerce, or are They Useless?
Some see them as a game-changer (for instance, Cang He used OpenClaw to open a cross-border store, automating product selection and listing processes, reducing monthly costs from several thousand to just 1000 yuan; Zhang Li used 23 agents to manage 100 stores, covering most aspects except for live product selection broadcasts). Others view them as a disappointment (for example, Wang Zhiming implemented AI customer service and marketing strategies, but the AI struggled with complex after-sales issues, requiring additional manpower to correct errors, turning cost reduction into additional expenses).
Key Difference: AI agents are not ready-to-use tools; they require training to function effectively. Successful users invest a lot of time teaching them their decision-making logic and business rules (as Zhang Li pointed out: “The cost of tuning is much higher than the cost of using them”). Those who fail overlook the importance of proper training.
II. Why Are There Such Different Results When Using AI Agents?
The difference lies in the quality of the “skill sets” used. Successful users have done three things:
1. Repeated Tuning: They teach the AI their daily decision-making processes (such as pricing rules and inventory management) to adapt it to their business needs.
2. Continuous Updates: They keep the AI up-to-date with new information from offline sources (platform policies, market trends) to prevent knowledge obsolescence.
3. Task Segmentation: They assign each agent a specific task (e.g., competitor monitoring, content generation), avoiding trying to handle too many tasks at once.
For example, Cang He’s “AI team” has roles such as general managers and content directors, each with a dedicated role; in contrast, Wang Zhiming’s agents tried to handle all customer service and marketing tasks, leading to poor performance in complex scenarios.
III. Are Token Costs Too High? They Can Be Controlled
Tokens are the units used to measure AI processing costs (similar to mobile data usage). The more you use them, the more expensive they become. For example, monitoring cross-border competitors may cost 0.002 yuan per transaction with traditional methods, while using general AI agents could be several times more expensive. Zhang Li’s monthly token and API costs for 5 stores amounted to thousands of dollars.
Optimization Methods:
- Choose Cost-effective Models: Domestic large-model APIs are 1/10 to 1/30 the price of overseas ones, prompting many cross-border sellers to switch.
- Improve Dialogue Strategies: Some merchants reduced the cost per transaction from 1.2 yuan to 0.18 yuan while increasing average order values.
- Avoid Unnecessary Calls: Repeated calls by the AI can waste tokens; optimizing instructions is necessary to minimize waste.
IV. Multi-Agent Collaboration Is the Future Trend
AI agents are moving from working independently to collaborating as a team:
- In China: Shantou MoonClaw uses 8 digital employees to cover the entire process from opening a store to reviewing performance, with the owner handling only 20% of the work; Alibaba Mama’s “AI Wanxiang” uses four agents for comprehensive marketing.
- Abroad: Shopify and Google have collaborated to create the UCP protocol, making it easier for merchants to integrate AI services; Indian e-commerce platforms use agent systems to handle 100,000 calls per day, with conversion rates 20% higher than manual processes.
The future trend is towards automated coordination. For example, an analytics agent that detects a decline in conversion rates will automatically notify the content and advertising agents to adjust images or strategies, allowing merchants to make final decisions.
V. Why Don’t Consumers Buy In?
The reason consumers are hesitant is that AI’s role in e-commerce is misunderstood:
- Lack of Participation: Consumers enjoy the process of discovering, comparing, and selecting products; having AI place orders for them takes away this enjoyment.
- Trust Issues: 55% of overseas consumers are reluctant to let AI make purchases for them, fearing misuse or data breaches.
The Right Role for AI: AI should act as a “helper” rather than a “decision-maker”—for example, by organizing data and recommending options, but the final choice should still be up to the consumer. As a women’s clothing store owner put it: “AI won’t decide whether to restock, but it can save you two hours analyzing reports so you can make better decisions.”
Conclusion
AI agents are not meant to replace humans or become passive profit-making tools; they are part of the e-commerce infrastructure. The future competition will not be about who uses them first, but about who develops more sophisticated skill sets, controls costs more effectively, and achieves better collaboration. Their core value is to eliminate repetitive tasks and leave decision-making to humans.