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618大变天 阿里、京东、抖音杀入新战场

赵云合 2026-06-09 09:49
赵云合 2026/06/09 09:49

邦小白快读

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本届618是电商行业的重要分水岭,大促竞争焦点已经从过往的补贴低价价格内卷转向AI应用,主流电商已经基本完成AI购物全覆盖,以下是普通消费者可用到的重点干货。

1. 当前各大主流平台都上线了可用的AI购物功能,京东推出独立APP京东AI购,支持对话式沉浸购物,可AI比价、智能算补贴、横向测评家电参数,京东APP内置的京言助手也可辅助购物;阿里打通通义千问和淘宝生态,可实现一句话逛淘宝完成全流程购物,还支持智能选品、虚拟试穿;抖音豆包上线买前问豆包入口,不用跳转就能完成对话式全流程购物。

2. AI购物能大幅简化购物路径,原来需要自己搜索比价做决策,现在只需要说出需求,AI就能完成推荐、比价、下单全流程,还能从用户的日常浏览中挖掘潜在需求,比如你看登山攻略就会推荐对应的登山装备,帮你节省购物时间,提升购物体验。

当前电商行业已经进入AI电商新时代,行业竞争逻辑和流量分发逻辑都发生改变,以下是品牌商可参考的干货内容。

1. 消费趋势和用户行为已经改变:用户购物路径大幅缩短,购物决策入口向AI转移,AI还能挖掘用户潜在消费需求,给品牌带来更多增量客源,主流平台已经完成AI布局,品牌必须跟上AI转型的节奏,才能拿到新的流量红利。

2. 各大平台都推出了面向商家的AI工具可以使用:京东的京小通能提供选品、定价、推广到复盘全流程智能服务,京小智AI客服已经服务百万商家;阿里的AI万相帮助商家实现确定性增长,AI店小蜜覆盖30多个经营场景降本提效;抖音也上线了多环节的商家专属AI助手,品牌可以借助这些工具降低运营成本。

3. AI时代对产品提出了更高要求:AI按照用户真实偏好分发流量,只有品质过硬、贴合细分需求的产品才能持续获得AI自然推荐,品牌需要加大产品研发,深耕用户需求打磨产品,才能持续获得流量。

本届618AI电商加速落地,给卖家带来了新的增长机会,也提出了新的要求,以下是卖家关注的干货整理。

1. 当前AI已经成为电商全交易链条的基础设施,能帮卖家降低运营成本,提升转化效率,各大平台都推出了覆盖选品、定价、推广、客服、复盘全经营环节的AI工具,卖家可以接入使用,实现经营提效,获取更多流量。

2. AI带来了新的流量机会:AI不仅能精准匹配用户需求,还能从用户日常内容浏览中挖掘潜在消费需求,给卖家带来增量流量,而且AI流量分发依托产品本身,只要产品好就能获得持续的自然推荐,流量成本比传统投放更低。

3. 需要注意的风险:AI不是能躺赚的万能工具,核心流量分发还是依托产品实力,如果产品品质不行,无法持续获得AI推荐;同时目前AI行业还未成熟,存在AI推荐受商业投放影响、客观性不足的问题,需要卖家注意合规运营,不要过度依赖AI投放。

AI电商的快速发展,给工厂带来了新的商业机会,也为工厂推进数字化转型提供了新的启示,相关干货整理如下。

1. 能帮助工厂更好把握产品生产和设计需求:AI可以更精准地捕捉用户的细分需求,汇总用户对产品功能、设计的评价偏好,工厂可以依托平台AI工具,获取精准的用户需求信息,反向指导产品的研发、生产和设计,开发更贴合市场的产品,降低滞销风险。

2. 给工厂供应链数字化提供了参考方向:京东已经把AI应用到供应链仓储环节,AI库存诊断助手能带动库存周转效率提升30%到40%,工厂可以参考这个模式,推进自身生产和供应链的数字化改造,提升周转效率,降低库存成本。

3. 给工厂带来了打造自有品牌的新机会:AI电商改变了原来的流量分发逻辑,不再单纯靠砸钱换流量,好产品更容易获得AI的自然推荐,工厂打造自有品牌的流量门槛降低,只要打磨出优质产品,就能获得曝光和成交,是转型品牌的好时机。

当前AI电商已经成为电商行业明确的发展趋势,全行业都在推进AI落地,给服务商带来了新的市场机会,相关干货整理如下。

1. 行业发展趋势明确:电商行业已经告别价格内卷,进入AI竞争的新阶段,AI已经从原来的营销辅助工具转变为贯穿消费、运营、物流、服务全链条的基础设施,不仅头部平台,大量中小品牌、中小卖家都有AI转型的需求,市场空间十分广阔。

2. 当前行业存在明确的客户痛点:一方面大量中小商家没有能力开发全流程的AI经营工具,降本增效的需求强烈;另一方面,现有AI电商还存在推荐受商业利益影响、客观性不足的问题,很多中小平台也没有能力打通全交易环节的AI闭环,这些都是未被满足的客户需求。

3. 服务商可以布局的方向:可以针对中小商家开发轻量化、全流程的AI经营工具,覆盖选品、定价、客服等环节;也可以为中小平台提供AI全链路落地的技术解决方案,还可以探索解决AI推荐客观性问题的合规方案,匹配行业需求。

本届618头部平台的AI电商落地实践,给各类平台商提供了可参考的经验和需要注意的方向,干货整理如下。

1. 当前商家和用户对平台AI能力已经形成刚性需求:用户希望AI简化购物流程,更快做出消费决策,商家希望AI降低运营成本、提升转化效果,布局AI已经不是平台的可选战略,而是必须推进的核心布局,直接决定平台未来的市场竞争力。

2. 平台可以结合自身优势选择对应打法:如果平台拥有深厚的供应链优势,可以参考京东模式,推进AI全链路全场景渗透,从前端消费到后端供应链履约全环节接入AI,靠重资产构筑产业壁垒;如果是拥有海量商品的货架电商,可以参考阿里模式,用大模型串联全链路购物,完成货架电商的智能化闭环;如果是内容型平台,可以参考抖音模式,依托大模型打通种草到成交的链路,实现内容流量的高效变现。

3. 需要注意规避的风险:当前AI电商还没有定型,没有绝对的领跑者,不要因为短期落地快就放松,核心目标是跑通完整的商业化闭环;同时要提前解决AI推荐受商业利益影响的问题,规避用户信任风险。

本届618AI电商的集体落地,标志着电商行业进入新的发展阶段,出现了很多值得研究的产业新动向和新问题,干货整理如下。

1. 产业新动向清晰:电商行业的竞争焦点已经从原来的补贴、低价价格内卷转向AI应用能力的竞争,AI已经从辅助工具转变为贯穿全交易链条的基础设施,目前主流电商已经基本完成AI购物的初步落地,全行业都在摸索AI电商的商业化路径,不同平台基于原有优势形成了多种差异化的商业模式,包括京东全链路AI渗透、阿里大模型串联货架闭环、抖音内容+AI变现等多种模式。

2. 出现了很多值得研究的新问题:目前AI电商发展还处于初期,存在多个待解的行业问题,比如如何平衡商业投放和AI推荐的客观性,如何保障AI推荐的准确度,不同模式的商业化效率谁更高,未来AI电商的最终格局会如何演化,这些都是新的研究方向。

3. 产业层面的新启示:AI电商已经重构了电商行业的竞争逻辑,未来谁能抢占用户购物决策的第一个入口,谁就能掌握流量分发的主动权,这个新的竞争逻辑也为产业研究提供了新的核心命题。

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声明:快读内容全程由AI生成,请注意甄别信息。如您发现问题,请发送邮件至 run@ebrun.com 。

我是 品牌商 卖家 工厂 服务商 平台商 研究者 帮我再读一遍。

Quick Summary

This year's 618 shopping festival marks a major turning point for China's e-commerce industry. The focus of competition has shifted from cutthroat price competition and subsidies to AI applications, and major platforms have nearly completed full-scale deployment of AI-powered shopping tools. Below are key takeaways for general consumers:

1. All leading platforms have launched usable AI shopping features. JD has released a standalone app JD AI Shopping, which supports immersive conversational shopping, with functions including AI-powered price comparison, automatic subsidy calculation and side-by-side comparison of home appliance specs. JD's built-in assistant Jingyan also provides shopping assistance. Alibaba has integrated its Tongyi Qianwen large language model into Taobao's ecosystem, enabling end-to-end shopping with a single text prompt, alongside smart product selection and virtual try-on. Douyin has added a "Ask Doubao Before Buying" entry for its AI chatbot Doubao, supporting full-process conversational shopping without leaving the app.

2. AI shopping greatly simplifies the customer journey. Instead of researching, comparing prices and making decisions independently, shoppers only need to state their needs for AI to handle the entire process of recommendation, comparison and checkout. AI can also uncover latent demand from users' browsing history — for example, it will recommend mountaineering gear when you browse hiking guides — cutting down shopping time and improving the overall experience.

China's e-commerce sector has entered a new era of AI-powered commerce, reshaping both industry competition and traffic distribution logic. Below are key takeaways for brands:

1. Consumer trends and user behavior have shifted fundamentally. The customer journey has shortened dramatically, and the center of shopping decision-making has moved to AI. AI can also uncover consumers' latent demand, driving incremental traffic for brands. As major platforms have completed their AI deployments, brands must keep pace with AI transformation to capture new traffic opportunities.

2. All major platforms have rolled out AI tools for merchants: JD's Jingxiaotong provides end-to-end intelligent services covering product selection, pricing, marketing and performance review, while its Jingxiaozhi AI customer service already serves over 1 million merchants. Alibaba's AI Wanxiang platform helps brands achieve consistent growth, and its AI Dianxiaomi assistant cuts costs and improves efficiency across more than 30 operational scenarios. Douyin has also launched dedicated AI assistants for merchants across multiple operational links. Brands can leverage these tools to reduce operating costs.

3. The AI era raises higher standards for products. AI distributes traffic based on consumers' true preferences, so only high-quality products that fit niche user demands can secure sustained organic AI recommendations. Brands need to increase R&D investment, deepen user insight and refine products to maintain consistent traffic.

This 618 has accelerated the adoption of AI e-commerce, bringing new growth opportunities for sellers while raising new requirements. Below are key takeaways for sellers:

1. AI has become a core infrastructure across the entire e-commerce transaction chain. It can help sellers cut operating costs and improve conversion efficiency. All major platforms have launched AI tools covering the full operational cycle from product selection, pricing, marketing and customer service to performance review. Sellers can integrate these tools to improve operational efficiency and capture more traffic.

2. AI opens up new traffic opportunities: It not only matches user demand accurately, but also uncovers latent consumption demand from users' daily content browsing, driving incremental traffic for sellers. In addition, AI traffic distribution is product-based — high-quality products can secure sustained organic recommendations at a lower traffic cost compared with traditional paid advertising.

3. Sellers should note key risks: AI is not a universal tool for easy profits. Core traffic distribution still depends on product strength, and low-quality products cannot secure sustained AI recommendations. The AI industry is still immature, and AI recommendations can be distorted by commercial paid placements, undermining objectivity. Sellers should operate compliantly and avoid over-reliance on AI-driven marketing.

The rapid growth of AI e-commerce brings new business opportunities for manufacturers and offers new insights for their digital transformation. Below are key takeaways for factories:

1. AI helps manufacturers better align production and design with market demand. It can capture consumers' niche needs more accurately, and aggregate user feedback on product features and design preferences. Factories can leverage platform AI tools to obtain accurate user demand data, which can inform R&D, production and design, helping them develop products that better fit the market and reduce the risk of unsold inventory.

2. It provides a reference direction for supply chain digitalization: JD has applied AI to supply chain and warehousing, and its AI inventory diagnostic assistant has improved inventory turnover efficiency by 30% to 40%. Factories can draw on this model to advance digital transformation of their own production and supply chain operations, improve turnover efficiency and cut inventory costs.

3. AI e-commerce opens up new opportunities for factories to build their own brands. It has reshaped traditional traffic distribution logic, moving beyond a pay-to-play model, where high-quality products are far more likely to earn organic AI recommendations. This lowers the barrier to entry for factories building owned brands, creating an ideal window for brand transformation — manufacturers can secure exposure and sales as long as they deliver high-quality products.

AI e-commerce is now a clear industry-wide development trend, with widespread adoption across the sector creating new market opportunities for service providers. Below are key takeaways:

1. The industry trend is unambiguous: E-commerce has left an era of cutthroat price competition and entered a new phase of competition centered on AI capabilities. AI has evolved from a marketing support tool to core infrastructure spanning the entire value chain of consumption, operations, logistics and services. Demand for AI transformation is not limited to top platforms — it also extends to large numbers of small and medium-sized brands and sellers, creating enormous market space.

2. Clear unmet customer pain points exist: On the one hand, most small and medium-sized merchants lack the capacity to develop end-to-end AI operations tools, leaving strong unmet demand for cost reduction and efficiency improvement. On the other hand, current AI e-commerce still faces problems where recommendations are skewed by commercial interests, lacking objectivity. Many smaller platforms also lack the capability to build a closed-loop AI system across the full transaction chain. All of these represent unmet customer demand.

3. Key directions for service providers to explore: They can develop lightweight, end-to-end AI operations tools for small and medium-sized merchants covering product selection, pricing, customer service and other links. They can also provide end-to-end AI implementation technical solutions for smaller platforms, and explore compliant solutions to improve the objectivity of AI recommendations to match industry demand.

The AI e-commerce implementation by leading platforms during this 618 offers referenceable lessons and key directions for all platform operators. Below are key takeaways:

1. AI platform capability has become a rigid demand from both merchants and consumers. Users want AI to simplify their shopping journey and help them make faster decisions, while merchants want AI to cut operating costs and improve conversion. AI deployment is no longer an optional strategy for platforms, but a core mandatory priority that directly determines a platform's future competitiveness.

2. Platforms can adopt strategies aligned with their own strengths: If a platform has strong supply chain advantages, it can follow JD's model and advance full-link, full-scenario AI penetration, integrating AI from front-end consumer experience to back-end supply chain fulfillment to build competitive barriers through heavy infrastructure investment. For shelf-based e-commerce platforms with massive product catalogs, they can follow Alibaba's model, using large language models to connect the full shopping journey and build an intelligent closed loop for traditional shelf e-commerce. Content platforms can follow Douyin's model, leveraging large language models to connect the full path from content discovery to checkout and enable efficient monetization of content traffic.

3. Key risks to avoid: AI e-commerce is still unformed with no clear front-runner, so platforms should not become complacent over fast early-stage implementation, and should prioritize building a complete, sustainable commercial closed loop. They should also address the problem of AI recommendation bias from commercial interests early on to avoid eroding user trust.

The widespread deployment of AI e-commerce during this 618 marks a new development stage for the e-commerce industry, bringing many new industry trends and questions worthy of research. Below are key takeaways:

1. Clear new industry trends have emerged: The industry's competitive focus has shifted from subsidies and cutthroat price competition to competition over AI application capability. AI has evolved from a supporting tool to infrastructure across the entire transaction chain. Mainstream e-commerce players have largely completed initial deployment of AI shopping functions, and the entire industry is exploring viable commercial paths for AI e-commerce. Different platforms have developed diverse differentiated business models based on their existing strengths, including JD's full-link AI penetration, Alibaba's large model-powered closed-loop intelligent shelf e-commerce, and Douyin's content + AI monetization model, among others.

2. Many new research questions have emerged: AI e-commerce is still in its early stage, with multiple open industry questions remaining, including how to balance commercial placements with the objectivity of AI recommendations, how to guarantee recommendation accuracy, which AI model delivers higher commercial efficiency, and how the final market structure of AI e-commerce will evolve. All of these are new research directions.

3. New industry insights: AI e-commerce has reshaped the competitive logic of the e-commerce industry. Going forward, players that capture the first entry point for users' shopping decision-making will control the initiative in traffic distribution. This new competitive logic has also created a new core research agenda for industry research.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

新的风暴已经来临,任何一方都无法置身事外。

出品 | 电商头条 作者 |赵云合

今年的618,是电商行业的一个重要分水岭。

告别过往单纯比拼补贴、低价、流量的价格内卷,本届618大促竞赛的焦点转向AI。玩家们面对的问题,已不再是“用不用AI”,而是“谁能把AI用得更好”。

AI不再只是单纯的辅助工具,而是贯穿消费、运营、物流、服务全交易链条的基础设施。目前,阿里的千问、京东的“京东AI购”、抖音的豆包、快手的“AI购物助手”等相继落地,主流电商已基本完成AI购物全覆盖。

作为一年一度的消费试炼场,618大促是印证“AI+电商”落地实力的关键节点。谁的AI能帮用户“更快做决定”,帮平台和商家“更低成本做转化”,谁就能在新的竞技场中掌握主导权。

京东、阿里、抖音三国杀

加速布局AI电商

当前,电商平台对AI的应用,主要从前端消费导购、商家经营提效、供应链/直播履约这三方面落地。

其中,京东实现了AI全链路渗透。京东集团技术委员会主席、京东云总裁曹鹏直言:“本届618将实现AI首次全场景、全产业深度融入。”

消费端,京东早在去年年底就推出了独立APP“京东AI购”,用户能体验对话式沉浸购物,还可以进行AI比价、智能算补贴、家电参数横向测评等等。京东AI购帮助用户更快了解产品,做出消费决策。

同时,京东APP内置的AI助手“京言”也可以辅助用户购物。据官方数据,今年一季度,“京言”已经服务接近8000万用户,同比增幅超过200%。

商家端,京东推出的一站式AI智能经营助手“京小通”,能够为商家提供从选品、定价、推广到复盘的全流程智能服务。还有AI客服“京小智”已经服务百万商家,大模型调用量同比增长14倍。

直播带货上,虽然京东不像其他平台有那么多大主播,但其有数字人主播。据京东黑板报,今年618开门红4小时,京东数字人JoyStreamer 实现规模与销量双向突破,开播商家同比增长6倍,带货成交额突破7000万。

供应链端,京东物流超脑大模型应用于超1000个核心场景,全新上线的“AI地图库存诊断助手”带动库存周转效率提升30%至40%。

从消费端到商家端再到履约端,京东将AI渗透入电商交易的每一个环节,让AI从营销工具升级为全链条生产底座,落地价值贯穿购物全周期。

不同于京东,阿里和抖音的打法相似,二者都重构了一个购物入口来撬动电商交易。

阿里旗下通义千问早就凭借“一分钱点奶茶”活动,落地实测AI对话下单的交易模式,验证了AI导购商业化的可行性。

5月11日,千问正式全域打通淘宝生态,用户在和AI的对话中即可完成选品-比价-领券-下单-支付-售后的完整购物流程。背靠淘宝超40亿海量全品类货品资源,千问化身全能消费助手,落地 “一句话逛淘宝” 的购物闭环。

同时,淘宝APP上线内置AI购物助手,支持智能选品、虚拟试穿、优惠自动核算等多元使用功能,优化消费者选购体验。

在商家服务侧,阿里妈妈推出AI万相,旨在打造AI时代的“全新经营范式”,帮助商家带来确定性的增长;还有覆盖30+经营场景的AI店小蜜,高效缩减运营成本、提升商家经营效率。

抖音这边,豆包的势头很猛。据行业消息,2026年春节后,豆包的日活已突破2亿用户。海量的用户储备,为AI电商转化筑牢天然流量底盘,这都是潜在的客源。

618大促期间,豆包在导航栏上线了“买前问豆包”的购物入口,并全面打通抖音商城。用户在对话页面就可以实现商品咨询、智能比价、优惠券领取、选购下单与闭环支付,全程无需跳转,真正实现对话即购物。

为进一步撬动需求,抖音还将百亿消费券同步投放至豆包、红果短剧等多款字节系APP,多端引流反哺商城成交。面向商家端,抖音配套上线了多款商家专属的AI智能助手,覆盖多个经营环节。

整体来看,京东围绕自身供应链优势,AI不只作用前端交易,更渗透仓储、履约、供应链后端全链条,以重资产构筑产业壁垒;

阿里坐拥海量商品资源,以千问串联全链路购物,从用户只能选购到商家AI经营双向落地,完成货架电商智能化闭环;

抖音立足内容生态,依托豆包打通短视频种草与商城成交,实现内容流量高效变现。

快手、小红书的AI电商尝试

当下,阿里、京东、抖音都跑通了AI购物全流程闭环。反观快手和小红书,整体落地节奏稍慢。不过,两家也在不断做出尝试,追赶AI潮流。

618大促期间,快手上线内测了“AI购物助手”,在APP首页单独开辟了入口。

该AI助手主打对话式选购,能够抓取用户浏览、收藏、加购记录,遇上商品降价及时提醒;用户发布指令咨询单品时,它自动拆分好评、差评关键信息,多款商品还能横向比对参数与到手优惠。可惜,目前只停留在选品、比价、整理资讯层面,未打通下单与支付链路。

小红书这边,在今年4月底宣布成立AI一级部门Dots。

平台AI依托海量种草笔记做支撑,依托测评内容精准种草,汇总产品优劣与实际售价,浏览攻略时自动弹出配套好物清单。

不过仅支持跳转商城,AI同样没能实现站内一站式成交,现阶段更多充当选购辅助工具。

各大平台AI电商的落地节奏参差不齐,根源在于各自商业模式、社区生态与供应链底盘天差地别。

眼下AI电商赛道尚无定型格局,没有绝对的领跑者,所有玩家都处在摸索赶路期,落地快慢只是阶段性表象。短期领先不代表锁定胜局,真正的比拼,在于谁能率先跑通完整商业化,抢占最终终点。

AI电商重塑行业竞争逻辑

虽然现在各大电商平台都在加速推进AI电商,但相信不少人依旧存疑:这真的行得通吗?AI推荐的商品就一定是好商品吗?

现阶段来说,AI确实给大众带来全新的购物体验。过去,消费者的购物路径是有购物需求、打开电商平台、在搜索框输出产品、在众多同类商品中进行比价、下单;现在,AI购物助手将这条路径简化为“对AI说出需求、AI推荐并生成订单”。

AI不仅能快速理解用户的需求精准推荐商品,加快用户消费决策的时间;还能从日常对话中挖掘用户的需求,为商家和平台带来增量。比如,用户搜索登山攻略,AI会相应地推荐登山设备。用户在做攻略的同时,把所需的产品也买好了。

值得注意的是,AI并非商家躺赚的万能底牌。AI优化分发逻辑、精准匹配需求,的确能缩短用户找货路径、提升产品曝光机会,但流量红利终究依附于产品实力。

AI算法是根据用户的真实偏好做推荐,只有产品贴合细分需求、品质过硬,才能持续获得AI自然流量推荐。这要求商家深耕用户需求,持续打磨优质产品,以寻求更多被AI推荐的机会。

不过,也有人发出疑问,当AI推荐受到商业投放与利益关系的影响时,所推荐的内容是客观建议还是广告。这是AI电商化的过程中,需要直面的课题。

不可否认的是,当前AI确实给电商行业带来了实实在在的红利。不管是缩短用户下单链路、降低商家运营成本,还是优化履约效率。行业竞争逻辑已随之改变,未来谁能抢占用户购物决策的首个入口,谁就手握流量分发主动权。

注:文/赵云合,文章来源:电商头条(公众号ID:ecxinwen),本文为作者独立观点,不代表亿邦动力立场。

文章来源:电商头条

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