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盘点:2026年美国零售巨头们如何用AI换增长?

亿邦动力 2026-09-29 09:30
亿邦动力 2026/09/29 09:30

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美国头部零售商正在把AI提升到公司战略层面,并已产生可量化的回报。

1. 零售巨头们纷纷设立首席人工智能官等新职位,例如塔吉特、家得宝、劳氏,沃尔玛则宣称要成为“AI原生企业”,显示AI不再只是技术试验,而是长期战略。

2. 消费者能直接体验的AI功能包括:塔吉特的AI评论洞察、图片搜索、Buy Again和Continue Shopping;沃尔玛的Sparky智能体可做个性化补货和膳食规划;家得宝的“魔法围裙”每月处理数百万条家装问题;Wayfair的Muse能生成装修效果图;易买得和百思买等也上线了AI购物助手。

3. 对普通用户最有价值的实操信息是:使用AI购物助手能获得更精准的推荐、更快的退货处理和更便捷的购物决策。例如Chewy的AI助手能自动处理约30%的客服对话,劳氏用户使用AI购物助手后线上转化率是不使用的三倍,沃尔玛Sparky用户的平均订单金额比未使用用户高35%。

零售巨头用AI重构消费者发现和购买商品的过程,这直接关系品牌触点和转化效率。

1. 品牌营销方面:AI正在成为新的流量入口,塔吉特接入ChatGPT和Claude,百思买与ChatGPT合作,易买得可跳转至多个AI平台。文章数据显示,塔吉特来自外部AI平台的流量同比增速已超过行业平均3.5倍,品牌应重视在AI对话平台中的露出和推荐位。

2. 产品研发与用户行为:AI评论洞察能识别商品高频讨论标签并按主题归类,塔吉特用它提高转化率和加购率,减少用户“决策疲劳”。品牌可借助这类工具了解消费者真实关注点,反向指导产品改进。

3. 消费趋势与用户留存:文章显示个性化补货、膳食规划、Buy Again复购工具是今年明显趋势,反映消费者对“省事、精准、个性化”的购物体验偏好增强。沃尔玛Sparky的周活跃用户涨幅超100%,说明AI驱动的用户粘性增长可观。

4. 价格和渠道竞争:易买得AI助手结合实时库存和预算推荐,沃尔玛Sparky结合库存、定价和配送时效,意味着价格和库存信息在AI决策中更透明,品牌渠道定价策略需适应这种新比较方式。

对卖家而言,文章透露出AI带来的流量迁移、运营提效和新合作机会。

1. 增长市场:AI购物助手正成为新的高转化流量入口。塔吉特外部AI平台流量同比增速超过行业平均水平3.5倍,百思买备战假日旺季与ChatGPT合作,卖家可主动布局这些AI渠道,争取被AI推荐的机会。

2. 消费需求变化:消费者更倾向通过对话式、个性化方式购物,比如用自然语言生成购物清单、自动补货、膳食规划。塔吉特的Buy Again带动食品饮料类需求,沃尔玛Sparky购买商品数量环比增长超4倍,说明复购场景的AI化能带来增量订单。

3. 事件应对和风险提示:多家零售商在AI布局上设立新岗位、推进长期战略,这意味着平台规则和流量分配可能快速变化。卖家若忽视AI搜索优化和对话式购物适配,可能错失新入口。同时AI客服、库存管理等工具能降低运营成本,例如Chewy的AI助手解决约30%对话问题,预计2026财年节省数千万美元成本。

4. 合作方式:易买得面向商家推出Cart Assistant,零售商可把自己的商品目录和用户数据接入,生成自有品牌AI助手,这是卖家或供应商可以探索的合作方式。

5. 机会提示:家得宝、劳氏的B端工具如Material List Builder和Materials Lists,帮助快速制作清单和报价,针对B端采购的卖家可关注类似AI工具带来的效率提升和客户扩展。

文章提供了零售端AI需求如何传导至生产端的启示。

1. 产品设计和需求:AI正在改变零售商对商品信息的处理方式,例如塔吉特的AI评论洞察会汇总高频标签,Wayfair用AI生成产品图、优化商品文案,这些都会影响产品在零售平台的展示逻辑。工厂在研发和设计时应更重视产品信息的结构化、标签化和视觉化,使商品更容易被AI理解和推荐。

2. 商业机会:AI购物助手依赖大量商品库和实时库存数据,易买得调用了20亿件商品库和超1000万条库存动态信号,沃尔玛Sparky可结合库存布局做推荐。这意味着能提供完善数字商品信息、支持实时库存对接的工厂会更有机会进入零售巨头的AI推荐体系。

3. 数字化和电商启示:家得宝、劳氏等零售巨头把AI嵌入门店、供应链和员工工作流,并强调供应链建设成果是AI决策的基础。沃尔玛CEO称投资AI数据能力能更快决策和履约,说明工厂若想接入这些体系,需要加快自身数字化,包括商品数据、库存数据和履约能力,否则在AI驱动的供应链中可能被边缘化。

4. 注重细节和高复杂度的品类更受AI青睐:Wayfair CEO认为类似商品库这种复杂、重细节的业务,恰恰是AI能创造超额价值的场景,工厂可在复杂产品线中挖掘AI技术带来的展示和销售优势。

服务商可从行业趋势、客户痛点和解决方案角度提取干货。

1. 行业趋势:美国零售巨头正从部门级AI升级为公司战略,新设首席人工智能官职位成为普遍动作,家得宝、塔吉特、劳氏、沃尔玛、百思买都在推进智能体AI的落地。服务商应看到,零售行业对AI的采购将从单点工具转向整链解决方案,尤其需要能与现有门店、供应链和客服系统集成的企业级AI。

2. 客户痛点:零售商既要服务好消费者,又要降低成本和提升内部效率。文章显示,Chewy用AI分流约30%的人工咨询并自动处理退货,百思买部署AI后客服满意度上升、联络成本和履约成本下降,劳氏AI工具让导购和报价更高效。这说明零售客户的核心痛点是客服压力、库存管理、履约成本和运营复杂性。

3. 解决方案方向:一是面向消费者的购物助手,如塔吉特Ask Shipt、沃尔玛Sparky、易买得Clementine,需要整合个性化推荐、实时库存和配送时效;二是面向内部员工的AI工作流,例如劳氏AI Foundry、家得宝魔法围裙、Wayfair自动化产品图制作,可帮助服务商设计可复用的内部AI产品;三是外部AI流量接入,塔吉特和百思买等与ChatGPT、Claude、Gemini合作,服务商可提供跨平台AI对接和电商交易链路方案。

4. 量化价值:劳氏AI购物助手的客户转化率是未使用的三倍,沃尔玛Sparky周活跃用户涨幅超100%、平均订单金额高35%,这些数据可作为服务商向零售客户证明AI价值的参考基准。

平台商可从零售巨头AI战略中看到平台功能升级、招商和治理方向。

1. 商业对平台的需求:零售商正把AI助手变成新的购物入口,塔吉特与OpenAI、谷歌Gemini合作,易买得与Claude、ChatGPT、Gemini和谷歌搜索AI模式打通,百思买也与ChatGPT合作完成下单。这表明电商平台必须考虑如何承接AI对话流量的交易闭环,否则会让用户流失到第三方AI入口。

2. 平台的最新做法:沃尔玛将AI融入广告业务,帮广告主动态调整内容组合,优化投放效果;易买得向商家开放Cart Assistant工具,让零售商接入自身商品目录和用户数据,生成自有品牌AI助手,并支持设置助手名称和对话语气。平台可以通过提供类似AI组件,帮助商家自主运营AI客服和推荐,增强平台生态粘性。

3. 运营管理:塔吉特的Buy Again和Continue Shopping均带来同比两位数转化增长,平台可借鉴这种基于历史行为和未完成订单的个性化召回机制。此外,沃尔玛Sparky结合库存布局、商品定价、配送时效做推荐,说明平台运营需要把库存、价格和物流数据统一纳入AI决策系统。

4. 风险规避和长期战略:百思买强调智能体AI是长期重点战略而非短期项目,塔吉特新设首席人工智能官统筹全业务线,提示平台商在追逐AI热点时应注意组织能力建设,避免各部门各自为战。同时,AI搜索和对话式购物会改变流量分配,平台需要提前防范外部AI平台造成的流量垄断风险,建立自主AI入口。

文章为研究零售业AI转型提供了丰富的产业观察素材。

1. 产业新动向:美国零售巨头集体将AI提升到公司战略层级,表现为新设首席人工智能官(塔吉特)、CTO换帅为AI背景人士(家得宝)、制定“AI原生企业”目标(沃尔玛),反映出AI在传统零售中的地位从部门工具变成长期战略核心。

2. 新问题与商业模式:传统零售业资产重、体量大,AI推进不是另起一局,而是嵌入现有门店运营、消费者体验和员工工作流。本文呈现了几种商业模式:一是AI购物助手驱动个性化购买,如沃尔玛Sparky、塔吉特Ask Shipt;二是AI内部提效工具,如Wayfair用AI工作流将产品图制作成本降低超99%;三是AI外部流量合作,通过ChatGPT、Claude等对话平台形成新的电商入口。

3. 政策法规与治理启示:AI客服自动处理退货、AI生成产品图和商品文案、AI根据实时库存做个性化推荐,这些应用涉及消费者隐私、商品信息真实性和广告披露等问题。文章未直接讨论法规,但研究者可从中提炼出自律规范和政策需求,比如AI推荐透明度、外部AI平台交易责任归属、智能体购物中的数据使用边界。

4. 可量化的影响证据:文章提供多组数据可作为研究基础,例如塔吉特愿望清单创建量同比增长超50%、返校购物转化率增长近20%,劳氏AI购物助手客户转化率为未使用用户的三倍,Chewy超一半订单流转由AI自动化完成,以及百思买客服满意度提升同时联络和履约成本下降。

5. 对行业结构和竞争壁垒的启示:文章提出AI改变消费者发现商品的方式,可能重新收拢分散的购买决策,过去依赖门店、货架和会员体系的壁垒需要重新估值,这是一个值得深入研究的理论命题。

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Quick Summary

Leading U.S. retailers are elevating AI to the level of corporate strategy, and it is already generating measurable returns.

1. Retail giants are creating new executive roles such as Chief AI Officer — Target, Home Depot, and Lowe's are examples, while Walmart has declared its ambition to become an "AI-native company." This signals that AI is no longer a technology experiment but a long-term strategic priority.

2. Consumer-facing AI features now include: Target's AI review insights, visual search, Buy Again, and Continue Shopping; Walmart's Sparky assistant for personalized replenishment and meal planning; Home Depot's "Magic Apron" handling millions of home improvement questions monthly; Wayfair's Muse generating room design renderings; plus AI shopping assistants from E-Mart and Best Buy, among others.

3. The most practical takeaway for everyday users is that AI shopping assistants deliver more accurate recommendations, faster returns processing, and more convenient purchase decisions. For example, Chewy's AI assistant resolves about 30% of customer service conversations automatically; Lowe's users who engage with its AI shopping assistant convert online at three times the rate of non-users; and Walmart Sparky users have an average order value 35% higher than non-users.

Retail giants are using AI to reshape how consumers discover and purchase products, which directly affects brand touchpoints and conversion efficiency.

1. Brand marketing: AI is becoming a new traffic gateway. Target has integrated with ChatGPT and Claude, Best Buy has partnered with ChatGPT, and E-Mart enables routing to multiple AI platforms. The article notes that Target's traffic from external AI platforms grew at a year-over-year rate exceeding the industry average by 3.5 times — brands should prioritize visibility and recommendation placement in AI conversational platforms.

2. Product development and user behavior: AI review insights identify frequently discussed product attributes and organize them into themes. Target uses these insights to improve conversion and add-to-cart rates while reducing "decision fatigue." Brands can leverage such tools to understand what consumers genuinely care about and feed that back into product improvement.

3. Consumption trends and retention: The article highlights personalized replenishment, meal planning, and Buy Again repeat-purchase tools as clear trends this year, reflecting a stronger consumer preference for "convenient, precise, personalized" shopping experiences. Walmart Sparky's weekly active users have grown by more than 100%, demonstrating significant AI-driven engagement gains.

4. Pricing and channel competition: E-Mart's AI assistant makes recommendations based on real-time inventory and budget, while Walmart Sparky integrates inventory, pricing, and delivery speed. This means price and stock information become more transparent in AI-driven decisions, and brands' channel pricing strategies must adapt to this new comparison dynamic.

For sellers, the article signals traffic migration, operational efficiency gains, and new partnership opportunities driven by AI.

1. Growth channels: AI shopping assistants are becoming new high-conversion traffic entry points. Target's traffic from external AI platforms grew more than 3.5 times the industry average year-over-year, and Best Buy has partnered with ChatGPT in preparation for the holiday peak season. Sellers should proactively pursue these AI channels to increase the chance of being recommended.

2. Changing consumer demand: Consumers increasingly prefer conversational, personalized shopping — generating shopping lists in natural language, automatic replenishment, and meal planning. Target's Buy Again has driven demand in food and beverage categories, while Walmart Sparky users have quadrupled the number of items purchased month-over-month. This indicates that AI-enabled repurchase scenarios can generate incremental orders.

3. Risk response: Multiple retailers are creating new executive roles and advancing long-term AI strategies, which means platform rules and traffic allocation may shift quickly. Sellers who ignore AI search optimization and conversational shopping adaptation may miss these new entry points. Meanwhile, AI customer service and inventory management tools can lower operating costs — Chewy's AI assistant resolves about 30% of conversations, with projected savings of tens of millions of dollars by fiscal 2026.

4. Partnership models: E-Mart has launched Cart Assistant for merchants, allowing retailers to connect their own product catalogs and user data to generate a proprietary AI assistant. This is a partnership model sellers or suppliers can explore.

5. Opportunity note: Home Depot's and Lowe's B2B tools, such as Material List Builder and Materials Lists, help quickly create lists and quotes. Sellers focused on B2B procurement should watch for efficiency gains and customer expansion opportunities from similar AI tools.

The article offers insights into how retail-side AI demand transmits to production.

1. Product design and requirements: AI is changing how retailers process product information. For example, Target's AI review insights aggregate high-frequency tags, and Wayfair uses AI to generate product images and optimize product copy — all of which affect how products appear on retail platforms. Factories should place greater emphasis on structured, tagged, and visual product information during R&D and design, making products easier for AI to understand and recommend.

2. Business opportunities: AI shopping assistants rely on extensive product catalogs and real-time inventory data. E-Mart uses a catalog of 2 billion items and over 10 million inventory signals; Walmart Sparky can make recommendations based on inventory positioning. Factories that can provide richer digital product information and support real-time inventory integration will have a greater chance of entering retail giants' AI recommendation systems.

3. Digitalization and e-commerce implications: Home Depot, Lowe's, and other retail giants are embedding AI into stores, supply chains, and employee workflows, emphasizing that supply chain build-out is the foundation for AI decisions. Walmart's CEO says investing in AI data capabilities enables faster decisions and fulfillment. Factories that want to plug into these systems need to accelerate their own digitalization — product data, inventory data, and fulfillment capability — or risk being marginalized in an AI-driven supply chain.

4. Detail-heavy and high-complexity categories favor AI: Wayfair's CEO believes that complex, detail-rich businesses — like product catalogs — are precisely where AI can create outsized value. Factories can explore display and sales advantages enabled by AI in complex product lines.

Service providers can extract actionable insights from industry trends, client pain points, and solution frameworks.

1. Industry trend: U.S. retail giants are moving AI from departmental pilots to company-wide strategy. Creating the Chief AI Officer role is becoming a common move — Home Depot, Target, Lowe's, Walmart, and Best Buy are all advancing agentic AI. Service providers should recognize that retail AI procurement will shift from point solutions to end-to-end systems, especially enterprise-grade AI that integrates with existing stores, supply chains, and customer service infrastructure.

2. Client pain points: Retailers need to serve consumers well while also lowering costs and improving internal efficiency. The article shows Chewy using AI to deflect around 30% of human inquiries and auto-process returns; Best Buy reporting higher customer satisfaction plus lower contact and fulfillment costs after AI deployment; Lowe's using AI tools to speed up associate guidance and quoting. This indicates core retail pain points are customer service pressure, inventory management, fulfillment costs, and operational complexity.

3. Solution directions: First, consumer-facing shopping assistants — such as Target's Ask Shipt, Walmart's Sparky, and E-Mart's Clementine — require integrating personalized recommendations, real-time inventory, and delivery speed. Second, employee-facing AI workflows — Lowe's AI Foundry, Home Depot's Magic Apron, Wayfair's automated product image production — can be designed as reusable internal AI products. Third, external AI traffic integration: Target and Best Buy are partnering with ChatGPT, Claude, and Gemini. Service providers can offer cross-platform AI integration and e-commerce transaction chain solutions.

4. Quantified value: Lowe's AI shopping assistant drives customer conversion at three times the rate of non-users; Walmart Sparky has seen weekly active users grow over 100% and average order value rise 35%. These figures serve as benchmarks for service providers proving AI value to retail clients.

Platform operators can draw lessons on platform capability upgrades, merchant recruitment, and governance from retail giants' AI strategies.

1. Demand from businesses: Retailers are turning AI assistants into a new shopping gateway. Target collaborates with OpenAI and Google Gemini; E-Mart integrates with Claude, ChatGPT, Gemini, and Google's AI Mode search; Best Buy works with ChatGPT to complete purchases. This means e-commerce platforms must build a closed loop for AI conversational traffic, or users will leak to third-party AI entry points.

2. Latest platform practices: Walmart integrates AI into its advertising business to help advertisers dynamically adjust content mixes and optimize campaign performance. E-Mart has opened its Cart Assistant tool to merchants, enabling retailers to connect their own product data and user data to generate a branded AI assistant, with options to customize the assistant's name and conversational tone. Platforms can offer similar AI components to help merchants run AI-powered customer service and recommendations, strengthening ecosystem stickiness.

3. Operations and management: Target's Buy Again and Continue Shopping both deliver double-digit year-over-year conversion growth. Platforms can adopt these personalized recall mechanisms based on purchase history and abandoned carts. In addition, Walmart Sparky makes recommendations based on inventory positioning, product pricing, and delivery speed, suggesting that platforms need to unify inventory, price, and logistics data in AI decision systems.

4. Risk mitigation and long-term strategy: Best Buy emphasizes agentic AI as a long-term strategic priority rather than a short-term project, and Target has created a Chief AI Officer role to coordinate AI across business lines. This reminds platforms to build organizational capabilities when chasing AI trends and avoid siloed initiatives. Meanwhile, AI search and conversational shopping will reshape traffic distribution; platforms need to guard against traffic monopolization by external AI platforms and establish their own AI entry points.

The article provides rich industrial observation material for researching AI transformation in retail.

1. New industry dynamics: Major U.S. retailers are collectively elevating AI to company strategy, reflected in new Chief AI Officer roles (Target), the appointment of AI-experienced CTOs (Home Depot), and the stated goal of becoming an "AI-native company" (Walmart). This demonstrates AI's shift from departmental tool to core long-term strategy in legacy retail.

2. New questions and business models: Traditional retail is asset-heavy and large-scale; AI implementation is not creating a greenfield operation but embedding into existing store operations, consumer experiences, and employee workflows. The article presents several business models: first, AI shopping assistants driving personalized purchasing — e.g., Walmart Sparky, Target Ask Shipt; second, internal AI efficiency tools — e.g., Wayfair using AI workflows to reduce product image production costs by over 99%; third, external AI traffic partnerships — forming new e-commerce entry points through conversational platforms like ChatGPT and Claude.

3. Policy, regulation, and governance implications: AI customer service that auto-processes returns, AI-generated product images and copy, and AI personalization based on real-time inventory all raise issues around consumer privacy, truthfulness of product information, and advertising disclosure. The article does not directly discuss regulation, but researchers can draw out self-regulatory norms and policy needs — such as AI recommendation transparency, liability for transactions on external AI platforms, and boundaries of data usage in agentic shopping.

4. Quantifiable impact evidence: The article provides multiple data points useful for research — Target's wish list creation grew over 50% year-over-year and back-to-school conversion rose nearly 20%; Lowe's AI shopping assistant drives customer conversion at three times the rate of non-users; Chewy automates more than half of order flow with AI; and Best Buy improved customer satisfaction while reducing contact and fulfillment costs.

5. Implications for industry structure and competitive barriers: The article suggests AI changes how consumers discover products and may re-aggregate fragmented purchase decisions. Traditional barriers built on stores, shelf space, and membership ecosystems need revaluation, offering a theoretical proposition worthy of deeper study.

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 .

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【亿邦原创】沃尔玛、塔吉特、百思买、家得宝、劳氏……美国头部零售商们今年似乎都不约而同地做了一件事:把AI从一个部门级的技术工具,抬升到公司战略的层级。

有人新设首席人工智能官(CAIO),有人宣称要向“AI原生企业”转型;从推出面向消费者的AI购物助手,到面向员工的AI运营工具;从用AI实现内部提效,到引入外部AI流量……各家都在密集行动。

这些企业在传统零售场景里已经攒下体量与供应链优势,但当AI开始改写消费者发现和购买商品的方式,分散在各个环节里的购买决策就可能在新的入口被重新收拢——过去靠门店网络、货架和会员体系立起来的壁垒,需要被重新估值。与此同时,体量大、门店与履约资产重的特性,又决定了它们的AI战略推进不可能像轻量玩家一样另起一局,而是需要把AI嵌进现有的店铺运营、消费者体验和员工工作流等环节。

到底如何用AI换来新增长?亿邦动力盘点了8家美国知名零售商今年以来的AI战略推进,从排兵布阵、落地场景、投入回报三条线,看看他们交出了怎样的答案。

01 换帅、设新岗、长期投注,美国零售巨头AI战略的排兵布阵

新设人工智能管理职位,是今年多家美国零售商的共同做法。

4月,家居建材用品零售商家得宝(The Home Depot)首席技术官(CTO)换帅,新任CTO有长期AI领域从业背景,将牵头推进智能体AI和机器学习在全公司落地。

8月,塔吉特(Target)为AI战略增设高层负责人,由首席人工智能官(CAIO)这一新职位,负责在全业务线用AI挖掘业务潜力。

劳氏(Lowes)也在9月的高管调整中,将人工智能升级为企业重点发展方向,公司首席信息官的主管范围加入AI相关职责。

沃尔玛CEO John Furner则表示,公司正在成为“AI原生企业”。2026财年第一季度的财报电话会上,智能体AI被重点提及,Furner认为,基于沃尔玛的供应链建设成果,投资AI驱动的数据能力,能让企业更快做出决策并优质履约。

同样在布局智能体AI的美国头部零售商,还有百思买(BestBuy)。百思买CEO Corie Barry称要扩大智能体AI在业务中的落地应用,并强调百思买的AI项目是为了优化客户体验,并为员工提供工具支持,以此提升整体运营效率。智能体AI也被定义为百思买的长期重点战略,而非短期项目。

02 从个性加购到自主服务,各家AI落地姿态有何不同?

在AI应用上,各大零售类企业各有对策。整体来看,AI改变着消费者发现、评估、购买商品的方式,也重塑着企业的工作运行方式。

一、用AI为消费者提供“私人定制”购物体验

塔吉特先后在6月和8月上线了AI评论洞察及图片搜索工具,前者能够识别商品高频讨论标签,将用户反馈按主题归类,帮助消费者快速聚焦能对购买决策最重要的信息。

此外,塔吉特也在利用Buy Again工具促进复购,可基于用户历史购物行为,展示高频购买产品能够匹配的优惠活动,针对性地留住老用户。Continue Shopping功能则针对未完成购物流程的用户,用个性化算法在用户首页推荐近期浏览商品与替代商品优惠。

塔吉特旗下的Shipt则推出了自有AI购物助手Ask Shipt,用户用文字、图片等形式输入购物需求,AI就能自动匹配商品生成购物清单。

美国线上家居零售商Wayfair前几年已经通过Decorify应用试水AI搜索与商品发现,今年2月,该应用已经迭代升级为Muse。就现有功能来看,Muse不仅可以直接调整商品链接,而且可以帮助用户生成装修效果图,帮消费者打造理想之家。

生鲜即时配送平台易买得9月上线AI购物助手Clementine,可结合消费者饮食偏好、采购预算和历史购物行为,根据实时货架库存数据,完成食物个性化采购。为达成实时个性化推荐效果,该AI助手调用了平台16亿笔累计订单数据、20亿件商品的商品库。同时接收来自北美2200家零售运营商、约10万个线下门店,每日超过1000万条库存动态信号。

沃尔玛的购物智能体Sparky,具有个性化补货、膳食规划功能,能同时结合库存布局、商品定价、配送时效,实现更智能的商品推荐。随着功能不断完善,用户也从最初用Sparky发现商品,到现在用它采购生活必需品。

家得宝的生成式AI工具“魔法围裙”(Magic Apron)则用于帮消费者解决家装项目相关疑问,现在每个月要承接数百万条用户提问。B端客户也可以借助Material List Builder来快速制作清单,扩大采购规模。

二、用AI为企业运营降本增效

消费者只是零售企业用AI服务的对象之一,在消费者看不见的地方,这些企业开发的AI工具,也在为内部工作人员提供便利。

劳氏内部有独立的AI研发平台AI Foundry,用于AI软件开发与内测。劳氏现有的Mylow助手,一方面用于提升用户互动,另一方面可以和员工界面配套,辅助门店导购服务顾客。已经推出的Materials Lists工具,能快速读取文件信息并导入项目规划,让报价工作变得更简单。

Wayfair旗下高端家居子品牌Perigold,则用自研的AI工作流制作产品图,相较原来的实拍置景,能降低超过99%的成本。不只产品图片,全平台的商品文案,也都在用AI优化,来更好地匹配客户需求。

易买得面向生鲜零售商家,开发了另一个AI工具Cart Assistant,零售商可以把自身的商品目录和用户数据接入进去,生成带有零售商自有品牌的AI助手,零售商能自由设定助手名称、对话语气等。

宠物电商平台Chewy的AI购物助手Cai,能自动处理退货退款问题。内置的多个AI智能体,可以分流人工要处理的客户咨询,解决约30%的对话问题,还能把用户诉求转化为智能洞察。宠物医疗门店也能用Chewy开发的工具,进行预约确认、排期调度等工作。

百思买2025年先在内部测试了智能搜索功能,后续在客服中心、内部系统、库存管理中部署AI应用。目前,客服服务满意度、库存管理得到提升,而联络成本、履约成本则同时下降。

沃尔玛的AI则在试图融入广告业务,现在已经可以帮广告主动态调整内容组合,来优化广告投放效果。

三、引入外部AI新流量

如果说将AI嵌入自有平台或内部工作系统,是针对已有用户进行运营优化、让用户愿意留存,那么这些企业与外部AI平台的合作,则在试图从获客角度布局更多的流量入口。

塔吉特已经和OpenAI、谷歌Gemini等进行合作,探索智能体电商模式,最近已经可以通过ChatGPT和Claude进行购物。易买得也采取了同样的合作,用户在Claude、ChatGPT、Gemini或者谷歌搜索AI模式中发起生鲜采购对话后,可以无缝跳转至易买得。

据外媒最新消息,为了备战今年的假日购物旺季,百思买还开始了和ChatGPT的合作,消费者在AI对话平台获得礼品灵感之后,可以直接在AI对话中完成下单。

让AI平台成为外部流量入口,已经显现出了增长潜力。据塔吉特披露,目前来自外部AI平台的整体流量规模虽然还比较小,但同比增速已经超过行业平均水平的3.5倍。

03 降本、留客,众玩家的AI投入已初现成效

令众多零售企业坚定AI投入的是,上述各类AI运营手段已经迎来一些回报,用户增长、成本下降之外,还有可量化的财务收益。

塔吉特的AI评论洞察提高了转化率和加购率,用户的“决策疲劳”也有所减少。Buy Again与Continue Shopping在去年推出后,均带来了同比两位数的转化增长。其中Buy Again还带动了食品和饮料等类别的需求,Continue Shopping带来了额外的加购订单。在AI驱动下,愿望清单的总创建量同比增长超50%,新增物品数量翻倍,返校购物板块的转化率增长了近20%。

Chewy首席财务官介绍,Chewy有超过一半的销售订单流转都由AI自动化系统完成。提效以外,AI购物助手的上线,也在不断吸引年轻客群。2026财年,若按照预期,AI相关项目将为企业节省数千万美元成本。

在劳氏,使用AI购物助手的线上客户转化率是未使用的三倍,累计处理超过2500万条咨询问题,优质的AI体验在推动用户购买决策方面的效果显著。易买得用户通过AI购物助手下单的商品数量高于普通用户,客单价也普遍在115美元以上。

随着沃尔玛AI助手的智能水平和应答质量不断提升,实用价值也越来越强。在上一季度,智能助手Sparky的周活跃用户涨幅超过100%,产生的平均订单金额比未使用用户高出35%,用户通过Sparky购买的商品数量,环比增长超过4倍。

家得宝魔法围裙AI工具有较高的用户互动热度,每月承接数百万条用户提问,不断带来线上转化率提升。

Wayfair的B端客户线上业务的超预期增长,以及第二财季营收增长,AI技术的落地应用都在背后发挥了关键作用。公司CEO认为,类似商品库这类复杂程度高、看重细节的业务,恰恰是AI可以创造超额价值的业务场景,未来对AI的投入也只会有增无减。

本文首发于 亿邦动力 官方网站

文章来源:亿邦动力

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FAQ回顾

美国零售巨头们2026年主要用AI做什么?

2026年美国零售巨头们主要将AI用于三大方向:一是为消费者提供个性化购物体验,如塔吉特的AI评论洞察、沃尔玛的智能体Sparky;二是用AI提升内部运营效率,如劳氏AI Foundry平台、百思买智能搜索;三是与外部AI平台如ChatGPT、Gemini合作引入新流量。整体围绕降本增效和创造新增长点展开。

AI购物助手能为零售商带来哪些可量化的收益?

AI购物助手为零售商带来了显著可量化收益。沃尔玛Sparky周活跃用户涨超100%,用户平均订单金额高出未使用用户35%;劳氏AI购物助手线上客户转化率是未使用的三倍;易买得AI助手下单用户客单价普遍在115美元以上;Chewy超过一半销售订单流转由AI自动化完成。

零售商为何要接入ChatGPT等外部AI平台?

零售商接入ChatGPT等外部AI平台是为了开拓新的流量入口。消费者在AI对话中获得购物灵感后可直接完成下单,如塔吉特已可通过ChatGPT购物,百思买正为假日旺季与ChatGPT合作。目前塔吉特来自外部AI平台的流量同比增速已超行业平均水平3.5倍,显示出增长潜力。

家得宝和劳氏这类家居建材零售商如何用AI服务客户?

家得宝推出了生成式AI工具"魔法围裙",每月承接数百万条用户家装咨询;劳氏则通过AI研发平台AI Foundry开发了Mylow助手和Materials Lists工具,前者辅助门店导购,后者简化报价工作。两家均将AI深度嵌入家装场景,提升了客户决策效率和采购规模。

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