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拥抱AI生产力革命,汇通达如何定义零售新规则

作者:张睿 文章来源:亿邦动力

2026-07-27 09:10

作者:张睿 文章来源:亿邦动力

2026-07-27 09:10

邦小白快读

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本文核心介绍了产业互联网平台汇通达在下沉零售领域落地AI的实践,普通读者可以了解AI对零售行业的改变,以及可接触到的AI实操干货,具体如下:

1. 当前AI已经从单纯的赋能工具变成生产主体,汇通达提出“小门店大模型”核心战略,能让数字化能力薄弱的乡镇小店也低门槛用上前沿AI能力,改变传统开店做生意的模式。

2. 可直接使用的实操AI工具包括千橙AI超级店长,能一键响应门店经营各类需求,几分钟就能完成原来人工一天的海报制作、内容创作、客户线索整理等工作,还有AI生图、直播助手等营销工具,大幅降低使用成本,提升经营效率。

3. 可以了解AI生产力革命的未来趋势,人机协同会成为未来各行业的常态,AI已经深度融入普通人的日常经营生活。

本文给品牌商展现了AI时代零售行业的新变化和新机会,可参考的干货如下:

1. 当前城乡零售都在加速AI转型,下沉市场小门店的AI赋能需求旺盛,汇通达的大数据能力可以精准抓取品类趋势、消费者需求和人群画像,能给品牌的产品研发、定价、营销方向提供数据支撑。

2. 渠道层面,汇通达拥有25.1万家乡镇门店网络,还已经拓展到社区零售、品牌连锁等各类城乡零售业态,目前已经签约安吉尔、Lululemon、自然堂等中大型品牌,品牌可以依托汇通达的AI能力快速下沉布局,通过AI智能体实现全链路获客营销提效。

3. 品牌可以借助汇通达旗下认知边界的大数据能力,实时监控品类销售趋势、消费者搜索热度,提前预判爆款潜力,反向指导产品研发和营销策略调整。

本文给各类线上线下零售卖家梳理了AI转型的新机会、可落地工具和风险提示,干货如下:

1. 机会层面,当前AI已经从工具转变为生产主体,能替代大量人工重复性工作,有效降低经营成本、提升成交效率,现在已经有低门槛的AI工具可供中小卖家接入,不需要承担高额的开发和使用成本。

2. 落地方向:线下实体卖家可以接入千橙AI智能体平台,实现智能收银、远程巡店、全流程获客销售自动化,线上电商卖家可以使用认知边界的AI工具,依托淘天、抖音的实时零售数据预判爆款,完成高效选品。

3. 模式和提示:创新的按效果付费模式比传统订阅制更灵活,降低了卖家的尝试成本,卖家需要尽快完成AI转型,跟上行业变化,避免在AI生产力革命中失去竞争力。

本文给面向零售端的生产工厂带来了AI时代的转型启示和商业机会,干货内容如下:

1. 产品研发设计层面,工厂可以借助汇通达体系下认知边界的大数据能力,获取实时更新的品类销售趋势、消费者搜索热度、不同价位段人群画像等信息,精准把握市场需求,反向指导产品生产和设计,降低研发试错风险,更容易打造出爆款产品。

2. 商业机会层面,汇通达覆盖25.1万家乡镇门店以及各类城乡零售业态,工厂可以接入汇通达的AI赋能零售网络,借助AI精准匹配消费需求,拓展下沉销售渠道,实现精准铺货,有效提升整体销量。

3. 数字化转型启示,工厂可以参考汇通达的路径,将AI能力融入生产和流通的各个环节,推进自身数字化升级,通过人机协同提升整体运营效率,降低综合运营成本。

本文给AI零售领域的服务商梳理了行业发展趋势、客户痛点和可行解决方案方向,干货如下:

1. 行业发展趋势:当前AI Agent正在加速替代功能固化的传统SaaS软件,AI已经从赋能工具转变为生产主体,垂直行业场景落地是AI发展的核心突破口,软硬一体化融合是主流发展方向,按效果付费会成为AI服务的主流收费模式。

2. 客户核心痛点:广大中小零售门店经营者数字化能力薄弱,AI应用的开发和使用成本偏高,传统SaaS无法满足全场景经营的灵活需求,线上线下卖家都缺乏精准的消费趋势判断工具和全链路高效经营工具。

3. 解决方案方向:服务商不需要盲目角逐通用大模型的算力研发,应该聚焦垂直零售场景深耕,把大模型能力拆解适配到中小商家的具体经营环节,降低使用门槛,采用按效果付费的模式匹配商家需求,整合线上线下数据打造全链路AI服务。

本文给零售产业互联网平台梳理了AI转型的可行路径、商家需求和风向规避方向,干货如下:

1. 商家核心需求:当前各类零售商家尤其是下沉市场中小门店,对低门槛、低成本的AI经营工具需求强烈,需要AI覆盖从选品、采购、营销到销售的全流程,替代人工完成繁琐的日常运营工作。

2. 可参考的最新做法:汇通达不走通用大模型算力角逐的路线,选择聚焦场景深耕,推出“小门店大模型”战略,对原有SaaS产品完成全面AI改造,推出全流程AI智能体,布局智能无人零售终端,收购科技公司补全线上电商数据能力,创新按效果付费的收费模式,已经在2025年上半年实现AI收入超6000万元的突破。

3. 风向规避:AI时代零售平台的竞争核心是场景、数据、供应链,而非单纯的算法算力,要避免盲目投入通用大模型研发的风险,聚焦产业场景落地才能建立真正的竞争壁垒。

本文给AI零售和产业互联网领域的研究者提供了最新的产业动向、商业模式创新案例和研究启示,干货如下:

1. 产业新动向:AI生产力革命已经从实验室走向产业腹地,AI Agent加速替代传统SaaS是明确的发展趋势,AI已经从通用消费场景走向垂直产业场景,角色从赋能工具转变为生产主体,人机协同的“碳基+硅基+钢铁机器人”会成为未来新型组织形态,软硬一体化融合是核心发展方向。

2. 新商业模式探索:汇通达提出“小门店大模型”战略,探索出“AI+数字化+软硬一体化”全场景赋能零售的新模式,收费模式从传统SaaS订阅制转变为按效果付费的Skill或Token收费模式,平台角色从SaaS工具提供商转变为AI数字员工的生产与运营方,已经通过实践验证了模式的可行性。

3. 研究启示:AI生产力革命中,最懂产业、能将技术转化为产业可用生产力的企业更有可能建立竞争壁垒,AI下沉零售会推动构建更智能高效、更公平的城乡商业新生态。

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 article introduces the on-the-ground AI implementation practices of Huitongda, an industrial internet platform operating in China’s downmarket retail sector. It helps general readers understand how AI is reshaping the retail industry and provides actionable practical takeaways, as follows:

1. AI has evolved from a purely enabling tool to a core production factor. Huitongda has launched its core "Small Stores, Large Models" strategy, which allows cash-strapped, digitally-underserved township and village retail shops to access cutting-edge AI capabilities at a low barrier, fundamentally transforming the traditional brick-and-mortar business model.

2. Ready-to-use AI tools include Qiancheng AI Super Store Manager, which can address all common store operation needs with one click. It completes tasks that previously took an entire day of manual work—including poster design, content creation, and customer lead organization—in just a few minutes. Additional marketing tools such as AI image generation and live stream assistant also drastically cut costs and boost operational efficiency.

3. Readers can gain insights into future trends of the AI productivity revolution: human-machine collaboration will become the new normal across all industries, and AI has already become deeply integrated into the daily operations and lives of ordinary people.

This article outlines new changes and opportunities in the AI-era retail industry for brand owners, with the following key takeaways:

1. AI transformation is accelerating across both urban and rural retail in China, and demand for AI enablement among small downmarket stores is booming. Huitongda’s big data capabilities can accurately capture category trends, consumer demand, and audience personas, providing data support for brands’ product development, pricing, and marketing strategy.

2. On the distribution front, Huitongda operates a network of 251,000 township and village stores, and has expanded into all types of urban and retail formats including community retail and branded chain stores. It has already signed partnership agreements with large and mid-sized brands including Angel, Lululemon, and Chando. Brands can leverage Huitongda’s AI capabilities to quickly expand into downmarket regions, and use AI agents to improve end-to-end customer acquisition and marketing efficiency.

3. Brands can leverage big data capabilities from Rengjie Bianjie (Cognitive Boundary), a subsidiary of Huitongda, to monitor real-time category sales trends and consumer search interest, identify potential breakout hits in advance, and reverse guide product development and marketing strategy adjustments.

This article organizes new AI transformation opportunities, implementable tools and risk warnings for all types of online and offline retail sellers, with the following key takeaways:

1. In terms of opportunities: AI has now shifted from a supporting tool to a core production factor, capable of replacing a large volume of repetitive manual work, effectively cutting operating costs and boosting conversion efficiency. Low-barrier AI tools are now available for small and medium-sized sellers to access, without the burden of high development and usage costs.

2. Implementation paths: Offline physical sellers can connect to the Qiancheng AI agent platform to enable smart checkout, remote store inspection, and end-to-end automated customer acquisition and sales. Online e-commerce sellers can use AI tools from Cognitive Boundary, which leverages real-time retail data from Taobao Group and Douyin to forecast hit products and enable efficient product selection.

3. Model and recommendations: The innovative pay-for-performance pricing model is more flexible than traditional subscription models, lowering sellers’ cost of testing new tools. Sellers need to complete AI transformation as soon as possible to keep up with industry changes and avoid losing competitiveness amid the AI productivity revolution.

This article shares AI-era transformation insights and business opportunities for retail-facing manufacturing factories, with the following key takeaways:

1. For product R&D and design: Factories can leverage big data capabilities from Huitongda subsidiary Cognitive Boundary to access real-time updated data on category sales trends, consumer search interest, and audience personas across different price points. This allows factories to accurately grasp market demand, reverse guide product design and production, reduce R&D trial-and-error costs, and increase the likelihood of developing hit products.

2. In terms of business opportunities: Huitongda covers 251,000 township and village stores and a full range of urban and rural retail formats. Factories can connect to Huitongda’s AI-enabled retail network, leverage AI to accurately match consumer demand, expand downmarket sales channels, achieve optimized精准 distribution, and effectively boost overall sales volume.

3. For digital transformation insights: Factories can follow Huitongda’s example to integrate AI capabilities into all links of production and distribution, advance their own digital upgrade, and improve overall operational efficiency and reduce total operating costs through human-machine collaboration.

This article organizes industry development trends, customer pain points and viable solution directions for AI retail service providers, with the following key takeaways:

1. Industry development trends: AI Agents are rapidly replacing traditionally rigid-function SaaS, and AI has shifted from an enabling tool to a core production factor. Vertical industry scenario implementation is the core breakthrough for AI development; integration of software and hardware is the mainstream development direction; and pay-for-performance will become the dominant pricing model for AI services.

2. Core customer pain points: Most small and medium-sized retail store operators lack digital capabilities, and the development and usage costs for AI applications remain relatively high. Traditional SaaS cannot meet the flexible needs of full-scenario operation, and both online and offline sellers lack tools for accurate consumer trend forecasting and end-to-end efficient operation.

3. Solution directions: Service providers do not need to blindly compete for general-purpose large model computing power R&D. Instead, they should focus on deep cultivation of vertical retail scenarios, adapt large model capabilities to the specific operational links of small and medium-sized merchants, lower the barrier to usage, adopt a pay-for-performance model to align with merchant needs, and integrate online and offline data to build end-to-end AI services.

This article organizes viable paths for AI transformation, merchant demand and risk mitigation directions for retail industrial internet platforms, with the following key takeaways:

1. Core merchant demand: All types of retail merchants, especially small and medium-sized stores in downmarket regions, have strong demand for low-barrier, low-cost AI operation tools. They require AI to cover the entire process from product selection, procurement, marketing to sales, to replace manual work for tedious daily operations.

2. Referenceable latest practices: Instead of competing for general-purpose large model computing power, Huitongda chose to focus on deep scenario cultivation, launched its "Small Stores, Large Models" strategy, completed a full AI overhaul of its original SaaS products, launched end-to-end AI agents,布局 smart unmanned retail terminals, acquired a technology company to补全 online e-commerce data capabilities, and innovated a pay-for-performance pricing model. In the first half of 2025, it exceeded RMB 60 million in AI-driven revenue.

3. Risk mitigation: In the AI era, the core competitiveness of retail platforms lies in scenarios, data and supply chains, not just algorithms and computing power. Platforms should avoid the risk of blindly investing in general-purpose large model R&D, and focus on industrial scenario implementation to build real sustainable competitive barriers.

This article provides the latest industry trends, business model innovation cases and research insights for researchers focused on AI retail and industrial internet, with the following key takeaways:

1. New industry trends: The AI productivity revolution has moved from the lab to the core of real industry. The replacement of traditional SaaS by AI agents is a clear development trend. AI has expanded from general consumer scenarios to vertical industrial scenarios, and its role has shifted from an enabling tool to a core production factor. The human-machine collaborative "carbon-based + silicon-based + steel robot" new organizational form will become the norm in the future, and integrated software-hardware development is the core development direction.

2. New business model exploration: Huitongda’s "Small Stores, Large Models" strategy has explored a new "AI + digitalization + integrated software-hardware" full-scenario retail enablement model. Its pricing model has shifted from traditional SaaS subscriptions to pay-for-performance Skill or Token-based pricing, and the platform’s role has shifted from a SaaS tool provider to a producer and operator of AI digital employees. Early implementation has already validated the feasibility of this model.

3. Research insights: In the AI productivity revolution, enterprises that understand the industry best and can translate technology into usable productivity for industry are far more likely to build competitive barriers. AI-enabled downmarket retail will drive the construction of a smarter, more efficient and more equitable new urban-rural commercial ecosystem.

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.

【亿邦原创】当英伟达创始人黄仁勋提出“AI不再是工具,而是我们的同事、员工”这一论断时,它标志着一个产业认知的根本性转变:AI Agent(智能体)正在加速替代功能固化的传统SaaS软件,一场深刻的AI生产力革命已从实验室走向产业腹地。

在此背景下,深耕中国下沉市场十余年的产业互联网平台——汇通达网络已经开启了一场全面的技术升级和商业模式进化,从“千橙云AI”智能大模型到“千橙AI”智能体平台,再到“边界BI”数据系统,汇通达将AI能力深度下沉,融入中国最广阔也最复杂的零售业态毛细血管之中,让乡镇零售门店、连锁便利店、快消折扣店、品牌专卖店以及电商店铺等零售场景,都能够在AI浪潮中实现规模增长、成本更低、效率更高。

01

“小门店大模型”

促销季来临,想用几张宣传海报做一波线上线下宣传?想运营小红书账号拓展客户群,不知道应该发布什么内容?各个渠道来的客户线索无法精准管理有效筛选?……

线下门店经营每天会面临类似的问题,以往靠有经验的员工花费一天时间处理,而如今AI仅需几分钟便能实现。

比如,商家使用汇通达网络面向零售门店、供应链伙伴推出的“千橙AI”智能体平台,能够更便捷、低门槛地调用“千橙系”智能体,助力在电商运营、社群营销、商品采购等多场景下的AI提效。

其中,改版后的“千橙AI超级店长”,上线了智能体一键响应功能,可以快速响应零售门店经营中的各类需求,通过更智能的采购、分销、直播……提升门店从选品到成交的全流程经营效率;“千橙AI”门户则为商家提供了“AI生图”“直播助手”等常用的营销功能,并支持国内外多种大模型统一配置调度,大幅降低普通商家的AI应用开发、使用成本,为商家的全场景营销和经营提效提供助力。

“店还是那个店,但做生意的方式不一样了。”一位家电门店老板如是说。

当大模型技术从通用走向垂直,从消费场景渗透至产业深处,具备自主理解任务、拆解步骤并执行闭环能力的AI智能体,开始扮演起“虚拟员工”的角色,对于广大线上线下零售行业从业者来说,AI已经不是“赋能工具”,而是“生产主体”。

这也是为什么汇通达提出“小门店大模型”的核心战略,其庞大的线下触点与复杂的零售场景,恰好为AI的规模化应用提供了绝佳的试验场与价值释放空间。

02

汇通达的零售全场景AI实践

所谓“小门店大模型”,意指将最前沿的AI能力,拆解、适配并深度融入下沉市场那些面积仅数十平米、经营者数字化能力薄弱的“小门店”的每一个经营细节中。

也就是说,汇通达将成为AI数字员工的生产与运营方,使“小门店”以更低门槛接入智能工具,向AI时代转型。

基于对零售场景的深度理解,汇通达AI实践的具体落地路径包括三方面:

首先,是SaaS产品的全面AI化,门店管理系统及超级店长APP进行全面AI改造,优化界面布局与操作逻辑,打造AI协同的智能运营体系。智能收银终端能自动识别商品,AI巡店系统可远程分析陈列问题,将传统繁琐的运营工作自动化;

其次,聚焦于能够为门店创造直接收入的AI智能体,如AI销售智能体,覆盖从获客、交流、选品、销售跟单、数据分析及复盘的全流程,AI营销智能体实现从用户洞察、智能投放到效果核销的全链路智能化,其中包括通过AI线索、AI外呼等工具提升获客效率;

最后,布局零售机器人等智能终端,启动全无人零售店试点,整合巡店机器人、智能货架与自动补货系统,实现“上货—理货—销售—收银”全链路无人化。

值得一提的是,汇通达拥有25.1万家乡镇门店网络,但其AI服务范围已经不只乡镇门店,而是延展至社区零售、折扣超市、品牌连锁等更多城乡零售业态,今年已经签约安吉尔、Lululemon、自然堂、斑马精酿等中大型品牌客户。

如果说,“小门店大模型”面向的是线下零售场景的AI实践,汇通达收购的“认知边界”瞄准的则是电商场景的AI实践。认知边界是专注电商及零售智能化的科技公司,2025年11月18日,汇通达通过股权收购将其收至麾下。

认知边界掌握了阿里系(含淘天)自2019年以来几乎全部零售端数据,并于2025年起接入抖音电商平台数据。这些高频更新的信息包括:各品类销售榜单及趋势变化;商品成交金额、发货地、供应链来源;消费者搜索关键词热度走势;不同客单价区间的人群画像,等等。通过对这些数据的实时监控,可精准判断哪些产品具备爆款潜力,不仅能指导企业进行高效选品,更能反向赋能集团内部管理。

认知边界当前业务不仅包括电商培训咨询、SaaS服务“边界之眼”,还有AI工具“大蝶”,据相关负责人介绍,2026年认知边界将聚焦所有年营收十亿元以上企业,完成全面数字化落地,且AI工具的客群会从电商赛道向实体零售及其他行业延伸。

可见,汇通达通过“AI+数字化+软硬一体化”全面赋能零售的升级,从乡镇到城市、从线下到线上,服务深度、广度都迭代至新的阶段。

03

AI生产力革命时代的竞争壁垒

AI时代,未来趋势已经显现出一些明显特征:

AI Agent加速替代传统SaaS,工具类软件将逐步免费,价值转向AI自主决策与执行;

行业级AI是突破口,通用模型之外,必须在特定场景(如零售、医疗、金融)实现深度落地;

智能终端与边缘计算融合,AI手机、AI Pad、AI车辆、AI电脑将成为新入口,推动软硬一体化发展;

新型组织形态将是“碳基+硅基+钢铁机器人”的模式,人机协同成为新常态。

在这样的趋势背景下,商业体系中不同的角色——科技公司、平台公司、品牌公司,门店店主,甚至每个消费者,都在重新确定自己的位置。

汇通达选择了一条不一样的路径,将全部精力倾注于“场景深耕”,而非算力与通用模型上激烈角逐,其竞争壁垒的核心在于“拼场景、拼数据、拼供应链”。就像汇通达总裁徐秀贤所言:“汇通达发展AI的重点不在研发,重点在应用,要从传统的SaaS赋能转变为AI赋能,深入应用场景,沉淀数据和能力。”

相应地,汇通达探索的是按效果付费的“Skill或Token收费”模式,即AI像员工一样“按劳计酬”,而非传统的SaaS订阅制。这项创新已初见成效:2025年上半年,汇通达实现了AI相关收入超6000万元的“从零到一”的突破。这标志着公司的角色发生了根本性转变——从SaaS工具的提供方,进化为AI数字员工的“生产与运营方”。AI不再是被动等待调用的工具,而是能主动为商家创造价值的“员工”,这才是AI生产力革命的真正内核。

据了解,汇通达已引进一支硅谷归国团队,成员包括清华系博士、美国金融算法专家、英特尔前技术骨干等。

汇通达的实践揭示了一个关键趋势:在AI生产力革命中,最大的赢家可能不是拥有最强算法的公司,而是那些最懂产业、最能将技术转化为产业可用生产力的企业。其通过让AI成为数百万乡镇小店以及零售客户、产业客户触手可及的“同事”,正在悄然改写下沉市场零售的剧本。这场变革的终点,或许是一个更智能、更高效、也更公平的城乡商业新生态。

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FAQ回顾

汇通达的AI产品能为零售商家提供什么帮助?

汇通达推出千橙AI智能体平台、边界BI数据系统等AI产品,可覆盖电商运营、社群营销、商品采购等多场景,提供AI生图、直播助手、智能选品、全链路销售支持等功能,降低商家AI使用成本,提升全流程经营效率,助力实现降本增效和规模增长。

汇通达提出的“小门店大模型”战略是什么?

“小门店大模型”是汇通达的核心AI战略,指将前沿AI能力拆解、适配到下沉市场数字化能力薄弱的小型零售门店的各经营环节中,让小门店以低门槛接入智能工具,向AI时代转型,目前已覆盖乡镇门店、社区零售、品牌连锁等多类零售业态。

AI生产力革命下零售行业有哪些发展趋势?

AI Agent正加速替代功能固化的传统SaaS软件,工具类软件将逐步免费,价值转向AI自主决策与执行;行业级AI垂直场景落地是核心突破口,智能终端与边缘计算深度融合,人机协同将成为零售行业运营新常态。

汇通达AI业务采用什么收费模式?

汇通达AI业务创新采用按效果付费的“Skill或Token收费”模式,即AI像员工一样“按劳计酬”,替代传统的SaaS订阅制,2025年上半年其AI相关收入已突破6000万元,实现从零到一的发展突破。

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