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中国制造网:用AI重构跨境B2B采购 |2025千峰访谈

亿邦动力 2025/11/24 15:38
亿邦动力 2025/11/24 15:38

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中国制造网利用AI技术显著提升跨境B2B采购效率,推动贸易模式变革。

1.核心工具与应用:MIC推出“AI麦可”帮助卖家自动化80%日常运营(如上架商品、生成营销素材),并推出“SourcingAI 2.0”协助买家通过自然语言或设计图纸精准匹配供应商,提升采购效率35%,涵盖需求发布、寻源、比价到订单管理全流程。

2.实践效果:AI消除语言和文化壁垒,实现“人机协同”;买家可高效完成背景调查和资质审核,减少商机流失;供应商通过自动回复商机提升转化率。

3.操作建议:企业可借助这些工具优化采购决策;未来AI将进化为“数字雇员”,自主处理从目标设定到交付全链条。

4.挑战应对:通用大模型存在“幻觉”风险,解决方案包括训练轻量小模型和知识增强技术,确保输出专业可靠。

AI驱动贸易效率提升和创新,为企业品牌建设与产品研发提供新机遇。

1.消费趋势与用户行为:AI工具如“SourcingAI 2.0”基于买家画像,精准洞察需求变化(如使用自然语言描绘需求),促进个性化商机转化,揭示数字时代采购偏好正从关键词搜索转向智能交互。

2.品牌营销与渠道:MIC平台整合在线询盘和通讯系统,实现对话留痕和行为可溯,帮助企业建立结构化数据名片(如通过验厂信息),提升品牌可信度和在线曝光率;AI自动化生成营销素材,降低人力成本40%。

3.产品研发启示:AI支持从模糊需求到精准匹配的供应链优化,品牌可借鉴开发智能客服系统;未来“数字雇员”将代表买家进行多轮谈判,启示产品功能迭代需侧重智能化和定制化。

4.趋势观察:全球贸易信任成本下降,品牌可利用AI工具加速出海布局;MIC的经验显示跨界团队整合(如技术+外贸人才)是成功关键。

AI工具为卖家提供增长机会和风险规避策略,助力效率提升和市场适应。

1.机会提示与需求变化:MIC的“AI麦可”已服务15000家供应商,自动化高达80%运营任务(如商品上架和商机回复),提升转化率;AI消除语言时区障碍,降低中小卖家出海人力成本80%。

2.风险提示:通用大模型有“幻觉”风险,可能导致决策偏差;MIC通过工程化体系预设规则拦截不良输出,卖家可学习建立本地数据验证机制规避风险。

3.学习点与模式创新:SourcingAI支持智能比价和背景审核,卖家可借鉴优化供应链管理;未来AI将进化至谈判阶段,启示卖家加强数字化能力应对全流程自动化。

4.增长市场建议:政策推动数字化筑基(如信息建档),卖家可利用平台集成物流支付体系,一站式闭环交易;面对贸易壁垒消融,及时应用AI工具抢占新市场。

AI优化生产设计需求,为工厂带来数字转型机遇和效率革命。

1.产品生产启示:AI工具如“AI麦可”能自动处理繁杂运营,释放人力专注设计;未来“数字雇员”可跟踪生产物流环节,启示工厂需整合智能化生产线(如基于图纸需求自动匹配)。

2.商业机会:MIC的数据体系支持可搜索产品名片(如第三方验厂信息),工厂可加入提升曝光率;AI采购提升35%效率,带来新订单机会,同时降低沟通成本30%。

3.数字推进建议:中小工厂可学习MIC的轻量化小模型应用(针对意图分类),降低延迟成本;平台一站式物流支付支持启示工厂整合线上交付体系,加速电商转型。

4.风险对策:通用模型鸿沟可能导致响应延迟,工厂可建立结构化知识库(如产品体系数据)增强AI可靠性。

AI解决行业痛点,开创服务新模式和技术迭代前景。

1.行业趋势:AI正从辅助工具进化为核心生产力,MIC的Agent概念揭示未来“数字雇员”将自主决策执行采购全流程(如从寻源到交付),重塑服务链。

2.新技术与解决方案:针对客户痛点(如供应商筛选难),MIC开发“SourcingAI 2.0”覆盖需求发布到订单管理;训练轻量小模型和知识增强技术解决通用模型幻觉风险。

3.客户需求洞察:买家面临信息繁杂等难题,服务可借鉴AI精准匹配(基于自然语言);MIC已提升效率35%,启示服务商开发集成系统(如物流跟踪)。

4.创新路径:数据安全前提下(如平台三十年合规数据),跨界团队模式(技术+专家)启示服务商构建高效数字方案。

平台通过AI满足用户需求,优化招商和运营管理。

1.需求与问题解决:买家和供应商痛点(如语言壁垒、人力成本)被MIC的AI工具解决(“AI麦可”自动化80%运营,“SourcingAI 2.0”提升效率35%),通过精准匹配和智能审核降低信任成本。

2.平台最新做法:SourcingAI 2.0升级至“更优决策”,未来扩展至桌面移动全场景和多语言服务;整合物流支付体系实现交易后线上闭环,支持跨境结算。

3.招商与运营:AI工具吸引供应商超15000家,启示平台强化数据建档(如验厂信息)以精准匹配;MIC的工程化体系预设规则规避风险,可应用于订单纠纷处理。

4.风向规避建议:通用模型鸿沟通过轻量模型和知识约束应对,确保响应速度;平台需结合专家团队优化生态闭环。

AI引发产业新动向和挑战,提供商业模式启示和政策思考。

1.新动向:AI正重构贸易连接,MIC的Agent进化代表“数字雇员”概念兴起,将从辅助工具变为自主执行核心任务(如谈判交付),标志产业互联网新周期。

2.新问题与建议:通用大模型“幻觉”和行业鸿沟难题,MIC通过训练专用小模型和知识增强解决;启示政策需强化数据安全框架(如隐私保障)。

3.商业模式演变:MIC从信息展示到全链路服务平台,数字化筑基(如结构化数据沉淀)转向智能化探索;未来AI深化采购赋能(如趋势洞察功能)。

4.法规启示:跨国贸易效率天花板被击穿,建议参考MIC经验建立跨界团队模型(技术+外贸),支持产业可信AI发展。

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Quick Summary

Made-in-China.com leverages AI technology to significantly enhance cross-border B2B procurement efficiency and drive transformation in trade models.

1. Core Tools & Applications: MIC launched "AI Mike" to help sellers automate 80% of daily operations (e.g., product listing, marketing material generation) and "SourcingAI 2.0" to assist buyers in precisely matching suppliers via natural language or design drawings, boosting procurement efficiency by 35%. It covers the entire process from demand publishing, sourcing, and price comparison to order management.

2. Practical Impact: AI eliminates language and cultural barriers, enabling human-machine collaboration. Buyers can efficiently conduct background checks and qualification audits, reducing missed business opportunities. Suppliers improve conversion rates through automated responses to inquiries.

3. Operational Suggestions: Companies can utilize these tools to optimize procurement decisions. In the future, AI will evolve into "digital employees," autonomously handling the entire chain from goal setting to delivery.

4. Challenge Response: General large language models carry "hallucination" risks. Solutions include training lightweight small models and employing knowledge enhancement techniques to ensure professional and reliable outputs.

AI-driven trade efficiency improvements and innovation present new opportunities for brand building and product development.

1. Consumer Trends & User Behavior: AI tools like "SourcingAI 2.0" utilize buyer profiles to precisely identify demand shifts (e.g., using natural language descriptions), fostering personalized opportunity conversion. This reveals a shift in digital-era procurement preferences from keyword searches to intelligent interaction.

2. Brand Marketing & Channels: The MIC platform integrates online inquiries and communication systems, enabling conversation tracking and behavior traceability. This helps companies build structured data profiles (e.g., via factory audit information), enhancing brand credibility and online exposure. AI automation in marketing material generation reduces labor costs by 40%.

3. Product Development Insights: AI facilitates supply chain optimization from vague requirements to precise matching. Brands can draw inspiration for developing intelligent customer service systems. Future "digital employees" will negotiate on behalf of buyers, indicating that product iteration should prioritize intelligence and customization.

4. Trend Observation: Declining global trade trust costs enable brands to accelerate international expansion using AI tools. MIC's experience highlights that cross-functional team integration (e.g., tech + foreign trade talent) is key to success.

AI tools offer sellers growth opportunities and risk mitigation strategies, enhancing efficiency and market adaptability.

1. Opportunities & Demand Shifts: MIC's "AI Mike" already serves 15,000 suppliers, automating up to 80% of operational tasks (e.g., product listing, inquiry response), thereby boosting conversion rates. AI removes language and time zone barriers, reducing labor costs for SMEs expanding overseas by 80%.

2. Risk Alert: General large language models carry "hallucination" risks, potentially leading to decision-making errors. MIC employs an engineering system with preset rules to intercept undesirable outputs. Sellers can learn to establish local data verification mechanisms to mitigate risks.

3. Learning Points & Model Innovation: SourcingAI supports intelligent price comparison and background checks, offering insights for sellers to optimize supply chain management. The future evolution of AI into negotiation phases suggests sellers should strengthen digital capabilities to adapt to full-process automation.

4. Growth Market Advice: Policies promoting digital infrastructure (e.g., information archiving) enable sellers to leverage integrated platform logistics and payment systems for one-stop closed-loop transactions. With diminishing trade barriers, timely adoption of AI tools is crucial for capturing new markets.

AI optimizes production and design requirements, bringing digital transformation opportunities and an efficiency revolution to factories.

1. Production Insights: AI tools like "AI Mike" can automatically handle complex operations, freeing up human resources to focus on design. Future "digital employees" will track production and logistics, indicating the need for factories to integrate intelligent production lines (e.g., automatic matching based on drawing requirements).

2. Business Opportunities: MIC's data system supports searchable product profiles (e.g., third-party factory audit information), allowing factories to enhance exposure. AI procurement improves efficiency by 35%, generating new order opportunities while reducing communication costs by 30%.

3. Digital Advancement Suggestions: Small and medium-sized factories can adopt MIC's approach of using lightweight small models (e.g., for intent classification) to reduce latency costs. The platform's integrated logistics and payment support highlights the need for factories to build online delivery systems, accelerating e-commerce transformation.

4. Risk Countermeasures: The gap in general models may cause response delays. Factories can establish structured knowledge bases (e.g., product system data) to enhance AI reliability.

AI addresses industry pain points, creating new service models and prospects for technological iteration.

1. Industry Trend: AI is evolving from an auxiliary tool to a core productivity driver. MIC's Agent concept indicates future "digital employees" will autonomously execute the entire procurement process (from sourcing to delivery), reshaping the service chain.

2. New Technologies & Solutions: Addressing client pain points (e.g., difficulty in supplier screening), MIC developed "SourcingAI 2.0" covering demand publishing to order management. Training lightweight small models and using knowledge enhancement techniques mitigate hallucination risks of general models.

3. Client Needs Insight: Buyers face challenges like information overload. Services can learn from AI's precise matching (based on natural language). MIC's 35% efficiency improvement suggests service providers should develop integrated systems (e.g., logistics tracking).

4. Innovation Path: Under the premise of data security (e.g., the platform's 30 years of compliant data), the cross-functional team model (tech + experts) offers insights for service providers to build efficient digital solutions.

The platform utilizes AI to meet user needs, optimizing merchant acquisition and operational management.

1. Needs & Problem Solving: Pain points for buyers and suppliers (e.g., language barriers, labor costs) are addressed by MIC's AI tools ("AI Mike" automates 80% of operations; "SourcingAI 2.0" improves efficiency by 35%). Precise matching and intelligent audits reduce trust costs.

2. Latest Platform Practices: SourcingAI 2.0 has been upgraded for "better decision-making," with future expansion to desktop/mobile scenarios and multilingual services. Integrating logistics and payment systems enables post-transaction online closed loops, supporting cross-border settlements.

3. Merchant Acquisition & Operations: AI tools have attracted over 15,000 suppliers, suggesting platforms should enhance data archiving (e.g., factory audit info) for precise matching. MIC's engineered system with preset rules for risk mitigation can be applied to order dispute resolution.

4. Risk Avoidance Advice: Address the general model gap using lightweight models and knowledge constraints to ensure response speed. Platforms need to collaborate with expert teams to optimize the ecosystem's closed loop.

AI triggers new industry trends and challenges, offering insights into business models and policy considerations.

1. New Trends: AI is reshaping trade connections. The evolution of MIC's Agent represents the rise of the "digital employee" concept, transitioning from an auxiliary tool to autonomously executing core tasks (e.g., negotiation, delivery), marking a new cycle in industrial internet development.

2. New Problems & Suggestions: Challenges like general model "hallucinations" and industry gaps are addressed by MIC through training specialized small models and knowledge enhancement. This suggests policies need to strengthen data security frameworks (e.g., privacy protection).

3. Business Model Evolution: MIC has shifted from an information display platform to a full-link service platform, moving from digital foundation building (e.g., structured data accumulation) to intelligent exploration. Future AI will deepen procurement empowerment (e.g., trend insight functions).

4. Regulatory Implications: The efficiency ceiling of cross-border trade is being broken. It is advisable to reference MIC's experience in establishing cross-functional team models (tech + foreign trade) to support the development of trustworthy AI in industry.

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】曾几何时,海外买家为寻找合适供应商需在海量商品中“大海捞针”,中国中小企业在出海时困于语言壁垒与人力成本;而如今,自然语言指令、设计图纸甚至模糊需求,都能被AI精准“翻译”为高效匹配——这场由技术驱动的采购革命,正重新定义全球贸易的连接方式。

作为中国最早一批布局垂类AI的跨境B2B平台,中国制造网(MIC国际站)在2017年组建AI团队,2023年推出“AI麦可”、2024年迭代“SourcingAI 2.0”。AI为买家带来了35%的采购效率提升,也帮卖家实现了80%的日常运营自动化。

焦点科技副总裁、中国制造网总裁曹睿表示,AI正从辅助工具进化为重塑产业的核心生产力,而Agent将从“助手”进化为“数字雇员”。“AI+外贸”的跨界组合可以打破通用大模型的“幻觉”陷阱,而全球贸易的“信任成本”与“效率天花板”,正被技术悄然击穿。

受访公司:焦点科技股份有限公司

受访人及职务:曹睿 焦点科技副总裁 中国制造网总裁

所属行业:商贸零售

1.亿邦动力:大模型与AI技术的迭代发展,给贵公司所在的行业带来哪些影响?

曹睿:首先,采购和匹配效率发生了革命性的变化。过去买家通过关键词搜索,在海量商品中筛选,并需要投入大量精力对供应商进行背调,过程繁琐、耗时,且结果不一定精准。现在买家可以直接用自然语言、产品图片、甚至设计图纸描绘复杂需求,AI能精准理解并匹配最合适的供应商,并协助完成背景调查、智能比价、资质审核等环节,帮助提升买家的整体采购效率,以及供应商的广告ROI。

其次是出海贸易壁垒的消融。过去中国中小企业走出去,主要被两大难题卡住:一是语言、文化和时区带来的沟通障碍,很多商机其实是在交流中流失的;二是从店铺运营到全球营销,都需要大量人力资源投入。而现在,出海已经进入“人机协同”的新阶段。在前端,AI基于对供应商知识库的深度学习与买家画像的精准洞察,自主编排高度个性化的商机回复,促进买家留资转化。在后台,它能批量处理商品上架/修改、营销素材生成等繁杂事务,将企业从高昂的人力成本中解放出来。

2.亿邦动力:贵公司在哪些业务环节中使用AI及大模型?取得了怎样的效果?

曹睿:早在2017年,中国制造网(以下简称:MIC国际站)便组建了AI研发团队,坚定布局垂类AI应用。2023年推出AI外贸助手“AI麦可”,2024年推出SourcingAI1.0,构建了“AI麦可(服务卖家)+SourcingAI(服务买家)”的智能协同布局,实现服务买卖双方的全流程AI赋能生态闭环。

SourcingAI在今年10月份重磅升级为全新2.0版本,从单纯的“找到所需”升级为“更优决策”。我们深入洞察跨国采购中信息繁杂、供应商筛选难、风险不可控等核心痛点,通过人工智能技术覆盖采购全流程——包括需求发布、寻源匹配、智能比价、资质审核及订单管理等关键环节。MIC国际站用户体验中心数据显示,使用SourcingAI 2.0后,买家的整体采购效率可提升35%。

AI麦可,从传统的“助手”角色也逐步升级为能自动规划并执行整个外贸工作流的智能体(AI Agent),帮助供应商自动完成高达80% 的日常运营工作。截至今年9月30日,AI麦可已累计服务中国供应商超15000家,有效提升了企业工作效率与商机转化率。

3.亿邦动力:请介绍下与AI相关的团队搭建和投入。

曹睿:我们依托自主构建的强大算力基础设施,并在充分保障数据安全与隐私的前提下,基于平台近三十年来形成的合规贸易数据体系,自主研发了专属于跨境B2B领域的行业智能体。这一切的背后,关键就在于我们的团队,顶尖的技术人才和资深的国际贸易专家融合在了一起,这是一个真正的跨界组合,也是我们最大的优势所在。简单说,就是我们有技术、有数据体系,也有一支同时懂AI和懂外贸的团队,所以能够在确保数据安全的前提下,为买家和供应商提供更高效、准确的数字化服务。

4.亿邦动力:AI与产业的结合过程中遇到过哪些难题?将如何克服?

曹睿:难题之一是通用大模型与垂直领域的“鸿沟”。通用模型虽知识广博,却缺乏深度行业经验,存在“幻觉”风险,且难以满足产业对响应速度与成本等严苛要求。

我们现在尝试通过两种路径来克服:

① 训练“小模型”:针对工作流中边界清晰、高频的执行节点(如SourcingAI中的意图分类),我们训练并部署专用的轻量化小模型,以极低的延迟和成本实现顶尖的精准度。最终,我们将这些独立的模型模块进行灵活编排,构建出能高效解决复杂任务的完整工作流。

② 对大模型实施知识增强和约束:比如AI商机接待等复杂功能,在确保信息安全的前提下,让模型学习基于供应商的授权信息和产品知识库,使其输出既专业又符合企业风格。同时,我们建立了一套强大的工程化体系,通过预设规则对潜在的不良输出进行系统性的修正或拦截,以此确保AI交互的可靠性。

5.亿邦动力:在您看来,产业互联网第一个创新周期(2019-2024)的主要特征是什么,对新周期有什么期待?

曹睿:“数字化筑基”与初步“智能化探索”。MIC国际站从一个主要提供信息展示的平台,完成“数字化筑基”,逐步演进为一个全链路外贸综合服务平台。在此基础上,平台于2023年推出AI外贸助手“麦可”,向初步 “智能化”迈进。

●商品与商家的“数字化建档”

从简单的图文信息展示,升级为结构化的数据沉淀。通过第三方实地验厂、标准化信息引导填报等方式,将复杂的生产能力和产品体系系统性地转化为可搜索、可筛选、可验证的线上数据名片,奠定精准匹配的底层基础。

●沟通与匹配的“数字化初探”

将原本依赖邮件、电话的线下沟通流程,逐步迁移至平台集成化询盘与即时通讯系统,实现对话留痕、行为可溯。这不仅提升了买卖双方对接效率,也为供应商服务考评、订单纠纷判责与运营优化提供了关键数据依据。2023年“AI麦可”的推出,进一步将该环节从“沟通数字化”向“沟通智能化”迈进,帮助供应商解决跨文化、跨语种、跨时区的沟通难题。

●交易后流程的“线上化”

积极整合跨境物流服务商与金融机构,构建涵盖在线支付、跨境结算、线上物流下单与运单跟踪的一站式支持体系,初步实现从交易到交付的关键环节线上闭环。

如果说2025年是AI应用的元年,那么在新周期中,AI正从辅助工具进化为重塑产业的核心生产力。

SourcingAI 2.0已经帮助买家从“找到所需”升级至“更优决策”。后续,Sourcing AI 2.0将逐步实现桌面端与移动端的全场景覆盖,并支持更多语言服务,满足全球买家随时随地的采购需求。同时,我们还将逐步迭代AI谈判、市场趋势洞察等延伸功能,进一步深化对采购全流程的赋能,让全球贸易连接更精准、高效、可信。

我们能够清晰地预见,不久后的某一天,采购Agent就会变成一个自主决策、自主执行的“数字雇员”。当一位买家提出一个商业目标,这个Agent可以制定执行方案、全网寻源、进行多维度的供应商筛选与深度背调、代表买家发起多轮初步商务谈判,并生成完整采购方案,等待买家最终决策。一旦决策通过,它将自动完成下单、跟进生产与物流、处理报关,直至最终交付的全流程。


文章来源:亿邦动力

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