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当AI开始“懂生意”:1%的产业带玩家重写游戏规则

亿邦智库 2026-01-14 16:07
亿邦智库 2026/01/14 16:07

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文章揭示了AI如何深刻改变产业带游戏规则,提供了关键行动建议。AI不再是辅助工具,而是成为智能中枢,驱动产业转型升级。2025年中国产业带进入AI原生时代,意味着依赖经验的传统模式被AI数据决策取代。

1.重点信息:中国产业带告别传统增长,进入AI2B阶段,报告指出只有效率、合规与确定性达标的1%商家能穿越周期。

2.实操干货:真实案例展示了AI应用方法,如深圳3C厂商用AI发现用户痛点后研发新品,实现38天成交1100万;东莞创业者用AI找商机、生成内容,15天登顶新晋榜;芜湖鞋厂用AI客服、买手、设计,提升人效,6人团队年销1.5亿。

3.行动倡议:建议信仰AI,深度融入流程;做足确定性,提升品控履约服务;布局双循环,打通国内国际市场。这些干货帮助读者理解如何抓住AI红利避免被淘汰。

文章探讨了品牌在AI时代的转型机遇,聚焦品牌营销和消费趋势。AI驱动品牌从代工角色升级为品牌主,通过C2M和DTC模式掌握定价权与价值链主导权。同时,消费趋势变化要求品牌精准响应用户需求。

1.品牌渠道建设:工厂角色从代工者蜕变为品牌主,借助C2M反向定制和DTC模式直接主导价值链,这在产业带规则重写中体现。

2.消费趋势与用户行为:买家涌入家居百货、美妆护肤等行业,需求刚性且与消费升级、场景专业化交织;三大钱景赛道增长迅猛:即时零售交易额+125%、定制生意规模+61%、跨境生意日活买家+44%,显示需求向快、专、近演化。

3.产品研发与定价策略:深圳3C厂商案例展示了如何用AI洞察用户痛点(如粘不牢、价格高),避开低端竞争,研发出新品,实现价值翻身;品牌需基于AI信用分重新定义好商品,强化品质确定性,避免价格战转向价值战。这些干货助力品牌商抓住AI红利优化策略。

文章解读了政策、增长市场和风险机会,为卖家提供实用指南。政策支持AI与产业带深度融合,宏观环境转向有效供给,卖家需关注需求变化和可学习案例。增长市场集中在效率高、确定性强的领域,机会与风险并重。

1.政策解读与扶持政策:政策层面鼓励人工智能+产业带,聚焦内需整治内卷、推动柔性出海;宏观经济进入有效供给时代,无效产能退场,卖家应顺势而为。

2.增长市场与消费需求变化:订单向效率×确定性双优产业带集中,如义乌和广州;三大增长赛道:即时零售倒逼供应链本地化、定制生意依赖场景理解、跨境生意胜在敏捷合规;消费需求转向场景响应,买家涌入刚性需求行业如服装鞋帽。

3.机会提示与可学习点:风险提示:只有1%商家能穿越周期,卖家需提升效率合规;机会提示:学习深圳厂商用AI接小订单、东莞创业者用AI降成本、芜湖团队用AI倍增人效;最新商业模式如柔性快反和AI共创,帮助卖家突破人效天花板。这些干货指导卖家应对变化。

文章聚焦产品生产设计和数字化启示,为工厂提供商业机会。AI驱动工厂从经验决策转向数据决策,提升生产效率和响应能力。同时,新赛道涌现带来机遇,工厂需推进数字化。

1.产品生产和设计需求:AI原生模式要求工厂以用户实时数据为中心,如深圳3C厂商用AI工具洞察痛点后优化设计,历经39次修改推出新品;芜湖鞋厂用AI设计师完成换色和场景图生成,实现零成本创意。

2.商业机会:订单流向效率高产业带,如义乌的智能排产和柔性快反;三大钱景赛道:即时零售需求+125%推动本地化供应链、定制生意+61%依赖精准解决方案、跨境生意+44%要求敏捷交付,工厂可切入这些领域。

3.推进数字化和电商启示:案例启示:东莞创业者用AI操盘电商,省下外包成本;工厂应信仰AI,让产线听得懂需求、看得见订单;做足确定性,在品控履约上构筑硬实力,以应对竞争核心从产品供给转向场景响应。这些干货帮助工厂抓住AI转型红利。

文章分析了行业趋势、技术和解决方案,服务商可从中识别客户痛点和创新方向。AI成为新基础设施,解决B端产业复杂性问题,服务商需关注新技术应用。

1.行业发展趋势:产业带迈向AI原生时代,AI从锦上添花变为智能中枢,重塑供应链;报告预言只有1%供给能穿越周期,趋势明朗。

2.新技术与客户痛点:痛点包括供给碎片化、需求非标化、交易决策链冗长、经验难以沉淀;新技术如AI大模型用于产品信用分严选,AI工具在案例中实现智能排产、数据选品、一键生成内容。

3.解决方案:针对痛点,AI驱动精准决策,如深圳厂商用AI共创柔性模式接小订单;芜湖鞋厂用AI外挂解放人力,提升响应率;服务商可提供类似AI整合方案,帮助客户在效率、合规与确定性上达标。这些干货为服务商提供创新灵感。

文章讨论了平台角色和运营管理,平台商可借鉴最新做法规避风险。商业需求向效率高平台集中,平台需创新招商和管理策略。

1.商业对平台的需求和问题:订单加速向效率×确定性双优产业带集中,如义乌和广州;问题包括供给碎片化和需求非标化,平台需提供解决方案。

2.平台的最新做法与招商运营:1688平台形成数字化活力网络,东高、中快、西广格局;平台用AI大模型重新定义好商品,年度好物榜基于产品力、品质确定性等多维信用分严选,替代传统渠道;招商应吸引如案例中的高效商家,运营管理需支持柔性出海和双循环布局。

3.风向规避:风险提示:市场转向价值战,无效产能退场;平台应鼓励商家做足确定性,避免内卷,聚焦真实价值;案例显示平台在AI应用中扮演关键连接角色,帮助商家提升响应率。这些干货指导平台商优化策略。

文章探讨了产业动向和政策启示,研究者可分析新问题和商业模式。AI引发产业范式转移,政策支持深度融合,商业模式创新显著。

1.产业新动向与新问题:产业带从数字化迈向智能化,AI原生本质是范式转移;新问题包括产业复杂度攀升(如供给碎片化、需求非标化),只有1%商家能持续达标,研究者需探讨如何应对。

2.政策法规建议和启示:政策鼓励人工智能+产业带,聚焦内需整治内卷、推动柔性出海;宏观经济启示:有效供给时代要求真实价值胜出,研究者可建议强化合规与确定性。

3.商业模式:C2M反向定制和DTC模式让工厂蜕变为品牌主,掌握定价权;案例如深圳厂商的柔性共创模式、东莞创业者的AI操盘模式、芜湖团队的AI倍增模式,展示了新兴商业模式如何打破经验壁垒。这些干货为研究者提供深度分析素材。

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Quick Summary

The article reveals how AI is fundamentally reshaping the rules of the game for industrial clusters, offering key actionable advice. AI is no longer just an auxiliary tool but has become an intelligent core driving industrial transformation and upgrading. By 2025, China's industrial clusters will enter an AI-native era, meaning traditional experience-dependent models are being replaced by AI-driven data decision-making.

1. Key Insights: China's industrial clusters are moving beyond traditional growth into an AI2B phase. The report indicates that only the top 1% of merchants who meet standards for efficiency, compliance, and certainty can thrive through economic cycles.

2. Practical Cases: Real-world examples demonstrate AI applications: a Shenzhen 3C manufacturer used AI to identify user pain points, developed a new product, and achieved 11 million RMB in sales within 38 days; a Dongguan entrepreneur used AI to find business opportunities and generate content, topping the newcomer chart in 15 days; a Wuhu shoe factory leveraged AI for customer service, buying, and design, boosting per-capita efficiency with a 6-person team generating 150 million RMB in annual sales.

3. Actionable Recommendations: The article advises fully embracing AI and deeply integrating it into workflows; ensuring operational certainty by enhancing quality control, fulfillment, and service; and strategically planning for dual circulation by connecting domestic and international markets. These insights help readers understand how to seize AI opportunities and avoid obsolescence.

The article explores transformation opportunities for brands in the AI era, focusing on brand marketing and consumer trends. AI empowers brands to evolve from OEM roles to brand owners, gaining pricing power and value chain dominance through C2M and DTC models. Simultaneously, shifting consumer trends demand precise responses to user needs.

1. Brand and Channel Development: Factories are transforming from OEMs to brand owners, leveraging C2M reverse customization and DTC models to directly control the value chain, reflecting a rewriting of industrial cluster rules.

2. Consumer Trends & User Behavior: Buyers are flocking to sectors like home goods, beauty, and skincare, driven by rigid demand intertwined with consumption upgrades and scenario specialization. Three high-growth sectors are emerging: instant retail (transaction volume +125%), customization (market size +61%), and cross-border e-commerce (daily active buyers +44%), indicating a shift towards speed, specialization, and proximity.

3. Product R&D & Pricing Strategy: The Shenzhen 3C case shows how AI can identify user pain points (e.g., poor adhesion, high price) to avoid low-end competition and develop successful new products. Brands must redefine 'good products' based on AI-driven credit scores, emphasizing quality certainty to shift from price wars to value-based competition. These insights help brands optimize strategies to capture AI dividends.

The article interprets policies, growth markets, and risk opportunities, providing a practical guide for sellers. Policies support deep AI-industrial cluster integration, with the macro-environment shifting towards effective supply. Sellers must monitor demand changes and learn from applicable case studies. Growth is concentrated in high-efficiency, high-certainty areas, presenting both opportunity and risk.

1. Policy Interpretation & Support: Policies encourage 'AI + Industrial Clusters,' focusing on stimulating domestic demand, curbing internal competition, and promoting agile globalization. The macroeconomy is entering an era of effective supply, with inefficient capacity phasing out; sellers should adapt accordingly.

2. Growth Markets & Consumer Demand: Orders are concentrating in clusters excelling in both efficiency and certainty, such as Yiwu and Guangzhou. Three key growth sectors are: instant retail (driving localized supply chains), customization (relying on scenario understanding), and cross-border trade (winning through agility and compliance). Consumer demand is shifting towards scenario-specific responses, with buyers涌入 rigid demand industries like apparel and footwear.

3. Opportunities & Learnings: Risk Warning: Only 1% of merchants can sustain through cycles; sellers must enhance efficiency and compliance. Opportunity Alert: Learn from the Shenzhen manufacturer using AI for small orders, the Dongguan entrepreneur using AI to cut costs, and the Wuhu team using AI to multiply human efficiency. New business models like flexible rapid response and AI co-creation help sellers break through efficiency ceilings. This guidance helps sellers navigate changes.

The article focuses on product production, design, and digitalization insights, highlighting commercial opportunities for factories. AI drives the shift from experience-based to data-driven decision-making, enhancing production efficiency and responsiveness. Emerging sectors present new opportunities, necessitating a push towards digitalization.

1. Product Production & Design Needs: AI-native models require factories to center on real-time user data. For example, a Shenzhen 3C manufacturer used AI tools to gain insights, optimized designs through 39 iterations, and launched a successful new product. A Wuhu shoe factory used an AI designer for color changes and scene image generation, enabling zero-cost creativity.

2. Commercial Opportunities: Orders flow to high-efficiency clusters like Yiwu, known for intelligent production scheduling and flexible fast response. Three promising sectors are: instant retail (demand +125%, pushing localized supply chains), customization (market size +61%, reliant on precise solutions), and cross-border trade (daily active buyers +44%, requiring agile delivery). Factories can enter these fields.

3. Advancing Digitalization & E-commerce Insights: Case studies show a Dongguan entrepreneur using AI to manage e-commerce, saving on outsourcing costs. Factories should embrace AI to make production lines 'understand' demand and 'see' orders. Building certainty through robust quality control and fulfillment is crucial as competition shifts from product supply to scenario response. These insights help factories capture AI transformation dividends.

The article analyzes industry trends, technologies, and solutions, helping service providers identify client pain points and innovation directions. AI is becoming new infrastructure for solving complex B2B industrial problems, requiring service providers to focus on new technology applications.

1. Industry Development Trends: Industrial clusters are moving towards an AI-native era, where AI evolves from a nice-to-have to an intelligent core reshaping supply chains. Reports predict only 1% of supply capacity will sustainably meet new standards, making the trend clear.

2. New Technologies & Client Pain Points: Key pain points include fragmented supply, non-standardized demand, lengthy transaction decision chains, and difficulty in codifying experience. New technologies like large AI models are used for product credit scoring and strict selection; AI tools in case studies enable intelligent production scheduling, data-driven product selection, and one-click content generation.

3. Solutions: AI drives precise decision-making to address pain points. Examples include the Shenzhen manufacturer's AI-co-created flexible model for small orders, and the Wuhu shoe factory's use of AI 'add-ons' to free up human resources and improve response rates. Service providers can offer integrated AI solutions to help clients meet standards for efficiency, compliance, and certainty. These insights provide inspiration for innovation.

The article discusses the evolving role and operational management of platforms, offering latest practices for platform operators to mitigate risks. Commercial demand is concentrating on high-efficiency platforms, necessitating innovative merchant recruitment and management strategies.

1. Commercial Demands & Platform Challenges: Orders are rapidly concentrating in clusters like Yiwu and Guangzhou that excel in both efficiency and certainty. Challenges include fragmented supply and non-standardized demand, requiring platforms to provide solutions.

2. Latest Platform Practices & Merchant Management: Platforms like 1688 are forming digital活力 networks with an 'East-High-Tech, Central-Fast, West-Wide' pattern. They use AI models to redefine 'good products,' with annual best-seller rankings based on multi-dimensional credit scores (product power, quality certainty) replacing traditional channels. Recruitment should target high-efficiency merchants like those in the case studies, while operations must support agile globalization and dual-circulation strategies.

3. Risk Mitigation & Strategic Direction: Risk Warning: The market is shifting towards value-based competition, with inefficient capacity exiting. Platforms should encourage merchants to build certainty, avoid internal卷 (involution), and focus on real value. Cases show platforms play a key connective role in AI applications, helping merchants improve response rates. This guidance helps platform operators optimize strategy.

The article examines industrial movements and policy implications, providing researchers with new questions and business models for analysis. AI is triggering a paradigm shift in industries, supported by policies encouraging deep integration, with significant business model innovation.

1. New Industrial Movements & Questions: Industrial clusters are transitioning from digitalization to intelligentization; the AI-native essence represents a paradigm shift. New research questions arise from increasing industrial complexity (e.g., supply fragmentation, non-standardized demand), with only 1% of merchants able to consistently meet standards, requiring investigation into coping mechanisms.

2. Policy Recommendations & Implications: Policies encourage 'AI + Industrial Clusters,' focusing on stimulating domestic demand, curbing involution, and promoting agile globalization. Macroeconomic implication: The era of effective supply demands that real value wins out; researchers can recommend strengthening compliance and certainty.

3. Business Models: C2M reverse customization and DTC models enable factories to transform into brand owners, mastering pricing power. Cases like the Shenzhen manufacturer's flexible co-creation model, the Dongguan entrepreneur's AI-driven operation model, and the Wuhu team's AI-amplified efficiency model demonstrate how emerging business models break down experience barriers. These insights provide material for in-depth analysis.

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】2025年,中国产业带站在了历史性的转折点上。亿邦智库联合1688发布的《2025中国产业带发展趋势报告》(点击下载完整报告)清晰揭示了一个新的时代信号:中国产业带正在告别依赖经验与规模的传统增长模式,开始迈入“AI 2 B”(人工智能服务企业)新阶段,站在了从数字化到智能化的历史跨越关口。

01

年度关键词:迈向AI原生

“AI 2 B”已成为产业新基础设施。2025年,中国产业带的年度关键词锁定为 “迈向AI原生”。这标志着AI的角色发生了根本性蜕变——它不再是锦上添花的“生意搭子”,而是重塑供应链、重构价值分配规则的“智能中枢”。

这一转变的背后,是产业复杂度的系统性攀升。报告指出了当前B端产业的四大痛点:供给极端碎片化、需求高度非标化、交易决策链冗长、产业经验难以沉淀复用。传统的“经验+规模”驱动模式,在这些复杂性面前已力不从心。

因此,“AI原生”的本质,是一场范式转移。它意味着源头工厂的经营核心,从依赖老板的个人判断和规模成本优势,转向以用户实时数据为中心、由AI驱动精准决策的智能模式。

政策风向与宏观经济环境共同为这一转型铺路。政策层面,“人工智能+”与产业带深度融合成为明确方向,鼓励数贸创新、聚焦内需整治内卷、推动柔性出海。宏观层面,中国经济进入“有效供给”时代,无效产能退场,真实价值胜出,市场从“价格战”转向“价值战”。

产业带的“江湖规矩”也在被重写,工厂角色从“代工者”蜕变为“品牌主”,通过C2M反向定制与DTC模式,直接掌握定价权与价值链主导权。

深刻的行业洗牌正在发生:报告预言,每个细分品类中只有效率、合规与确定性均持续达标的“1%”供给和背后的商家,才能穿越周期,赢得下一程增长。

02

数据说话:订单流向哪里,哪里就是未来

“十四五”期间,我国制造业增加值占全球比重已接近30%,连续15年保持全球第一的规模优势。这一优势,根植于一张梯度清晰、协同高效的全国产业带数字化网络——以1688平台数据为镜,地理格局上,一张 “东高、中快、西广” 的数字化活力网络已然形成。

但AI 2 B真正的主战场,不在CBD与总部大楼,而是发生在轰鸣的工厂与忙碌的档口。以金华(义乌)、广州、深圳等为代表的产业带重镇,是AI应用最活跃的地区,率先进入AI与核心业务深度融合、重构模式的新阶段。这里发生的,已不是“是否用AI”的讨论,而是“如何用AI重塑竞争力”的实践。

订单加速向“效率×确定性”双优产业带集中。市场选择清晰地指向两类产业带:一是如义乌这样的“需求消化中枢”,凭借AI驱动的数据选品、智能排产与柔性快反,高效吞纳海量碎片化需求;二是如广州(女装)、深圳(数码)、汕头(内衣)等在垂直领域依托AI赋能建立“确定性护城河”的专业型高地。

三大“钱景赛道”增长迅猛,揭示了需求端的进化方向:

即时零售(交易额+125%)爆发倒逼供应链本地化与极速响应。定制生意(规模+61%)依赖对细分场景的深度理解与精准解决方案。跨境生意(日活买家+44%)胜在贴近海外终端需求与敏捷合规交付。

买家争相涌入家居百货、美妆护肤、服装鞋帽、户外运动等行业,这些行业呈现出鲜明的共性:需求具有刚性基础,同时与消费升级、场景专业化、情感悦己等趋势深度交织。

这些趋势均意味着,产业带的竞争核心,已从过去的“产品供给”大规模转移至 “场景响应” 。能理解并满足“快、专、近”需求的产业带与工厂,将获得超额的增长红利。

更深远的变化来自“好商品”的重新定义。平台的“年度好物榜”不再以销量论英雄,而是基于AI大模型对产品力、品质确定性、情感设计等多维度的“信用分”进行严选。算法正在代替传统渠道,重新定义价值标准。

03

2026行动指南:信仰AI,做足确定性,布局双循环

趋势明朗,对于身处浪潮中的商家而言,最关心的的问题可能是:如何行动?几个来自一线战场的真实故事或者能为行业带来一些灵感。他们依靠AI,在传统产业实现转型升级,在新兴赛道脱颖而出,让小微团队突破“人效”天花板。

在深圳,一家深陷利润困境的3C配件厂商,借助AI工具洞察到用户“粘不牢、价格高”的真实槽点。他们不再在低端市场血拼,而是死磕研发,历经39次修改推出了“真空磁吸支架”,通过与平台智能工厂共创“28分钟出图,72小时发货”的柔性模式,奇迹般地接住了海量小订单。最终实现38天新品成交额破1100万,全年业绩从百万级跃升至近亿,跨境业务占比超过70%,完成了一场漂亮的“价值翻身仗”。

在东莞,一位毫无电商经验的90后创业者,跨界做起了小众的钛钢饰品。他没有组建庞大团队,而是选择让“懂生意的AI”成为全职操盘手。从“AI找商机”锁定“钛钢鼻钉”这个趋势爆点,到用AI一键生成高转化图文视频,再到靠AI分析数据管理店铺,开店仅15天,他的店铺就冲上了品类新晋榜第一名,点击转化率高达10.12%,每月还省下近万元外包成本。这个故事证明,在AI时代,行业经验的壁垒能够被技术平权打破。

在芜湖,一家只有6个人的老牌鞋厂,面临着小团队典型的人效天花板。老板将客服、买手、设计、运营顾问这些角色,全部接入了“AI外挂”。AI客服实现7×24小时响应,解放了核心人力;AI买手分析趋势,指导开款方向;AI设计师完成换色、场景图生成,实现了“零成本创意”。最终,这个6人小微团队创造了年销售额1.5亿元的业绩,上新效率提升4倍,客服响应率高达97.23%。他们验证了:AI不是来替代人的,而是来倍增人的价值的。

基于这些实践,报告向所有产业带商家发出三项核心行动倡议:

第一,要信仰AI。 真正将AI深度融入生意全流程,让产线听得懂需求、看得见订单、控得住质量。

第二,做足确定性。 在品控、履约、服务上构筑不可替代的硬实力。确定性本身,已成为这个时代最稀缺的竞争力。

第三,布局双循环。 用一套AI驱动的系统,同时打通国内与国际市场,拥抱无边界生意。

在可预见的未来,AI将越来越“懂生意”,越来越“出人效”。这场进化没有旁观席,而对于所有参与者而言,唯一不变的行动准则就是:拥抱变化,信仰技术,在效率、合规与确定性的道路上,做那个被严选出来的“1%”。

文章来源:亿邦动力

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