SummerLi

十二年,我走过了一件消费品的一生。 现在,我正用 AI 把这段旅程重写一遍。 Twelve years, walking the whole life of a consumer product. Now I'm rewriting that journey with AI.

我是 Summer,一个持续探索技术如何重塑消费产业的 AI builder。
从线下零售、品牌商品、电商数据到 AI 智能化经历,一件零售商品从设计图纸到工厂流水线,从货架灯光、电商商品详情页,到具体消费者手里——一件消费品要走的路,我每一段都走过。
现在我正在做:探寻 AI 在垂直行业(服装)里能产生十倍以上效率的新路径。欢迎同行(服装商家、AI builder)交流。
I'm Summer, an AI builder who keeps exploring how technology reshapes the consumer industry.
From offline retail, brand merchandising and commerce data to AI — from design sketches to factory lines, from the light of a store shelf to a product page to someone's hands, I've walked every step of the road a retail product travels.
What I'm doing now: finding new paths where AI delivers a 10x-plus gain in a vertical industry — fashion. Fellow travelers — fashion merchants and AI builders — welcome to connect.

"Believing that the dots will connect down the road will give you the confidence to follow your heart." — Steve Jobs

现在Now
AI

大厂 AI 应用产品负责人 | 狂热的 AI builder | 重度 AI 使用者AI-application product lead at a major tech company | obsessive AI builder | heavy AI user

我正在实践和总结 AI 如何重构消费产业。I'm practicing and distilling how AI rebuilds consumer industries.

通过观察、调研、参与不同环节的 AI 应用落地,我不断在寻找 AI 放进真实商业链路后、能产生十倍效率的最佳实践和创新工作流。欢迎服装行业、AI 应用实践的商家、同行找我交流,我也乐意提供咨询和建议。Through observing, researching and taking part in AI deployment across different stages, I keep looking for the best practices and innovative workflows where AI creates a 10x gain inside real commercial chains. Fashion-industry operators, AI practitioners and peers are welcome to reach out — I'm glad to share advice and consulting.

2023 - 现在

阿里巴巴 | 淘宝 · AI 规模化/生产级能力建设、AI 工作流重构Alibaba | Taobao · Production-grade AI capability & AI-workflow rebuild

@产品宣发账号

2023-现在从 23 年年底至今,一直在探索 AI 重构服饰行业全链路,推动 AI 能力从业务验证到产品化商业落地。2023-NowSince late 2023, exploring how AI rebuilds the fashion industry's full value chain — pushing AI capabilities from business validation to productized commercialization.

先以 AI 自营业务为实验场,将生成式 AI 能力应用到服饰经营的核心环节,亲自跑通从商机洞察、商品设计、AI 拍摄、快速测款到供应链快反的完整闭环;后进一步打造面向商家服务的 AI Fashion 全链路解决方案(趋势洞察 + AI 设计/拍摄 + AI 辅助快速上新/测款),并尝试商业化闭环验证。
我从传统商品经营视角进一步走向 AI Native 产品建设,深入理解了 AI 应用落地的关键,尝试回答了如何利用 AI 重构消费产业的生产效率,和如何让 B 端客户感受到真实价值并为之买单。
First used a self-operated AI business as the testbed, applying generative AI to the core stages of apparel operations and personally running the complete loop from opportunity insight, product design and AI photography to rapid style-testing and agile supply-chain response; then built a full-chain AI Fashion solution for merchants (trend insight + AI design/photography + AI-assisted rapid launch/testing), with commercial-loop validation.
Moving from a traditional merchandise-operations lens toward AI-native product building, I came to understand the key to AI deployment — and tried to answer how AI can rebuild the productivity of consumer industries, and how to make B2B customers feel real value and pay for it.

2021 - 2023

阿里巴巴 | 新品创新中心 · 数据洞察产品建设与商业化设计Alibaba | New-Product Innovation Center · Data-insight product building & commercialization design

2021-2023在新品创新中心,我利用阿里巴巴生态中的消费者数据帮品牌更好地理解市场、开发新品。2021-2023At the New-Product Innovation Center, I used consumer data across the Alibaba ecosystem to help brands better understand the market and develop new products.
I helped build a data-insight product — cleaning and structuring scattered consumer-behavior and product data across the platform, and leading the build of a knowledge graph serving professional industry analysis, turning hard-to-read consumer trends and market opportunities into something analyzable and actionable.
I drove the business model from traditional traffic-selling toward a service model centered on consumer insight and new-product solutions, raising customer value and commercialization capability.
Here I built my understanding of how data connects consumers, products and business decisions — felt the value of data more deeply, and came to see the gap between e-commerce data and industry product data.

我参与建设数据洞察产品,将分散在平台的消费者行为和商品数据进行清洗、结构化,主导了服务于行业专业分析的知识图谱搭建,让原本难以理解的消费趋势和市场机会变得可分析、可应用
我推动商业模式从传统流量售卖升级为以消费者洞察和新品解决方案为核心的商业服务模式,提高客户价值和产品商业化能力
在这里,我建立了对"数据如何连接消费者、商品和商业决策"的理解,进一步感受了数据价值,也知道了电商数据和产业商品数据的 gap。

2018 - 2021

天猫服饰 商品/流量运营 | 理解电商平台如何通过机制连接消费者与商品Tmall Fashion · Merchandise / traffic operations | how a commerce platform connects consumers and products through mechanisms

2018-2021从平台视角理解商品和消费者之间的关系。2018-2021Understood the relationship between products and consumers from the platform's vantage point.

围绕品类增长负责商品策略和流量运营,通过平台机制提升消费者和商品之间的匹配效率。
我参与设计商品 & 消费者分类分层的运营体系,通过商品分层、流量策略和人群运营,让不同商品获得更精准有效的消费者触达。通过供给组织、前台场景设计及商家协同,推动平台品类增长目标落地。
在这里,我理解了电商平台用户行为和成交增长的本质:通过数据、机制和生态,让需求、供给和商家能力形成正向促进。
Owning merchandise strategy and traffic operations around category growth, I improved matching efficiency between consumers and products through platform mechanisms.
I helped design an operating system for classifying and tiering both products and consumers — using product tiering, traffic strategy and audience operations to give different products more precise, effective consumer reach; and driving category growth targets through supply organization, front-end scenario design and merchant collaboration.
Here I grasped the essence of user behavior and GMV growth on an e-commerce platform: using data, mechanisms and ecosystem to create a virtuous loop among demand, supply and merchant capability.

2016 - 2018

Adidas · 实践品牌运作、商品开发,理解消费者需求Adidas · Brand operations, product development, understanding consumer needs

2016-2018真正了解了品牌管理中 DTC 和经销商管理的逻辑,看懂了一个品牌是如何通过商品和营销形成品牌记忆、消费者心智,带来销售正循环。2016-2018Understood the real logic of brand management across DTC and distributor operations — how a brand builds brand memory and consumer mindshare through product and marketing, driving a virtuous sales cycle.
Here I was fully involved in brand marketing, merchandising and local product creation, and in how seasonal order fairs carry a brand's product story and win over distributors.

我在这完全参与到 brand marketing、merchandising、local product creation,以及如何通过订货会来实现品牌每季商品故事的传递、打动经销商。通过对消费市场的深入走访、调研来理解不同国家和区域的消费者审美和需求,实现精准开发。
在这里我知道了一个品牌是如何去做它的商品企划和品牌故事传递的。
Through deep field visits and research into consumer markets, I came to understand the aesthetics and needs of consumers across countries and regions, enabling precise product development.
This is where I learned how a brand actually does its merchandise planning and brand-story delivery.

2014 - 2016

Decathlon · 深入参与零售经营和供应链Decathlon · Hands-on retail operations & supply chain

2014-2016看懂了货品的完整逻辑:一件商品如何从趋势判断穿过设计、生产、供应链,在正确的时间以正确的价格出现在正确的渠道。2014-2016Understood the full logic of product: how an item travels from trend signal through design, production and supply chain to the right channel, at the right price, at the right time.
Just under three years at Decathlon — starting in sales management for running and walking footwear & apparel at the store level, then moving to the brand center to own selection, pricing, sales forecasting, buy depth and inventory management for ski apparel and shooting-sports products in China.
Here I learned retail's ground truth: the three seconds a shopper hesitates at a shelf, how one promotion reshapes a week of cash flow and inventory turnover, and just how much inventory turnover matters to a retailer.

我在迪卡侬服务不到 3 年时间,从负责线下门店跑步和步行鞋服的销售管理,后去到品牌中心,负责中国区滑雪服装、射击运动商品的选品、定价、销量预测、采购深度与库存管理。
在这里我理解了零售的一线真相:消费者在货架前三秒钟的犹豫,一场促销如何改写一周的现金流和库存周转,以及库存周转对于零售商有多重要。

2009 - 2013

广东外语外贸大学 · 软件工程Guangdong University of Foreign Studies · Software Engineering

2009-2013大学四年做过社联主席,创办过一个公益组织,一直在持续运营,继续帮扶大凉山地区儿童教育,在尼尔森实习过。2009-2013Four years at university: president of the student-societies union, and founder of a public-welfare organization that still runs today, supporting children's education in the Daliangshan region; interned at Nielsen.

身为软件工程专业学生,毕业后选择了服装零售和时尚领域,冥冥之中又进入互联网,真正开始热爱的事业。A software-engineering student who chose fashion retail after graduation — and, as if by fate, circled back into the internet industry, where the work I truly love began.

bio

我现在是一位 AI 产品负责人与业务操盘者,在零售、消费品牌、电商与 AI 创新领域拥有十余年经验。我关注与实践探寻技术如何重塑消费产业,并致力于将 AI 能力转化为真实业务价值。
曾服务于迪卡侬、阿迪达斯和阿里巴巴,积累了从消费者洞察、商品策略、供应链运营到数据产品和 AI 商业化的完整产业经验。
曾参与并负责多个创新业务探索落地。
I'm an AI product leader and business builder with over a decade of experience across retail, consumer brands, e-commerce and AI innovation. I focus on — and practice — finding how technology reshapes the consumer industry, turning AI capabilities into real business value.
I've worked at Decathlon, Adidas and Alibaba, building end-to-end industry experience from consumer insight, merchandise strategy and supply-chain operations to data products and AI commercialization.
I've led and contributed to multiple innovation initiatives from exploration to launch.

近年来,聚焦生成式 AI 在服饰产业中的应用,探索 AI 从趋势洞察、商品设计、内容生产到供应链协同的全链路创新模式。我的核心方向是连接 AI 技术与传统产业场景,打造 AI Native 的产品和商业模式。 In recent years I've focused on applying generative AI in the apparel industry, exploring end-to-end innovation across trend insight, product design, content production and supply-chain collaboration. My core direction: connecting AI technology with traditional industry contexts to build AI-native products and business models.

项目Projects

@ALIBABA · TAOBAO
AI FashionAI Fashion
生成式 AIGenerative AIAgentAgent端到端End-to-end商业化Commercialization
@ALIBABA
趋势中心Trend Center
多源数据Multi-source dataSchemaSchema数据产品Data product中小商家SMB merchants
@ALIBABA · TMALL
风格数字化项目Style-Digitalization Project
标签体系Tag taxonomy机器学习Machine learning视觉算法Visual algorithms品牌诊断Brand diagnosis
@ADIDAS
本土市场商品创新Localized product innovation
LocalizationLocalization商品企划Merchandise planning
@DECATHLON
中国市场供应链优化China-Market Supply-Chain Optimization
供应链优化Supply-chain optimization成本优化Cost optimization
项目背景Background

服饰行业长期存在四个结构性问题:商品开发周期长、市场判断依赖经验、内容生产成本高、供应链响应效率低。以 AI 自营业务作为验证场景,通过实际商品经营积累行业认知和业务数据,探索并跑通 AI 赋能服饰产业的完整链路。Fashion suffers four structural problems: long development cycles, experience-based judgment, high content costs, slow supply response. Used a self-operated AI business as the validation scenario to run the complete AI-powered pipeline.

1参与自研 SD 和 DiT 架构下的生图模型,建设设计和拍摄产业级别的 schema。建设评测体系和实现模型评估、验收和迭代方向确认。Contributed to in-house image-generation models on SD and DiT architectures, building industry-grade schemas for design and photography. Built the evaluation system for model assessment, acceptance and iteration-direction decisions.
2通过视觉算法解析爆款和潜力款特征,识别潜在市场机会。AI 辅助设计,提升设计效率,用 AI 完成拍摄和商详图生成,极大减少传统拍摄成本和上新周期,通过标准化和在线链路打通设计、生产和销售反馈,缩短创意到市场、用户反馈到生产的周期。Used visual algorithms to parse the traits of best-sellers and high-potential styles, identifying market opportunities. AI-assisted design raised design efficiency; AI photography and product-detail image generation slashed traditional shoot costs and launch cycles; standardized, online pipelines connected design, production and sales feedback — shortening both idea-to-market and feedback-to-production loops.
结果Results
在真实店铺跑通各环节 AI 应用的完整链路,将最佳实践 SOP 和模型能力产品化,覆盖商机洞察 - AI 设计/拍摄 - 快速上新/测款全链路,提供端到端的产品解决方案Full AI pipeline proven in a real store; best-practice SOPs and model capabilities productized into an end-to-end solution covering opportunity insight — AI design/photography — rapid launch/testing
商业化闭环验证初探Initial validation of the commercial loop
AI Fashion
里程碑Milestones
自营验证Self-op validation
链路跑通Pipeline complete
能力产品化Productization
商业化验证Commercialization

思考沉淀Writing

比结论更重要的,是判断的过程。 The reasoning matters more than the conclusions.

交流Let's talk

I'm always looking to connect with people who are building with AI.

如果你正在创业、探索 AI 产品、研究 AI 与产业结合,或正在思考 AI 如何改变自己的行业,我很乐意交流。 If you're building a company, exploring AI products, researching how AI meets industry, or thinking about how AI will change your own field — I'd love to talk.

我的经历横跨零售、消费品牌、电商和 AI 创新与应用。我关注的不只是 AI 能做什么,更关注 AI 如何真正进入业务流程,改变效率、决策和商业模式。 My experience spans retail, consumer brands, e-commerce, and AI innovation and applications. What I care about isn't just what AI can do, but how AI actually enters business workflows — reshaping efficiency, decisions, and business models.

我愿意分享实践中的经验、思考,以及那些没有成功的尝试和教训;同时也期待从不同领域的实践者那里获得新的洞察和反馈。 I'm happy to share hands-on experience, thinking, and the attempts and lessons that didn't work out — and I look forward to new insights and feedback from practitioners across different fields.

AI 时代的价值实现,需要更多跨领域的连接和共同探索。Realizing value in the AI era takes more cross-disciplinary connection and shared exploration.