Artificial intelligence is reshaping traditional industries, and Sylvia Fu has a story to tell, one informed by her cross-cultural perspective and the execution speed often associated with China.
From 2022–2025, as vice president for Greater China at Edgewell Personal Care, she delivered a strong record: Greater China ranked first globally for sales and profit growth for three consecutive years, and the company incubated in China one of the most commercially explosive new products in its history.
At the end of last year, Fu formally took on a new role as Edgewell’s global AI transformation leader, reporting directly to the global CEO. The move signaled that Edgewell was elevating AI from a point solution to a core strategy.
At the end of March, she had just returned to New York from Shoptalk Spring in Las Vegas when 36Kr sat down with her. At this new starting point, the outlet asked: how does she plan to lead a bottom-up cultural transformation? How are AI and consumers reshaping retail in the US? And what does that mean for the globalization ambitions of Chinese companies?
The following transcript has been edited and consolidated for brevity and clarity.
36Kr: As Edgewell’s global AI transformation leader, can you tell us what you plan to do next? How far along is the company in its AI journey?
Sylvia Fu (SF): In a recent presentation to the global board, we laid out a very clear transformation framework. Our core principle for the next phase of AI deployment is equally clear: operator-first.
At many companies, AI transformation is driven by pure technology teams, which can easily fall into the trap of pursuing technology for technology’s sake. But I came up through the business side. I was directly responsible for the P&L. So the only standard I use to evaluate AI is whether it can translate into real business leverage: driving topline growth, making the organization more agile and faster, improving bottomline efficiency through higher profit and lower costs, while also ensuring responsible data governance and cultural readiness.
Around that principle, we built a framework with three pillars:
- The first pillar is embedding AI into everyday workflows. AI transformation is, at its core, a transformation of people. The goal is for employees in every role around the world to use AI-driven tools such as Copilot and ChatGPT as routine digital assistants. At the same time, we are developing dedicated AI agents for functions such as finance, procurement, and HR to redesign workflows. We selected more than 100 cross-functional employees globally to form a group of “AI advocates,” which serves as an engine for cultural diffusion. Through peer learning, we hope to spark organization-wide transformation. To address business teams’ concerns about data security, we plan to release guidelines in the middle of the year, defining compliance boundaries so employees feel comfortable working intelligently within a secure zone.
- The second pillar is strengthening our data and technology foundation. Like many multinational companies, we face data silos. Our approach is highly pragmatic and runs on two tracks. On one hand, we are not waiting for perfect data. We are prioritizing the use of internal and external data that is already available to launch pilots quickly and validate business return on investment. On the other hand, we continue investing in infrastructure, including underlying systems and our data lake, to standardize global data definitions and levels of granularity, laying the runway for AI at scale.
- The third pillar is focusing on core business scenarios. What I fear most is the tendency to do whatever is trendy or viral. That may be a chatbot today, an image generator tomorrow, and there is lots of activity but usually not much real business value. Our approach is to start with the group’s biggest business pain points. For each use case, we assign both a business team lead and a technology team lead. Those two teams work in tandem. That is the only way to make sure AI is truly solving problems rather than creating new complexity.
These three pillars form a flywheel. The data foundation supplies ammunition for use cases. The use cases generate profit for the business. And an AI culture with broad participation keeps the flywheel spinning and accelerating.
On the marketing insights side, in February this year, we did something very exciting with our North American team. We used a large language model to analyze more than 190,000 pieces of social and review data and identified five entirely new consumer segments in the sun care category. Those insights are now directly informing product development and marketing strategy for Banana Boat and Hawaii Tropic. In the past, this kind of deep consumer insight work would have taken at least three months and cost more than USD 1 million. Now it can be done in a matter of weeks, with finer granularity.
On the global operations side, our European supply chain team is using machine learning models across five active workflows, including center-of-gravity simulation and demand sensing forecasts. On the global legal side, a newly developed AI tool is already being used in six scenarios, including contract review, saving 25% of working time.
36Kr: You have spent most of the past six months in the US. From your vantage point, how has the attitude of US retailers and consumers toward AI fundamentally changed?
SF: What has struck me most over the past six months is that major US retailers have completely moved beyond the early proof-of-concept stage and into deep restructuring across their core value chains.
We can clearly see that the giants are building AI-first operating ecosystems. David Guggina, Walmart US CEO, has explicitly said that agentic AI is being embedded broadly into Walmart’s day-to-day operations and checkout processes. Best Buy is using generative AI to provide real-time sentiment analysis for tens of thousands of customer service staff, freeing employees to build more customer empathy. Costco is applying AI deeply in predictive demand insights and frictionless checkout.
EMarketer’s latest forecast is striking. It projects that AI platform-driven US e-commerce sales will surge from USD 5.4 billion in 2025 to USD 144.5 billion by 2029, roughly 9% of the market. Under a more aggressive scenario, that number could be as high as USD 225 billion. That means every brand must rethink the new opportunities available to it in an AI-driven ecosystem.
But the other side of the coin is an equally revealing phenomenon on the consumer side, what I call the trust paradox. According to a survey released by Publicis Commerce in January 2026, as many as 64% of US consumers have already used AI-powered shopping tools, and their behavior is shifting from simple keyword searches to problem-solving based on complex intent. But here is the paradox: even though 92% say AI is helpful, only 52% actually trust AI-driven product recommendations.
What is especially interesting is that consumers are most likely to trust AI recommendations only when they are already familiar with a brand. And 60% of people, after receiving an AI recommendation, immediately go to real human communities such as Reddit to cross-check it.
That offers a profound lesson. In the age of AI, high tech must be paired with high touch. When AI makes product comparison effortless, the trust a brand has built, the emotional connection it creates, and the authenticity of human experience become the ultimate moat for breaking through the trust barrier and driving conversion.
36Kr: In the face of rapid change, what will Edgewell’s AI focus areas be going forward?
SF: We are closely evaluating several strong partners right now, and there are four main directions:
- First, intelligent supply chains and demand forecasting. This is the lifeblood of fast-moving consumer goods. To be honest, traditional SKU-level forecasting is no longer enough, especially when launching new products, where historical data offers almost no guidance. We are exploring a “data fabric” approach, one that does not rip apart existing systems or require large-scale reconstruction. Instead, we add an AI intelligence layer on top to connect data silos, and then use digital twins to enable more accurate forecasting. Even a ten-point improvement in forecast accuracy would deliver major gains in sales and inventory health.
- Second, data infrastructure and system readiness. No matter how smart the AI model is, if you feed it messy data, the output will also be messy. Our most pragmatic step right now is to make our SAP systems truly AI-ready. Cleaning up and building out our data lake is a midterm priority.
- Third, brand discovery in the age of AI, from SEO (search engine optimization) to GEO (generative engine optimization). I especially want to say a few more words about this because it is truly changing the rules of the game. Today, 60% of Google searches already end in zero clicks. Consumers are no longer browsing links. They want answers directly. When someone asks ChatGPT a question like, “What sunscreen is good for sensitive skin?” and your brand does not appear in the answer, you effectively disappear from the digital shelf. So we are constantly testing generative engine optimization, or GEO. In practice, that means converting product information and consumer reviews into structured formats that large models can understand, so AI can proactively recommend our products when answering consumer questions.
- Fourth, accelerating brand content and product innovation through AI. In China, AI is already helping us produce more than 2,000 e-commerce videos every month. Next, we want to expand that capability into North America and Europe, scaling up the production of product images, videos, and social content while preserving a localized feel. On the product innovation side, we are also using AI to connect the entire chain from consumer insight to concept validation to launch. The earlier example of our North American analysis of 190,000 consumer data points is one case. Halo’s AI-powered technology scouting on the R&D side is another. In the future, those two ends need to connect so we can truly compress the innovation cycle.
36Kr: You have mentioned agentic commerce several times. How will it reshape the future of retail?
SF: This year marks an inflection point for agentic commerce.
In just the six months from September 2025 to March 2026, ChatGPT launched Instant Checkout, Amazon rolled out its Rufus shopping assistant, Google released the Universal Commerce Protocol, and Microsoft and Perplexity also entered the race. Every tech giant is scrambling to control the next-generation AI shopping gateway.
In the future, retail will serve three types of consumers: traditional human shoppers, hybrid consumers who use AI to assist their shopping, and fully autonomous AI agents. The most interesting group is the middle one. They have one foot in the human world, where they care about experience, personalization, and values, and one foot in the AI world, where they use conversational search and multimodal interaction. Right now, the most active AI shopping use case is the middle of the funnel, “help me choose.” But it is already expanding across the whole shopping journey.
For brands, one especially important signal is that consumers are shifting from searching keywords to asking questions. They are no longer typing in “sunscreen SPF50.” Instead, they ask AI, “My skin burns easily. What sunscreen should I use for a trip to the beach?” That is a completely different logic. Traditional SEO cannot solve for that. You need GEO so that AI can understand your product and recommend it in its answers. If AI does not recommend you, your brand may effectively not exist in the eyes of the consumer. That is why winning brand discovery in the age of AI is one of Edgewell’s four core opportunities.
36Kr: How do you view the significance of AI for Chinese companies expanding overseas?
SF: Chinese companies going global have entered a new phase, and it is no longer enough to focus on scale expansion alone. They now need to build value and use technology as a driver in parallel. In that process, AI is not a nice-to-have. It is effectively the core operating system that supports global operations.
What used to be the hardest part of going overseas? You could not achieve scale and localization at the same time. If you wanted to grow big, you could not go deep. If you wanted to go deep, you could not scale broadly. AI is changing that equation. Capabilities such as multilingual generation, predictive analytics, and consumer insights make it possible to deliver genuinely localized experiences in each market at very low marginal cost.
There is another point that many people overlook: compliance. The EU AI Act and privacy regulations in different countries may look like constraints, but if you can systematically embed trust by design into your products and operations, compliance can become a competitive advantage. Consumers will feel that your brand is reliable, and that is especially valuable in overseas markets.
Chinese companies genuinely have natural advantages in applied innovation and agile iteration. As long as they infuse high tech with high empathy, they are fully capable of leading in this new wave of globalization.
36Kr: What matters most if Chinese brands want to enter the US, truly build brands, and establish channels there? And where can AI help?
SF: To be honest, the US and China require very different playbooks. I have seen many Chinese brands still trying to use the domestic approach, and some detours could absolutely be avoided. To me, it comes down to three things: how to build a durable brand, how to open up channels, and how to put down organizational roots:
- First, your brand DNA must take clear shape. Building a brand in the US is not just a matter of registering a trademark and launching an English-language website. Middle-class consumers in the US care deeply about what you stand for, your values, and your mission. That directly affects whether they will buy from you again. Shein talks about making the beauty of fashion accessible to everyone. Skims uses intimate apparel to communicate diversity and inclusion. These brands have established themselves in the US not because they are cheap, but because consumers feel understood.
- Second, the emotional value sought by Gen Z cannot be ignored. This generation has a different consumption logic. Good functionality alone is not enough. They want a feeling: companionship, identification, and self-expression. Chinese brands have strengths in technology and supply chains, but what they often lack is that emotional hook. The brands that can tell stories in the language of young people are the ones that can command a premium.
- Third, brand visibility in the age of AI must be planned in advance. The GEO point we discussed earlier is just as critical for overseas brands. More and more US consumers are going directly to ChatGPT and asking, “Recommend a good sunscreen.” If your brand is missing from the AI’s answer, it becomes invisible on the digital shelf. Brands have to turn their product information and consumer reviews into high-quality structured content so large models can understand them and recommend them.
In terms of channels, omnichannel integration is essential, and brands must go after offline retail:
- First, omnichannel integration is a basic capability. US consumers have highly fragmented shopping paths. They may discover a product online and buy it offline, examine it offline and order it online, or move between channels in countless combinations. If your Amazon storefront, independent site, TikTok Shop, and offline data are all disconnected, the consumer experience will break down. You need a data hub that fully connects inventory, customer profiles, and orders.
- Second, you absolutely have to attack offline retail. Many people may not realize that offline sales still account for as much as 87% in the US, which is very different from China. And major retailers have enormous bargaining power. Right now, there is a rare window for replacement. Changes in tariffs are pushing some legacy brands out, while buying teams are getting younger and becoming more sensitive to innovation. Chinese brands that have already built an online reputation are finding it easier than before to win entry tickets to offline retail.
- Third, when moving from online to offline, pricing and supply chains are decisive. Online and offline prices must be carefully separated so they do not cannibalize each other. US retailers are extremely strict about sales per square foot, and their supply chain requirements are exacting. Regional warehouse stocking and replenishment within three days are basic expectations. Stockouts or packaging noncompliance can bring heavy penalties, and even after entering Walmart, a brand can still be delisted.
Finally, there is the organization, which must be deeply localized. “Real globalization is extreme localization.” That is one of the most important lessons I have learned over the past few years. In the US, companies need a fully local team. This goes beyond language. It requires understanding how sensitive American consumers are to privacy, how high their expectations are for after-sales service, and which cultural taboos to avoid. At the same time, companies should know how to leverage local professional service providers. Their buyer networks and channel experience can save significant time and cost.
As for AI’s role, it is not a standalone tool for going overseas. It is an accelerator that runs through every part of the process. On the brand side, it supports consumer insight, content production, and GEO optimization. On the channel side, it helps connect omnichannel data, forecast demand, and enable intelligent replenishment. On the organizational side, it supports cross-cultural communication and localized content adaptation. The key is not to treat AI as a cure-all, but to identify the single business pain point that matters most and solve it precisely.
To sum it up in one sentence: online is the proving ground, while offline is where the competition concentrates.
Use emotional connection and AI-based content infrastructure to secure mindshare, and use supply chains and integrated channels to deliver value. That is the path for Chinese brands to move from selling globally to competing globally with intelligence.
KrASIA features translated and adapted content that was originally published by 36Kr. This article was written by Xiao Xi for 36Kr.