Inside LVMH Beauty’s AI Strategy with CDAO Julie De Moyer

What happens when you combine the rigor of economics, the creativity of luxury beauty, and the transformative power of AI? 

In a recent episode of Data Radicals, LVMH Beauty’s Chief Data and AI Officer, Julie De Moyer, joined host Satyen Sangani to share how she’s reshaping the role of the CDAO from behind-the-scenes technologist to business-driving strategist. From running regressions on marathon times to orchestrating AI across 15 iconic brands, Julie’s journey is anything but conventional. Watch the full episode or read on for key takeaways from this insightful conversation about data, creativity, and leadership at the intersection of luxury and technology.

From economics to enterprise AI: De Moyer’s unconventional path

Julie De Moyer’s journey to becoming Chief Data and AI Officer at LVMH Beauty wasn’t a linear one. With a background in economics—not computer science—she brings a business-first mindset to data and AI strategy. 

“I’m not your typical data officer,” she tells host Satyen Sangani. “My background is in economics, and later in product marketing. But the red thread throughout my career has been leveraging data to drive better decisions.”

Her early passion for analytics started not in a lab but on the track—literally. While training for marathons, she ran regressions on her running times to optimize performance. That analytical hobby eventually became a profession as she transitioned from marketing to data roles at Philips, the World Bank, and today LVMH Beauty.

Rethinking the role of the Chief Data Officer

The modern CDO isn’t a back-office technician focused on building dashboards. Today’s data leaders are business strategists, integrators, and change agents.

De Moyer puts it simply: “We’ve moved from being builders to being value creators.”

At LVMH Beauty, de Moyer works directly with the CEO and across the C-suite to align data and AI strategy with the business’s long-term goals. “I’m no longer just working with the CIO,” she explains. Today, she is a trusted partner to the CEO, helping to meet business goals through cross-functional collaboration. “I'm actually really much closer to the business owners and the domain owners,” she elaborates.

CDOs, she says, must transition from tech implementers to value orchestrators—enabling real-time decision-making and steering organizations with forward-looking insight. “We're no longer the rear-view mirror,” Julie noted. “We’re the augmented GPS.”

Leading with listening: Why data must follow business needs

De Moyer emphasizes that meaningful transformation starts with understanding the real—not theoretical—processes behind how a business operates. You can’t solve problems from the cubicle, she argues. Instead, her team hits the floor literally—visiting factories, warehouses, and retail stores to observe how decisions are made and where gaps in key processes lie.

Their findings are surprising. Often, the real processes on the ground deviate from the process HQ has recommended, as De Moyer shares stories of warehouse workers creating their own systems because the digital tools weren’t working properly. These represent opportunities. “These operational people on the ground have good ideas that we can then turn into a process optimization,” De Moyer points out.

Her own process for finding these ways to improve? Start by listening. “Sit in on the three-year planning sessions, listen to the CEOs to learn their biggest headaches,” and then prioritize by impact. Be curious! Talk to store managers, e-commerce leaders, and perfumers to understand where decisions are made and what really matters to each persona.

How LVMH applies AI across the luxury value chain

De Moyer outlined five core areas where AI and data add value across LVMH Beauty’s operations:

1. Create: In product R&D, AI accelerates formulation testing and helps navigate new regulations. But the final say always lies with human creators. AI will never replace the perfumer, she stresses. But it can help them explore “substitutions of products that might need to go out as a result of regulatory changes.”

2. Make: In supply chain operations, AI helps reduce overstock and understock, aligning forecasting with sustainability goals. This ensures operational efficiency without sacrificing values.

3. Show: Data is used to personalize communication. We want to reach customers at the right moment with the right message—and only when it’s truly relevant, De Moyer shares.

4. Engage: Post-purchase, AI deepens client relationships by securely using shared preferences and routines to offer personalized advice—like makeup recommendations for a special night out—while always respecting strict privacy boundaries.

Despite the focus on AI, De Moyer emphasizes that AI is often in the background. The customer experience must always feel personal, human, and in line with the brand’s identity.

Building responsible AI: Ethics, guardrails, and culture

With AI playing a bigger role across the enterprise, De Moyer believes CDOs must champion governance. You need guardrails, she explains. “What are the ethics? How do you set that moral compass for your organization?”

That means ensuring privacy, embedding AI into workflows responsibly, and avoiding the shiny object syndrome. “Bad data plus good AI still leads to bad decisions,” she reminds listeners. The focus should always be on value, not novelty.

The art of orchestration in a multi-brand environment

Overseeing 15 beauty brands, each with its own voice, challenges, and goals, is no small feat. De Moyer explains that while synergies—like shared data lakes or ethical AI guidelines—are essential, each brand’s unique data, channels, products, and pricing require tailored approaches. “The variables are endless, but it makes the job really, really interesting,” she says.

She clusters brands by maturity and capability, ensuring that no maison is left behind. The larger brands can move fast, but her team also needs to enable the smaller, agile brands to innovate, she explains.

It’s not about centralizing for the sake of efficiency—it’s about building flexible systems that serve a range of business needs, from bespoke luxury experiences to large-scale operations.

Advice for aspiring CDAOs and data leaders

De Moyer offers candid advice for the next generation of data leaders:

1. Know the business: De Moyer emphasizes the importance of aligning data strategy to business value. “No matter your level, understand where your company makes its money” whether that’s through volume, craftsmanship, or speed—and translate that into your data priorities, she says. This means saying yes to what supports the brand’s ambition and no to what doesn’t.

2. Respect the magic: De Moyer urges data leaders to leverage their wisdom about their business niche to recognize where AI doesn’t belong. “In luxury, you never choose speed over quality,” she says. Understanding your industry’s unique trade-offs—whether it’s protecting craftsmanship or ensuring thoughtful decisions—helps determine when to say no to AI. Know what makes your brand special, and protect that magic.

3. Put the consumer first: Data leaders should stay close to the customer as a habit. “Go speak to them. Understand what makes them tick, what ticks them off, and how you can add value,” she says. Consumer preferences evolve—across channels, regions, and expectations—so staying connected is an ongoing, essential process.

She also emphasizes communication skills: Don’t speak in software. Don’t speak in Jira tickets. Speak in value. Speak the language of the business. “I personally don’t use abbreviations anymore,” she reveals. Focus instead on the “value and the impact you are driving.”

De Moyer’s parting wisdom? “We don’t need big data to get started. Sometimes a simple regression model does the trick. What matters most is impact.”

Data with soul: Where AI enhances, not replaces, creativity

Julie De Moyer is redefining what it means to be a data leader in luxury. At LVMH Beauty, data isn’t just about KPIs—it’s about craftsmanship, storytelling, and brand identity. The goal isn’t to use AI for everything, she counsels. It’s to use it where it adds value—and stay out of the way where it doesn’t.

In an industry where emotional connection and creative excellence are paramount, data must work in service of the brand, not the other way around.

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