In January, a McKinsey report projected that generative AI could create between $9 billion and $10 billion in annual economic value for the global beauty industry. Six months later and the world’s largest beauty conglomerates appear determined to turn that prediction into reality. From Estée Lauder’s partnership with Microsoft, intended to speed up product development cycles, to Unilever’s internal rollout of more than 500 AI tools across supply chain, R&D, and marketing, the sector is rushing to embed AI into its operating systems. L’Oréal, in particular, has made headlines with its collaboration with IBM and Nvidia to build out a generative AI content lab. This effort will contribute to helping L'Oréal meet its L'Oréal for the Future’s target of sourcing most of its product formulas based on bio-sourced materials and the circular economy by 2030. At retail event Shoptalk Europe 2025, the French beauty giant discussed how it is preparing for a future where AI search assistants could become the new gatekeepers of product discovery, from WhatsApp conversations with dermatologists through to virtual try-on tools for makeup and hairstyling. “We’re seeing the beauty industry catch up very quickly, especially over the past 12 months,” says Guilhem Souche, senior advisor at Sthrive.AI and a former executive at L’Oréal, Parfums Christian Dior, and Coty. “Initially, sectors like fintech and entertainment moved faster. They needed automation and were ready to make heavy investments [...] But today, AI is being democratized. Beauty has unique opportunities to leverage it, particularly in terms of personalization, content creation, and consumer engagement, with a good ROI, and that's where momentum is building.” Melissa Alcocer, founder and director of market intelligence agency Inluxury, concurs. “To assess adoption, we need to distinguish between AI and generative AI,” she says. “Traditional AI analyses data and supports automation. Generative AI creates — from product concepts and content to personalised recommendations and routines. “It’s being used not only for marketing and personalisation, but also in early-stage product development, trend simulation, and R&D ideation — bringing creativity and speed into processes that once took months.” As well as a fear of being left behind, the push is being propelled by a growing recognition that generative AI can produce measurable gains, especially when it comes to personalization, content generation, and speed to market. Yet, alongside opportunity comes complexity, from data privacy risks to shifting regulations. Beauty brands catching up with GenAI Compared to sectors like fintech or entertainment, which were early movers in generative AI due to existing digital infrastructure and fewer creative constraints, beauty initially lagged behind. But that’s changing rapidly. Souche says that companies like Coty were experimenting with generative AI as early as 2021, including the use of AI avatars during livestreaming on Tmall. More recently, Coty has claimed the ability to generate 1,000 content assets in minutes, tailored to each platform and market. Meanwhile, newer players like E.L.F. Beauty is testing GenAI voice search optimization, campaign ideation and augmented reality. This year’s Viva Tech conference served as a signal of just how seriously L’Oréal is taking the shift. During the event, the French multinational showcased advanced AI-generated virtual host technology developed by Topview.ai leveraging China’s booming e-commerce and livestreaming expertise. The brand successfully “exported” this innovation from Chinese livestream rooms to a global stage. Scaling marketing and rethinking retail with AI As brands aim to reach consumers across dozens of channels and geographies, generative AI is proving most immediately transformative in marketing operations. Souche says the technology enables faster content production, automated translation, and real-time localization, providing consistency while responding to platform-specific needs. “It’s not just faster, it’s more consistent and more adaptable to each platform,” he says. The beauty industry’s interest in generative AI extends beyond digital touchpoints. As physical retail regains traction, especially across Asia, brands are also exploring how GenAI can optimize offline performance. Alcocer says that the impact at the retail level is already evident. “AR mirrors alone have been shown to increase conversion by up to 90% and boost basket size by 30%,” she says. “But when paired with generative AI, these experiences become even more powerful, offering personalized looks, custom shade matching, and real-time diagnostics that adapt based on each user’s data.” “Consumers no longer want to just try a product virtually; they expect it to be designed for them at that moment,” she adds. Platforms like Sthrive.AI have emerged to facilitate offline sales with GenAI. While staying platform-agnostic in tone, Souche says that these tools combine store sales, CRM, and training data to generate performance-enhancing action plans for frontline retail teams. “In a recent case, we noticed a hero SKU wasn’t converting in a few stores, even though traffic and visibility were fine. The data flagged that newer associates lacked confidence selling it. We pushed targeted training and conversions improved within days,” he says. “These are things traditional dashboards often miss.” As AI becomes more embedded across retail and marketing workflows, brands are expected to gain an edge, not by simply having the technology, but by applying it at the ground level. Using AI from product to prediction Beyond marketing and retail, beauty companies are exploring how generative AI might enhance R&D by compressing development timelines and anticipating consumer preferences. “In R&D, we’re seeing GenAI being used to anticipate trends and even co-create with consumers,” Souche says. “That helps brands ensure what their launch aligns with actual market demand.” Aslada Gu, Director of Product and Innovation at Hong Kong-headquartered martech firm Gusto Collective, agrees that the implications span the full value chain. “In R&D, AI allows for more precise forecasting of consumer needs. In marketing, it enables deeper personalization and in-depth interactions. And in operations, it helps automate inventory and logistics,” she says. For global conglomerates such as Estée Lauder and L’Oréal, Gu believes these capabilities unlock efficiency at scale, leveraging the businesses’ vast user bases and data pools. “These groups are positioned to achieve substantial advantages across all facets of their operations,” she adds. AI’s data, IP, and the compliance tightrope With increased AI adoption comes greater risk, especially around data governance, IP leakage, and algorithmic transparency. This is particularly relevant for global beauty players operating in highly regulated markets such as China and the EU. “Beauty has always been cautious with consumer data, especially in markets like Europe and China,” says Souche. “But now, GenAI tools expose internal concepts, product formulations, brand names, and campaign strategies. These are all sensitive assets.” One major risk is the inadvertent leakage of intellectual property. “If your teams use open AI tools without safeguards, you could unknowingly seed proprietary ideas into public models,” he says. “Imagine brainstorming a new fragrance name and seeing a similar one launch from a competitor six months later. That’s not fiction; that’s a genuine risk.” Regulatory scrutiny is also intensifying. China has enacted new rules around data localization and algorithmic governance, while Europe’s AI Act introduces requirements around transparency and consumer protection. Brands must balance innovation with compliance and ensure their AI models are interpretable and responsibly deployed. Gu echoes these concerns. “IP management becomes complex when AI generates creative content, raising questions about ownership,” she says. “Brands must navigate these issues carefully to maintain compliance and protect their reputation.” One of the most sensitive areas is the use of personal and biometric data, particularly facial scans and skin diagnostics, says Alcocer. These tools collect and process highly individualised information to generate personalised product recommendations or visual try-ons. While this offers real value to consumers, it also raises serious concerns about consent, storage, and long-term data use. Facial data, in particular, is considered biometric information and is subject to strict regulations in many markets. Bias is another area of concern, particularly in beauty, where issues of representation and inclusivity are central. “If an AI model is trained on skewed data, or out of context, it can generate tone-deaf, or even offensive outputs,” Souche says. “That could mean skin tone misrepresentation, or culturally inappropriate suggestions.” Alcocer warns there is a fine line between enhancing appearance and reinforcing unrealistic beauty standards. “Generative tools must be designed with care to avoid contributing to image dysmorphia or exclusion. Transparency, inclusivity, and digital wellbeing are becoming just as important as technological capability,” she says. Both Gu and Souche emphasized the importance of human oversight. “AI can scale operations, but it doesn’t understand nuance,” says Souche. “In beauty, we’re not just selling formulas. We’re telling stories.” The investment outlook As generative AI gains traction, the definition of “beauty tech” is evolving. Historically associated with skin devices or AR-powered mirrors, the term now encompasses AI-native platforms that embed intelligence throughout operations. “Diagnostics can become more personalized and predictive. AR experiences can adapt in real time. Magic mirrors can deliver tailored storytelling, not just product listings,” says Souche. “From an investor’s perspective, GenAI is a game-changer.” Gu sees growing interest from investors in startups that place generative AI at the core of their offering. Beauty-tech startups integrating generative AI are poised to attract heightened investment interest undoubtedly, she says. But brands must prioritize the intrinsic value and significance of their products for consumers. AI is a tool — it shouldn’t replace product excellence and brand experience. “At VivaTech 2025, generative AI dominated innovation conversations across beauty and fashion,” says Alcocer. “From real-time skincare diagnostics to AI-generated visuals and marketing assets, the message was clear: VCs are backing platforms that embed generative AI throughout the product lifecycle — not just at the consumer-facing level, but also within content creation, product design, and demand prediction.” Major beauty groups are also positioning themselves to work more closely with AI-first startups. LVMH’s AI Factory, for example, is designed to deploy modular AI solutions internally while also scouting external innovators. The value proposition for beauty-tech startups now hinges on whether they can deliver ROI at the ground level, translating complex data into decisions that improve consumer engagement, retail execution, or product relevance. A new operating system for beauty The generative AI wave is still unfolding, but beauty’s adoption curve has steepened. What started as a tool for content creation is now reshaping how brands formulate products, plan inventory, and train frontline staff. With regulators tightening oversight and competition intensifying, the real winners may be those who pair technological sophistication with strategic discipline. In an industry that sells aspiration and emotion, the success of AI will ultimately hinge on its ability to enhance, not replace, human creativity and connection.
