L'Oréal, Mondelez, and Nestlé are deploying AI tools that cut product development timelines from months to weeks, compressing formulation testing, ingredient screening, and recipe generation into digital workflows that run before anything reaches a lab bench.
Speaking to Reuters, Fabrice Megarbane, president of L'Oréal's consumer products division, said the French cosmetics group has applied AI in its laboratories for the past four years. The technology helps the company predict how molecules will affect skin and hair before they are used in new formulations. L'Oréal said product formulation is now four times faster.
Cosmetics testing moves to predictive models
L'Oréal's predictive formulation work lets scientists simulate ingredient performance digitally. Variables that once required physical samples get tested in software first, narrowing the options that proceed to the lab. One recent example involved molecules previously used in skincare products. The company repurposed them for a collagen-based shampoo designed to add lift and fullness to hair.
Megarbane said AI allows product teams to test new combinations of molecules and assess their potential benefits more quickly. The technology sits alongside a broader innovation push after the group reported its slowest sales growth in years. Chief Executive Nicolas Hieronimus introduced a "beauty stimulus plan" last year to support new product development as consumer tastes shift across beauty categories.
Recipe generation moves upstream
At Mondelez, AI supports recipe development across brands including Cadbury, Toblerone, Oreo, and Chips Ahoy. Chief Information and Digital Officer Filippo Catalano said the company's tool can generate recipe ideas - including unusual combinations - before human experts review them. The system supported development of Gluten Free Golden Oreo cookies and a refreshed Chips Ahoy recipe.
Mondelez said 60% of biscuit recipes produced with its AI tool performed better across nutrition, sustainability, and cost. Catalano said the technology is reducing the number of physical samples typically generated during product innovation. The tool also helps the company reduce dependence on single sourcing by identifying alternative recipe options when ingredients vary by availability, price, or other requirements.
Nestlé is applying similar pressure to reformulation. The company plans to remove artificial food colourings from all products worldwide by the end of 2026, having already removed them from its US portfolio. Chief Technology Officer Stefan Palzer said the work required screening natural alternatives, testing them during production, and assessing shelf life. The US Food and Drug Administration has said it is working with manufacturers, retailers, and trade groups to remove six remaining certified colour additives frequently used in the food supply by the end of 2027.
Packaging and partnerships expand the scope
Nestlé and IBM Research said in 2025 that they had developed a generative AI tool to identify high-barrier packaging materials. The tool assesses materials designed to protect products from moisture, oxygen, and temperature changes, while factoring in cost, recyclability, and functionality. It uses chemical language modelling and IBM Research's regression transformer to link molecular structures with physical-chemical properties. Separately, Barry Callebaut has partnered with NotCo to use AI in chocolate recipe development, including work on ingredient alternatives using plant-based combinations.
Haleon announced a five-year collaboration with Microsoft in June 2026 covering consumer insights, product innovation, supply chain operations, scientific research, clinical content development, forecasting, and commercial execution. The consumer health company is among the firms cited as using AI in product innovation.
Catalano said AI is compressing work that previously took months or years. "The technology is not replacing human product teams," he said, but is being used to speed up existing research, testing, and formulation processes. Human experts assess AI-generated recipe ideas before products move further through development. For product managers navigating this shift, the AI Learning Path for Product Managers covers the practical skills needed to integrate these tools into development workflows without losing the human judgment that final decisions still require.
Why this matters for product development teams
The common thread across these cases is compression. L'Oréal's fourfold speed increase, Mondelez's 60% recipe performance gain, and Nestlé's reformulation timeline all point to the same operational change: the bottleneck moves from physical testing to the quality of the questions teams ask upfront. Product developers who can write precise formulation briefs, set clear constraints for AI tools, and evaluate machine-generated options against business criteria will get more out of these systems than those who treat them as a faster version of the old process. The AI for Product Development landscape is shifting toward tools that augment rather than replace - but the speed of adoption means the gap between teams that build this capability and those that delay it will widen quickly.
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