VIENNA - L'Oréal has used artificial intelligence to identify molecules in its skincare products that can be repurposed for shampoo, allowing the company to create new products four times faster than before, a senior executive told Reuters. Chocolate maker Mondelēz, Nescafé owner Nestlé, and toothpaste producer Haleon are also embedding AI into product innovation, compressing timelines and reshaping how recipes and ingredients are tested.
Repurposing molecules with AI at L'Oréal
L'Oréal began using AI in its labs four years ago and now predicts the effect molecules will have on skin and hair, said Fabrice Megarbane, president of the consumer products unit. A recent shampoo uses collagen molecules originally deployed in skincare to add lift and fullness. "You can really go much faster by imagining… new associations of molecules and new benefits of molecules," Megarbane said at the Consumer Goods Forum's Global Summit in Vienna in late June. The approach helps the company sidestep the usual trial-and-error cycle by simulating outcomes before physical testing begins.
Recipe generation and supply chain resilience at Mondelēz
Mondelēz, the company behind Cadbury and Toblerone, uses an AI tool that generates recipe ideas - including what Chief Information and Digital Officer Filippo Catalano called "out-of-the-box" concepts - which a human expert then assesses. The technology helped develop Gluten Free Golden Oreo cookies and a refreshed Chips Ahoy recipe. In the biscuit category, 60% of recipes produced using the AI tool performed better on nutrition, sustainability, or cost metrics.
Catalano said the tool reduces the number of physical samples typically needed during innovation. It also allows the company to adapt formulas quickly to changing consumer tastes and reduce dependency on single suppliers. "(AI capabilities are) accelerating things you could do already, but compressing the time from months to weeks or years to months," he said.
The broader push for speed in consumer goods
Nestlé and Haleon are among other large players using AI to test ingredients faster and generate recipe ideas, executives confirmed at the summit. The common thread is a need to respond to shifting demand without the long lead times that traditional R&D cycles require. AI models can screen ingredient combinations, flag supply chain vulnerabilities, and model cost implications before a single prototype is made, letting teams focus on the most promising candidates.
Why this matters for product development
For product development professionals, these examples show that AI is not replacing the creative or scientific judgment of human teams - it is cutting the repetitive, low-value work that pads timelines. When a machine can surface viable molecule combinations or recipe tweaks overnight, teams spend their hours on strategic decisions about market fit, sensory testing, and regulatory compliance. The result is a pipeline that moves from concept to shelf in weeks rather than months, directly tying AI investment to faster revenue from new products.
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