Shampoo and cookies get an AI makeover as consumer giants rewire their labs

The AI story has mostly been told through chips, data centres, and the companies building the models. It is now being told through the shampoo aisle.

The world’s largest makers of everyday goods, the businesses behind the bottles and packets in most kitchens and bathrooms, say they are using artificial intelligence to design products and run the campaigns that sell them, turning a technology associated with software into a fixture of the consumer-goods lab.

It is the same wave of enterprise adoption that has pulled AI tooling into corporate software stacks, arriving now in categories as unglamorous as body wash and biscuits.

Procter & Gamble offers the clearest example of what this looks like inside research and development.

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The company says it used AI to screen tens of thousands of peptides in developing a formula for a Pantene product, drawing on an internal database of more than 8,500 formulations to predict how a mixture would feel on skin or hair before anyone mixed it.

The point is not novelty for its own sake. It is time. Steps that once required rounds of physical testing can be narrowed down computationally, which pushes candidates toward consumer trials faster.

Mondelez, the snacking company behind a long list of familiar biscuit and chocolate brands, describes a similar shift on the food side.

It says an AI product-development tool has helped it generate dozens of new formulations, and that the software lets developers move between two and five times faster than conventional methods.

The same generative systems are being pointed at marketing, producing personalised images, text, and video at a pace traditional studios cannot match.

Unilever has leaned hardest into the campaign side. Its Dove brand ran a cookie-scented body-care line in partnership with Crumbl, with AI involved across the effort, from product direction to the selection of influencers and the creative itself.

The company reported the campaign drew billions of impressions and brought a large share of new buyers to the brand. Whatever one makes of a cookie-scented soap, the mechanics are instructive: a single AI-assisted pipeline running from formulation to feed.

What ties the examples together is compression. In consumer goods, the traditional cost of experimentation is measured in months of lab work and test batches, and the traditional cost of a campaign is measured in agency hours. AI attacks both.

Reformulation becomes a search problem over known ingredients, and content becomes something generated and varied on demand, an approach that mirrors the advertising ambitions on display when OpenAI pitched AI-made ads at Cannes.

The claims deserve some caution. Most of the specific figures come from the companies themselves, and consumer giants have every reason to present their AI programmes as further along than they are.

Product development still ends with human tasting panels and dermatological testing, and a formula an algorithm likes is not the same as one a shopper buys twice.

The industry’s own researchers have flagged that AI-generated marketing often drifts toward the generic, missing the brand-specific character that makes a campaign land.

Still, the direction is consistent across firms that rarely agree on much. The reallocation of enterprise budgets toward AI agents and tooling has become a general feature of large companies, from Tencent’s enterprise agents to the consumer-goods R&D described here, and the packaged-goods sector is not sitting it out.

For shoppers, the visible result will be mundane: more variants, faster refreshes, scents and textures that arrive and vanish more quickly than they used to.

The machinery behind the shelf is changing even where the products look the same. A bottle of shampoo is, increasingly, the output of a search.