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.
This 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. 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 Speeds Up Snack Development
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’s AI-Powered Campaign Pipeline
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.
Cautionary Notes and Industry Realities
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.
Historical Context of Innovation in Consumer Goods
To understand the magnitude of this shift, it helps to look back at how consumer goods have historically innovated. For decades, product development relied on trial and error, extensive manual testing, and lengthy cycles. A new shampoo formula might take 12 to 18 months from concept to shelf. Today, AI can narrow down thousands of possible ingredients and combinations in days. The same applies to snacks: a new cookie flavor used to require dozens of test batches in a pilot plant before moving to consumer panels. Now, AI models predict taste, texture, and stability based on molecular properties and historical data.
This acceleration has profound implications for supply chains, inventory management, and marketing calendars. Brands can respond faster to consumer trends, such as the sudden popularity of a certain scent or health ingredient. However, it also raises questions about the homogenization of products if too many companies rely on similar AI tools and datasets.
How AI Models Work in Formulation
The underlying technology typically involves machine learning models trained on vast databases of past formulations, sensory panel results, and consumer feedback. When a formulator inputs desired characteristics—like a creamy texture for a lotion or a crunchy bite for a biscuit—the AI suggests mixtures that are most likely to meet those parameters. It can also flag potential stability issues or interactions between ingredients. This is not a replacement for chemists or food scientists but a powerful assistant that reduces the search space.
Procter & Gamble, for instance, has been developing its “digital twin” systems for years, creating virtual replicas of physical products that can be tested computationally. This allows the company to run thousands of simulations without consuming physical resources. Similarly, Mondelez uses generative models to create novel flavor combinations that human developers might not think of. These systems can even optimize for cost, sustainability, or nutritional profiles.
Impact on Marketing and Personalization
The marketing side is equally transformative. AI tools now generate personalized ad copy, images, and even short videos tailored to individual consumer segments. Unilever’s campaign for Dove and Crumbl exemplifies this: the AI selected influencers whose followers matched the target demographic, generated product descriptions that resonated with different age groups, and optimized the timing of posts. The result was a highly efficient, data-driven campaign that would have taken weeks to produce manually.
This capability is not limited to large multinationals. Smaller brands are beginning to adopt AI marketing platforms that offer similar functionality at lower costs. The barrier to entry is dropping, which could democratize advertising but also intensify competition for consumer attention.
Challenges and Limitations
Despite the promise, several challenges remain. First, the quality of AI outputs depends heavily on the data fed into the models. If a company’s historical data is biased or incomplete, the AI may propose suboptimal or even harmful formulations. For example, an AI trained on past successful products might miss novel ingredients that break the mold. Second, regulatory hurdles can slow down the adoption of AI-formulated products, especially in foods and cosmetics, where safety testing is mandatory. Regulators may require additional documentation to verify that AI-generated formulations meet health standards.
Third, there is the risk of over-reliance on AI. Human creativity and intuition have historically driven breakthrough products—think of the invention of Post-it Notes or the development of plant-based meats. If companies outsource too much creativity to algorithms, they might lose the ability to innovate beyond what the data suggests.
The Broader Enterprise AI Landscape
The consumer goods sector is part of a larger trend where enterprises across industries are investing in AI agents and automation. From financial services using AI for fraud detection to manufacturing robots that self-optimize, the pattern is clear. Packaged goods companies are now joining this wave because the competitive pressure is mounting. Early adopters can bring products to market faster and more cheaply, leaving slower rivals to play catch-up.
But the shift also has societal implications. Jobs in traditional R&D labs may evolve or disappear as AI takes over routine screening tasks. At the same time, new roles are emerging, such as prompt engineers, data annotators, and AI ethicists. Companies must manage this transition carefully to avoid alienating their workforce.
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—a search that began with an algorithm narrowing possibilities before a single drop was mixed.