"If nobody sees it, it doesn't exist," said David Van Veen, managing director of beauty branding agency MSLK, summing up what may become the defining challenge for beauty brands in the AI era.

As consumers lean on ChatGPT and Google's AI Overviews to narrow their options, brands get fewer chances to make an impression. AI doesn't return pages of search results. It surfaces a handful of recommendations, favoring brands that appear most consistently across trusted sources on the web.

"It is based on frequency,” said Van Veen. “If you can get the same message associated with you in enough places."

AI doesn't independently evaluate which brands deserve attention. It reflects the information it encounters most consistently across retailer sites, publishers, marketplaces, and the broader web.

"It's taking us backwards," Van Veen said, comparing today's AI landscape with the early days of advertising, when only established companies could afford widespread visibility. "It's frequency-based. It naturally favors brands that are large already."

Beauty has an advantage other categories don't

Despite those challenges, Van Veen believes beauty is better positioned than many industries to withstand AI's tendency to compress discovery.

Unlike commodity purchases, beauty shoppers rarely stop after receiving an AI recommendation. They want detailed information about ingredients, application, routines, shades, and whether a product will work for their specific needs.

"We almost can't build a product page that is long enough if we keep giving really good information on it," Van Veen said. "A quick summary, even a worded summary with an image, is not enough for consumers to choose which foundation they're going to go after or which sunscreen they want to be using."

That appetite for information creates an opportunity. AI may help consumers discover products, but beauty brands still have room to win shoppers through rich educational content once they're in consideration.

Winning AI visibility requires specificity, not scale

For indie brands that can't outspend multinational competitors, Van Veen recommends becoming more specific rather than trying to compete on broad category terms.

He describes the strategy as building a "quantity of specificities."

A moisturizer, for example, isn't simply a moisturizer. Consumers use it for countless highly specific concerns, routines, skin conditions, climates, and occasions. Brands that create content around those real-world use cases have more opportunities to appear when shoppers ask increasingly personalized AI questions.

"People are using it in a lot of different ways that are really specific to them," Van Veen said. "You can really use those specific use cases in lots of different content and lots of different landing pages."

He expects this approach to become even more valuable as AI conversations become more detailed over time. Consumers may begin with broad prompts, but they quickly refine their requests into increasingly specific needs.

AI visibility extends beyond your own website

Brands should think more broadly about where they appear online, Van Veen argues.

Retail partnerships and marketplace listings, even on smaller platforms, create additional digital signals that AI systems can ingest.

"All of those places have their own digital ecosystems," he said. "It provides you a lot of leverage and helps you appear larger than you are."

The same philosophy applies to keyword strategy. Rather than starting with high-volume search terms, MSLK works from the bottom up, analyzing millions of combinations built around ingredients, use cases, consumer identities, and problem-solving language.

"We use this to back it up with data and say this is how consumers are actually talking," Van Veen said. The research influences everything from messaging to formulation decisions.

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