AI Shopping Tools Gain Traction in Beauty, Health—Not Snacks

AI Shopping Adoption Follows Decision Complexity, Not Just Online Sales

Artificial intelligence is reshaping how consumers discover and choose products, but the impact varies dramatically by category. AI referral traffic grew more than 300% last year, yet that growth concentrates in a narrow band of retail segments where shoppers face the most uncertainty.

Beauty and personal care captures roughly 45% of generative AI referrals in fast-moving consumer goods, while consumer health accounts for another 28%, according to data from Euromonitor International based on a panel of 5.3 million AI users. Meanwhile, categories like snacks and pet care remain in the low single digits—despite many having strong e-commerce penetration.

The pattern suggests AI shopping tools gain traction not simply where online sales are high, but where the decision itself is hard. Shoppers turn to AI when they need help interpreting product claims, comparing options, and building confidence in their choice.

Why it matters

Retailers and brands investing in AI discovery need category-specific strategies, not blanket approaches. In habitual, low-risk purchases, AI adds little value. In complex, high-stakes categories, it can fundamentally alter which brands enter consideration—and current data shows only 10–15% of U.S. skincare brands appear in AI recommendations at all.

Where AI Removes Shopping Friction

More than one-third of consumers say they use generative AI for clearer explanations and answers, according to a Euromonitor survey. The technology excels at collapsing the effort required to compare products, read reviews, and evaluate claims.

The categories most exposed to AI disruption sit at the intersection of two shopper needs: comparison and reassurance. Anti-aging skincare exemplifies this. The aisle is crowded, claims are technical, products can be expensive, and outcomes are personal. AI helps narrow the field and build purchase confidence.

By contrast, low-risk habitual purchases like soft drinks or milk present little decision work for AI to perform. The shopper already knows what they want.

Algorithmic Visibility Is Not Guaranteed

Two data points underscore the competitive stakes. First, 78% of consumers report discovering a new brand through generative AI, per Euromonitor's consumer survey. Second, AI real estate is severely limited—only a small fraction of brands in any category appear in AI-generated recommendations.

Visibility in AI answers depends on whether a brand's data, claims, and category signals are structured clearly enough for AI systems to interpret. Euromonitor research found that "derm tested" positioning alone produces a 95% lift in referral share. Transparency and problem-solution messaging outperform traditional brand strength in these environments.

Established brands that assume commercial scale will automatically translate into algorithmic visibility face the greatest risk. AI rewards relevance and interpretability, not legacy market position.

Strategy Must Be Category-Specific

A single AI approach will not work across a full retail portfolio. Investment and adaptation need to align with where shoppers genuinely need help reducing uncertainty. For many categories, that threshold has not been crossed. For others—particularly in beauty and health—AI is already reshaping the consideration set.

The competitive question is whether a brand is visible and legible in the moments when AI begins to shape purchase decisions. The answer depends less on the strength of the brand today and more on how well its information architecture serves algorithmic discovery.

These findings were first reported by Michelle Evans in Forbes.