AI is already reshaping the shopper journey. The question is no longer whether it will influence shopping, but where it will matter most. That distinction has important implications for retailers and brands.

AI referral traffic grew more than 300% last year , according to Euromonitor International data. That's an important signal, but headline numbers only tell part of the story. Retail doesn't move in aggregate. AI will not reshape every aisle equally because shoppers do not shop uniformly across each category.

The data shows something far more selective: AI wins where it adds the most decision value. That is highest when AI helps shoppers reduce uncertainty, narrow choice or feel more confident about a purchase.

Across much of e-commerce, AI may have little work to do. In other areas, it could fundamentally reshape how consumers shop. That means investment and adaptation need to be highly category specific.

To start, look at where shoppers arrive after using AI platforms for their shopping questions.

Across fast-moving consumer goods, AI referral traffic is already concentrated in a narrow set of categories. Beauty and personal care captures roughly 45% of generative AI referrals, while consumer health accounts for another 28%, according to Euromonitor International’s panel of 5.3 million AI users. Many others, such as snacks and pet care, remain in the low single digits.

That split matters because it suggests AI shopping is not simply following online penetration. If it were, pet care would be higher. Instead, referrals are concentrating in highly digitized categories where shoppers need help interpreting claims, comparing options and ensuring confidence in the choice.

Simply put, AI removes the hard part of shopping . Shoppers indicate that this is the reason they use GenAI, too. More than one-third of consumers said the top reason they turn to GenAI is for clearer explanations and answers in a Euromonitor survey. AI helps consumers move faster through moments that used to require effort: comparing products, reading reviews and evaluating claims.

Where AI Adds Decision Value

At its core, discovery comes down to two questions: How does this product compare? And am I making the right choice? Those questions map to two needs: the need for comparison and reassurance. AI’s potential category impact becomes clearer when brands plot their categories against both dimensions.

The categories most exposed to AI are those where comparison and reassurance both run high. In low-risk, habitual categories, such as soft drinks or milk, there is little decision work for AI to perform. In more complex categories, AI becomes useful because it reduces effort. It helps shoppers sort through claims, narrow options and build confidence in a decision.

The highest exposure categories are those where the shopper faces both complexity and consequences. Anti-aging skin care is one example. The aisle is crowded, product claims are difficult to evaluate, products can be pricey and the purchase is closely tied to personal outcomes. In that context, AI narrows the field of consideration to help the shopper arrive at a decision.

Visibility Is Earned, Not Guaranteed

Two facts underscore the urgency of this shift. According to a Euromonitor consumer survey, 78% of consumers say they’ve already discovered a new brand through GenAI. Second, AI real estate is severely limited. For example, only 10 to 15% of U.S. skin care brands show up in AI-driven recommendations, according to Euromonitor research. AI discovery is binary: a brand appears in the limited number of options given in the AI answer or it does not.

In AI discovery, visibility is not guaranteed by shelf position or media spend, at least not yet. It depends on whether a brand’s data, claims and category signals are structured clearly enough for AI systems to interpret. Brands built on ingredient transparency and clear problem-solution messaging are gaining fastest. In fact, Euromonitor research found that "derm tested" positioning alone produces a 95% lift in referral share. AI algorithms reward relevance before it rewards effort.

A Single AI Strategy Won’t Be Enough

A single AI strategy will not be enough across a full retail portfolio. It should be assessed category by category, based on where shoppers need help reducing uncertainty, comparing options or feeling confident in the decision. That is where AI has the clearest job to do.

For brands, the competitive question is not simply how to use AI. It is whether the brand is visible and legible in the moments when AI begins to shape consideration. The brands most at risk may not be the weakest players today, but established names that assume commercial strength will automatically translate into algorithmic visibility.