Across 40 consumer categories, Stackline found that shoppers are turning to AI far more heavily in some purchase decisions than others. The difference is not simply price, purchase frequency, or how specific the shopper’s question is. It comes down to something more fundamental: how much help shoppers need defining what to consider before they are ready to search for a product.

That distinction matters because it points to where AI has its greatest influence on product discovery. In categories where shoppers need to compare features, interpret tradeoffs, or translate a need into a product type, AI is already taking on a larger role relative to retailer search.

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AI engagement looks very different by category

Across the latest 52 weeks, AI question volume represented 44.5% of retailer search volume for Cameras, the highest level among the 40 categories analyzed. Air Conditioners & Accessories followed at 35.7%, while Lawn Mowers, Strollers, Grills, and Laptops also ranked among the categories with the highest relative AI engagement.

At the other end of the spectrum, Bottled Beverages registered 2.1%, Coffee 2.9%, Snack Foods 3.4%, Cat Food 3.6%, and Dog Food 3.7%.

The spread is substantial, but a lower index does not mean AI is less important to a category. Instead, AI plays a different role relative to retailer search. In some categories, shoppers turn to AI more heavily while they are still defining the purchase. In others, AI supports more specific moments of discovery, comparison, validation, or product education alongside a much larger base of retailer search activity.

Price offers one possible explanation, but it does not hold across the full dataset. Purchase frequency does not either. Some expensive categories rank highly, while others do not. Some replenishment categories sit toward the lower end of the index, yet Multivitamins reaches approximately 11%.

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AI has an advantage before shoppers know exactly what to search for

Across the higher-index categories, shoppers often face decisions that depend on multiple attributes, fit requirements, technical specifications, or intended use. Cameras, air conditioners, strollers, printers, and similar categories require more than identifying a product type; shoppers often need to understand which criteria matter before they can narrow the field.

That gives conversational AI a natural role earlier in the shopping process. Retailer search works especially well once shoppers know what they are looking for. AI can help with the step before that: identifying which product type, features, specifications, or tradeoffs should matter in the first place.

AI plays its largest relative role when shoppers need help turning a need into a defined consideration set.

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AI plays different roles across shopping decisions

Lower-index categories still generate detailed, high-intent AI shopping activity. Shoppers use AI to navigate nutritional requirements, skin concerns, product preferences, household performance, and other specific needs.

The difference is less about whether AI matters and more about where it fits into the decision. In familiar or frequently purchased categories, shoppers already understand the basic purchase framework before turning to AI. Conversational tools can then help compare options, validate a choice, explore a new need, or refine an existing consideration set.

In other categories, AI becomes useful even earlier, helping shoppers establish that framework itself: which attributes matter, which product type fits the use case, and what belongs in the consideration set.

Both represent meaningful roles for AI, and the balance between them changes by category.

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Related categories reveal the difference more clearly

The pattern becomes especially clear when closely related categories are compared.

Across baby products, relative AI engagement rises from Diapers and Bottles to Car Seats and Strollers, categories that typically require more evaluation of fit, safety, features, and use case.

A similar contrast appears with Coffee and Coffee Makers. AI question volume was equivalent to 2.9% of retailer search volume for Coffee, compared with 12.2% for Coffee Makers.

The products participate in the same broader consumption occasion, but the decisions look very different. Coffee purchases can follow established preferences around brand, format, roast, or flavor. Coffee makers require shoppers to navigate brewing systems, capacity, footprint, features, price, and use cases.

Across these related categories, AI accounts for a larger share of shopping activity relative to retailer search as shoppers face more attributes to evaluate, more tradeoffs to resolve, or more uncertainty about which product best fits the need. That does not diminish AI’s role in lower-index categories; it points to different ways conversational shopping can enter the journey.

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Multivitamins broaden the picture

Multivitamins show that decision complexity is not limited to expensive or infrequent purchases.

AI question volume reaches approximately 11% of retailer search volume, even though the category is highly replenishment-driven. Shoppers still need to navigate life stage, formulation, dietary needs, potency, format, and desired outcomes. The purchase repeats, but the decision framework can remain complex.

AI's role grows wherever shoppers need help translating personal requirements into product attributes, regardless of how often the product is purchased.

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AI shopping activity is accelerating, but retailer search remains critical

The pattern is also developing quickly. AI question volume is growing far faster than retailer search across many of the categories analyzed. Television questions increased more than 770% year over year, Air Conditioners more than 640%, Laptops more than 530%, and Printers & Scanners more than 430%, while retailer search moved much more modestly across those categories.

Those differences do not mean AI is replacing retailer search. The two channels serve different roles, and growth in one cannot be directly attributed to movement away from the other. Instead, the data points to an expanding research layer in the shopping journey, one that is especially important when consumers need more interpretation, education, and comparison before they know what to buy.

For brands, the implication goes beyond measuring whether products appear in AI results. The more important question is what role AI plays in the category’s shopper journey. In some categories, AI helps build the consideration set from the ground up. In others, it helps shoppers refine, compare, or validate choices they already understand.

Stackline tracks AI question volume against retailer search at the category level, giving brands a way to see this shift as it happens rather than after a quarter closes. That level of detail shows not just whether a category is high- or low-index today, but how fast the balance is moving, which is where the next round of consideration-set battles will be won.

As AI shopping activity continues to grow, both moments matter. The opportunity is not limited to the categories where AI over-indexes today. It is understanding where AI enters the decision, what shoppers need from it, and how brands can earn consideration at that moment. Brands ready to see where their categories fall on this index can explore the full data set with Stackline.