Concept visual: Wild Bite Club.
Consumers are beginning to ask grocery assistants for attributes rather than products. That turns ingredients, allergens and availability into a new kind of shelf space.
A shopper no longer has to type the name of a snack. The question can be much fussier: find something crunchy, nut-free, high in protein, available nearby and cheap enough for a weekday. That sentence contains an occasion, a dietary restriction, a texture, a nutrition target, a price ceiling and an inventory check. It also contains a quiet demotion. The brand name has disappeared.
This is the reversal now reaching the American snack aisle. Packaged-food companies spent a century teaching shoppers to ask for products by name. Grocery assistants are teaching them to describe a problem instead. Conagra Brands says consumers are increasingly using artificial intelligence to find snacks with particular benefits, while Instacart has placed a conversational assistant inside the shopping journey. The machine does not walk the aisle dazzled by a familiar logo. It tries to match attributes.
The immediate winners seem obvious: protein bars, fibre-rich foods and anything that can answer a functional brief. The less obvious consequence is that product data is becoming a second package. A snack can be perfectly visible on a physical shelf and still vanish from the answer if its ingredients, allergens, nutrition, pack size, price or local availability are poorly described.
The search box used to reward memory
Traditional grocery search is largely a test of recall. A shopper enters a category, a flavour or a brand, then sorts through a list. Retail media made that list commercial territory: manufacturers paid for prominent placement, retailers arranged digital shelves, and brand recognition shortened the route to checkout.
Conversational shopping changes the unit of demand. Instacart says its new assistant can turn natural-language requests, a photograph of a handwritten list or an occasion such as a football gathering into a ready-to-buy cart grounded in live inventory. It can also use previous orders and dietary preferences. The company says the tool is available to millions of US customers and is planned for wider rollout across the United States and Canada.
That does not abolish brands. Instacart explicitly says the assistant can learn the brands a household already prefers. But it inserts a new gate before brand choice. When the request begins with “high-protein afternoon snack under $5” rather than a trademark, every qualifying product competes to become the answer.
There is evidence that the underlying requests are moving in this direction even without an assistant. Instacart’s analysis of platform activity in the first half of 2026 found that the share of searches containing “protein” rose 12.7% year on year; “fibre,” from a smaller base, rose 26.4%. Protein drinks, bars and cottage cheese also gained share of items sold. Those figures describe one platform, not the entire grocery market, but they show shoppers increasingly entering the aisle through a nutrient rather than a brand.
A $198 billion aisle learns to answer questions
Conagra’s 2026 Future of Snacking study provides the larger commercial backdrop. The company, working with Circana data, describes a US snacking market worth $198.2 billion and says its research analysed more than 53 million transactions across 17,000 products. It combined sales and eating occasions with search activity, restaurant menus, digital behaviour, social engagement and AI conversations.
The result is not a single march toward virtue. Conagra groups current demand around bold flavour, functional fuel and permissible indulgence. Functional products associated with protein, energy, digestion or hydration reached $19 billion in retail sales, according to the company, while snacks with recognisable ingredients and simpler formulations represented about $69.4 billion. People still want pleasure. They increasingly want the pleasure to carry a job description.
Circana’s separate Global Snack Unwrap makes the same point from another angle. It reports that 55% of US shoppers eat three or more snacks a day, nine percentage points more than in 2021. As snacks replace parts of meals, the question becomes less “which treat?” and more “what will get me through the afternoon?” Circana’s advice to manufacturers is unusually direct: optimise product data so brands appear in AI-driven results.
That sentence is the hinge. Food companies have long optimised packaging for a person standing a metre away. Now they must also make the product legible to a system assembling an answer from databases. The front of pack still needs appetite appeal. The invisible fields behind the listing need precision.
The new package has no front
For a recommendation system, “healthy” is an awkward word. “Contains 12 grams of protein, no peanuts, a 40-gram pack and stock at the selected retailer” is usable. The more specific the question, the more structured the product record must become.
This turns mundane catalogue work into merchandising. Ingredients have to be current. Allergen declarations must be unambiguous. Nutrition values need to correspond to the correct serving and variant. Availability must reflect the local store. A product described only as a delicious, wholesome pick-me-up gives the machine little to compare. Marketing poetry is not data.
The competitive implication is uncomfortable for large brands. Scale once helped them dominate attention through advertising and shelf space. Attribute-based discovery can give a smaller product a route into the cart if it answers the brief more exactly. A regional jerky, seed mix or pulse snack does not need to be the name the customer remembers; it needs to be the item the system can verify.
Yet the same mechanism can reinforce incumbents. Large manufacturers can afford better catalogue operations, retailer integrations, claim substantiation and constant updates. They also possess years of transaction data and portfolios broad enough to answer many occasions. The virtual shelf is not automatically democratic. It rewards whoever can make products both relevant and machine-readable at scale.
The assistant may preserve the brand after all
The strongest case against a dramatic reversal is simple: the evidence does not yet show that AI caused the sales shift. Conagra’s public material says it examined AI conversations, but it does not disclose how many snack questions were asked, how often recommendations were accepted or whether those conversations led to purchases. The 53 million transactions establish what sold. They do not establish why it sold.
Protein and fibre were already gaining attention through GLP-1 use, social media and conventional health marketing. Instacart’s own data show nutrient terms rising in ordinary search, which could explain much of the same behaviour without conversational assistance. AI may be a new window onto existing demand rather than the engine creating it.
There is another limit. Personalisation can protect familiar brands. Instacart says its assistant draws on order history and established preferences. A shopper who repeatedly buys the same popcorn may receive that popcorn again, now wrapped in the language of convenience. Recommendation systems can reduce discovery as easily as expand it.
Research on AI recommendations for functional foods also suggests that transparency matters. Personalisation can lift perceived value, but shoppers still judge health benefits, naturalness and the credibility of the recommendation. Food is not an abstract search result: allergens, taste, texture and trust make a wrong suggestion costly. If answers feel sponsored, inconsistent or unsafe, users may return to filters, labels and known brands.
The thesis would weaken if conversational grocery tools remain a niche planning feature, if assistants mostly reproduce past purchases, or if retailers do not report meaningful conversion from recommendations. It would strengthen if platforms publish rising usage and acceptance, manufacturers expand product-data teams, and sales shift toward products that precisely match multi-attribute requests despite low initial brand awareness.
From slogan to specification
Observed: Conagra and Circana report growth in functional snacking and increasingly attribute-led discovery. Instacart has launched an assistant that turns open-ended needs into live, shoppable carts, while searches for protein and fibre are increasing on its platform.
Emerging: snack discovery is moving from a keyword list toward a negotiated brief. In that environment, accurate product attributes operate like virtual shelf placement. Brands must win both the human glance and the machine’s comparison.
Possible next step: manufacturers may begin designing products and data together—formulating for a need, then ensuring every claim, allergen, serving and availability field can be evaluated by shopping assistants. Retailers may sell visibility inside those answers, creating a new advertising market and a new trust problem. Neither outcome is yet an established standard.