AI Grocery Shopping: Will Assistants Pick Your Brand?

AI Grocery Shopping: When the Assistant Writes the List

AI grocery shopping has arrived faster than most consumer goods companies planned for. NIQ's latest Consumer Outlook, based on more than 21,000 consumers across 31 countries, found that 55 percent of shoppers say AI recommendations at least occasionally influence their household and grocery purchases, and 27 percent have used an AI assistant to research or decide what to buy in the past three months. As one NIQ executive put it, AI is beginning to shape what consumers see before traditional brand influence gets a chance. For a category built on habit, shelf position, and decades of advertising, that is a structural change. When a shopper asks an assistant for pasta rather than a particular brand of pasta, the assistant decides.

Shelf, Search, and Assistant: How Brands Win Each

The rules that put a brand in the basket differ sharply between the three places shoppers now choose.

What the New Data Shows

The NIQ numbers are worth reading carefully. Fifty-five percent saying AI "at least sometimes" influences purchases is not the same as fifty-five percent buying groceries through a chatbot. Influence includes a meal plan generated by an assistant, a recipe that names ingredients, or a quick question about which detergent works best in hard water. Much of that influence happens before the shopper opens a retailer app at all, which is exactly why it is hard for brands to see.

The industry is not yet set up for it. Research from the Consumer Goods Forum and Boston Consulting Group suggests around three quarters of consumer goods companies are still in an exploring phase with AI, and fewer than one in five are scaling real impact. Most of that effort points inward, at forecasting, marketing production, and supply chains. Far less of it addresses what assistants say to shoppers about the brand's products.

How AI Grocery Shopping Changes the Basket

Grocery is a list business. Shoppers rarely browse for laundry detergent. They write "detergent" on a list and pick the familiar pack when they see it. AI changes the step between the list and the pack. A shopper can ask for a week of dinners under a budget, a basket for a gluten-free household, or the best value nappies for a newborn, and the assistant translates those needs into specific products.

Why AI Grocery Shopping Weakens Brand Habit

Habit depends on the moment of recognition at the shelf. An assistant removes that moment. It answers the shopper's stated criteria, and unless the shopper asks for a brand by name, it has no particular reason to prefer the one they bought last time. Criteria such as price per unit, dietary needs, ingredients, and ratings are things assistants can compare easily, and they often favour retailer own brands that compete hard on value. In our view this is the single biggest risk for mid-tier brands whose loyalty rested more on familiarity than on a clear functional difference.

The Product Data Problem

Assistants recommend what they can understand. For grocery that means structured, accurate, consistent product information, and many brands have less control over it than they assume. Information about the same product often differs between the brand site, each retailer listing, and third-party databases. The details that matter most to assistants are frequently the ones that are wrong or missing:

• Pack size and unit price. Without clear sizes, assistants cannot compare value fairly and may skip the product.
• Ingredients and allergens. Missing or outdated lists can exclude a product from any dietary request.
• Claims such as vegan or gluten-free. These need to be accurate and consistent, or assistants may repeat an error.
• Variants and pack changes. Discontinued sizes and old formulations linger in listings and confuse recommendations.
• Availability by retailer. An assistant that cannot confirm where a product is sold may recommend one it can.
• Reviews and ratings. Thin or outdated reviews weaken a product against well-reviewed alternatives.

Claims deserve particular care, because assistants repeat them in their own words. Environmental claims in particular carry rising regulatory risk, which we covered in green claims and AI product copy.

A Seven-Step Plan for FMCG Brands

1. Audit what assistants recommend. Run common shopping questions in your categories across several assistants and record which brands appear.
2. Fix product data at the source. Correct sizes, ingredients, allergens, and claims in the master data that feeds retailers.
3. Chase listing consistency. Work with key retailers to make sure their listings match your current range and packs.
4. Make functional differences explicit. If your product is better for hard water or sensitive skin, say so clearly and back it up.
5. Strengthen reviews. Encourage genuine reviews on the retailers that matter, especially for newer products.
6. Model private label substitution. Identify where an assistant would reasonably suggest an own-brand equivalent and decide how to respond.
7. Repeat quarterly. Assistants, retailer integrations, and ranges all change, so a single audit goes stale quickly.

Pros and Cons for Consumer Goods Brands

• Pro: merit can beat shelf power. A genuinely better product with clear data can win recommendations without dominant shelf space.
• Pro: new occasions. Meal planning and needs-based requests can introduce shoppers to products they never browsed.
• Pro: clearer feedback. Seeing why an assistant prefers a competitor highlights real gaps in information or positioning.
• Con: weaker habit. Familiarity counts for less when the shopper never sees the pack before choosing.
• Con: private label pressure. Value comparisons favour own brands that compete on unit price.
• Con: limited control. Brands cannot buy their way into an organic recommendation the way they can buy a shelf end.

Real Scenarios Worth Thinking Through

These scenarios are illustrative, showing how AI grocery shopping plays out in practice rather than presented as verified case studies.

A cereal brand asks several assistants for a high-fibre breakfast for children and finds it missing from every answer, despite a strong fibre content. The reason is mundane: retailer listings carry an old nutrition panel from before a reformulation. Correcting the data puts the product back into recommendations within a few months.

A household cleaning brand discovers that when shoppers ask for the best value dishwasher tablets, assistants consistently name a retailer's own brand, citing price per wash. The brand cannot win on price, so it makes its performance claims specific and evidenced, and assistants begin recommending it for heavily soiled loads.

A snack brand launches a new flavour and sees strong sales in stores but almost none through assistant-led online baskets. The product has no reviews yet and appears on only one retailer's site, so assistants rarely suggest it. A sampling programme focused on early reviews changes that.

“After testing multiple AI models on coding, research, and business prompts, combined outputs produced more reliable results than any single model.” Internal multi-model evaluation, Talkory research team.

Retailers Hold the Keys

Much grocery AI runs inside retailer apps, not general assistants, and retailers have their own commercial priorities, including own-brand margins and retail media income. That makes the joint business plan with each retailer as important for AI recommendations as it has always been for shelf space. Ask how the retailer's assistant chooses products, what data it uses, and whether sponsored signals influence it.

General assistants are increasingly connected to retailers too, which raises the access questions we discussed in AI agent traffic for retailers. And because each model draws on different sources, what one assistant says about your brand may differ from the next, a point covered in AI search visibility.

Why Talkory Wins

The fastest way to understand your exposure is to ask the questions shoppers ask and see what comes back. Talkory runs the same question across GPT, Claude, Gemini, Grok, Perplexity Sonar, and Kimi K3 in one pass. A category team can ask for the best value laundry detergent, a gluten-free breakfast, or a family dinner basket, and see in one view which brands each model recommends and why. Where all six favour a competitor, that is a real gap in positioning or data. Where models disagree about your product's ingredients or claims, that usually points to inconsistent information somewhere upstream that is worth fixing.

Final Verdict

AI grocery shopping does not replace the shelf, but it is quietly moving the moment of choice to a place where brand habit counts for less and product information counts for more. More than half of shoppers already feel its influence, while most consumer goods companies are still exploring AI internally. The brands that hold their place will be the ones with accurate, consistent product data, clear functional differences, healthy reviews, and a regular view of what assistants actually recommend. The list still says pasta. Make sure the assistant has a reason to choose yours.