Shoppers Act On Generative AI Recommendations, Shifting Purchase Behavior

As generative AI tools become more embedded in online shopping platforms, consumers are increasingly relying on them to guide purchasing decisions. Adobe’s latest research shows a significant rise in the number of consumers who say they’ve bought products based on AI-generated suggestions. The shift is beginning to reshape how shoppers discover products, from general household goods to groceries and restaurant items.

For foodservice operators and industry buyers, the growing trust in AI-driven recommendations signals a new factor influencing demand — and potentially a new channel for menu planning, sourcing, and product positioning.

Adobe’s 2024 survey of over 1,000 U.S. adults found that 40% of online shoppers had purchased a product recommended by a generative AI tool, up from 32% the year before. The majority of those shoppers (64%) reported being satisfied with their AI-assisted purchases. Notably, the research found that AI-influenced buying wasn’t limited to tech or niche categories. A wide range of items — including food and beverages — are increasingly discovered through tools like ChatGPT, retail bots, and AI-powered shopping assistants.

For restaurant groups and foodservice distributors, this trend signals a shift in how consumers are exposed to and select food items. As AI tools begin surfacing specific brands or product types in response to user prompts (e.g., “What should I cook for dinner with salmon?”), operators should consider how their offerings align with searchable product descriptions, dietary tags, and recipe relevance.

A separate report from Retail Dive highlights how OpenAI’s ChatGPT is becoming more “shopper-friendly” through plug-ins and integrations that help users browse products in real time. Retailers such as Instacart and Shopify are embedding these capabilities, enabling users to go from recipe suggestions to checkout in a few clicks.

For foodservice professionals, this development presents both opportunities and challenges:

• Product discovery is becoming conversational. AI prompts may dictate what ingredients, snacks, or beverages consumers are introduced to.

• Private label visibility could expand or contract. Depending on how AI tools are trained, branded versus generic items may see fluctuating exposure.

• Speed-to-cart is shortening. Faster AI-assisted decision-making could shift how often consumers explore menus or try new items outside their comfort zone.
  
  

Operators may need to revisit how their product listings, digital menus, and e-commerce descriptions are structured to align with natural language queries.

As more consumers trust AI to guide their purchases, foodservice brands must prepare for downstream effects on sourcing and marketing. Distributors may see increasing demand for SKUs that align with health trends, convenience, or dietary preferences commonly queried by AI users. On the marketing side, descriptive accuracy and language alignment with AI prompt structures could become key to visibility.

Practical considerations include:

• SEO-optimized product descriptions that match how consumers phrase questions to AI tools.

• Recipe and usage suggestions that AI can extract or highlight.

• Menu design that supports AI-guided food discovery, especially on third-party apps.
  
  

Even though current AI platforms do not guarantee exposure or placement, early alignment with their input-output logic may influence consumer behavior in measurable ways.

While generative AI tools are not replacing human shopping preferences, they are clearly altering how and where discovery happens. For foodservice leaders, the takeaway is clear: visibility in AI-driven environments may soon be as important as placement on a shelf or in a digital menu. By anticipating how these tools shape demand patterns, operators and distributors can position themselves to respond with agility, and potentially lead the curve.