Agentic Commerce Is Changing How Shoppers Buy Groceries

From Product Visibility to Personal Health Goals

The grocery industry has mastered the logistics of delivery and digital storefronts, yet it has failed to solve the fundamental cognitive load of shopping. While consumers can now have meals delivered to their doors or instantly repurchase previous orders, the burden of deciding what to buy remains entirely on the shopper. This paradox highlights a critical gap in modern retail: technology has optimized for product visibility while leaving the complex task of curation untouched. Neomi co-founders Dmytro Lylyk and Vladyslav Mehera argue that the next evolution in commerce must shift focus from shelves to people. Their approach uses AI-driven personalization to construct shopping baskets based on health goals, dietary restrictions, and life context, rather than relying on isolated product searches.

McKinsey’s 2026 grocery research underscores this demand for change. The data reveals that 70% of consumers prefer home delivery, with 67% citing time savings as the primary motivator. However, the deeper opportunity lies in personalization. Nearly 55% of consumers seek retailers that provide personalized nutrition recommendations, indicating a market ready to trade search effort for expert guidance. McKinsey identifies AI as a transformative force capable of reshaping discovery, suggesting that browsing may soon become less necessary if systems can anticipate needs before they are explicitly stated.

Redefining the Shopping Basket with Contextual AI

Lylyk and Mehera built Neomi on the premise that current retail strategies are overly focused on packaging and placement. They believe the starting point of commerce should be the human being, not the item being sold. “Our philosophy is around foods for health, not for shelves,” Lylyk explains. “Because currently, retailers and brands optimize their strategies around packaging, placing themselves on shelves to be noticed, to be picked.”

This perspective challenges the traditional catalog search model. Instead of forcing shoppers to navigate endless SKUs, Neomi interprets signals such as physiological needs, emotional states, and personal wellness goals. For example, a user planning a romantic dinner might request a meal solution without specifying every ingredient needed. The AI understands the broader context and includes complementary items, such as wine, which were not explicitly searched for but are relevant to the intent. “We can provide them with a broader, more contextual search which adds products which really help,” he says. This method treats the grocery basket as an expression of intent, aligning purchases with the shopper’s actual lifestyle rather than their immediate keyword queries.

The Future of Retail Media and Relevance

The rise of intent-based shopping also disrupts the traditional model of retail media. Historically, promoted products have relied on buying visibility regardless of relevance. Neomi’s founders argue that AI introduces a stricter standard where promotion must follow genuine fit. If an AI system influences a basket, it cannot simply push sponsored items into the cart if they contradict the shopper’s stated intentions or expectations. “AI could push products into the cart just because they are promoted, but they wouldn’t fit the cart, or they wouldn’t fit the expectation,” he says. “That’s the borderline which must not be crossed.”

This shift places a premium on relevance over reach. Brands must answer a new question: Where does a product fit within an actual human need? By prioritizing contextual alignment, retailers can maintain trust while still integrating advertising. This approach ensures that promotions enhance the shopping experience rather than cluttering it with irrelevant choices.

Teaching Shoppers to Communicate Intent

Implementing this model requires a behavioral shift among consumers. Mehera, whose background spans neuroscience and machine learning, observed that users initially interacted with Neomi like conventional search engines, asking for specific items one by one. Over time, however, shoppers began to adapt, learning to describe weekly eating habits, preferred cuisines, and dietary objectives. This transition represents a new form of digital literacy where users communicate comprehensive needs rather than isolated requests.

Mehera views this as a learned behavior that will take time to mature. As the ecosystem evolves, shoppers may communicate their food needs through various touchpoints, including retailer websites, recipe platforms, and diet applications. Technology will act as the translator, converting these broad intentions into coherent product lists. Lylyk and Mehera urge retailers to begin testing intent-based shopping now, recognizing that the future of grocery commerce depends on technology’s ability to understand the person behind the purchase.