The paradox of modern grocery retail
Despite the rise of app-based ordering and home delivery, grocery shopping still forces consumers to make countless decisions about what to buy. McKinsey research shows that 70% of shoppers prefer home delivery for online grocery orders, with 67% citing time savings as their primary motivation. Yet the fundamental challenge—deciding which products belong in the basket—remains largely unsolved by the convenience revolution.
This gap has caught the attention of Dmytro Lylyk and Vladyslav Mehera, co-founders of Neomi, an AI shopping assistant. They argue that grocery commerce has spent decades perfecting product visibility through packaging, shelf placement, and digital merchandising, while neglecting the person actually making the purchase. The pair contend that technology should invert this logic entirely.
Starting with the shopper, not the shelf
Lylyk articulates their core philosophy: "Our philosophy is around foods for health, not for shelves. Because currently, retailers and brands optimize their strategies around packaging, placing themselves on shelves to be noticed, to be picked."
The Neomi approach treats the shopper's circumstances as the foundation for basket construction. Rather than asking consumers to search through catalogs or repeat previous orders, the system begins by understanding their needs. Lylyk explains the methodology: "What we do differently is that we understand the needs, both emotional and physiological needs of a human, including information around health, dietary restrictions, preferences and personal goals as signals that can determine what belongs in a basket."
Consumer demand supports this shift. McKinsey's 2026 grocery research found that nearly 55% of consumers want personalized nutrition recommendations from retailers. Almost half actively seek specific functional benefits like high protein or low sugar content. These preferences suggest that shoppers increasingly view their grocery basket as an expression of their broader intentions rather than a series of isolated product choices.
Context as a tool for relevance
AI's capacity to interpret context transforms what becomes possible in grocery shopping. A consumer planning a romantic dinner may know they need a meal without knowing which specific products to purchase. Lylyk describes a Neomi user who selected a "Romantic Dinner for Two" experience and discovered wine automatically included in their basket—a product never explicitly requested, but contextually appropriate. "We can provide them with a broader, more contextual search which adds products which really help," he notes.
This contextual approach also redefines the role of advertising within grocery commerce. Lylyk draws a firm boundary around retail media: promoted products should only appear when they align with the shopper's stated intention. "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."
Teaching shoppers to communicate with machines
Mehera, who brings expertise in neuroscience and machine learning alongside prior grocery technology experience, identified an unexpected obstacle: users initially treated the AI system like a conventional search engine. Observing behavior across 120 department store chains, he found shoppers asking for individual products—milk, then something else—rather than describing their comprehensive needs.
Neomi began teaching users to communicate differently. Mehera notes that descriptions of weekly eating patterns, preferred cuisines, or dietary goals provide the AI with sufficient context to construct more coherent baskets. He views this shift as a learned behavior that will require time to establish across the shopper base.
Reshaping the grocery ecosystem
Lylyk and Mehera envision a future where shoppers communicate their food needs across multiple platforms—retailer websites, AI assistants, recipe platforms, and diet applications—with technology translating those intentions into relevant products. Such a model could fundamentally alter how retailers relate to shoppers and force brands to answer a new question: Where does a product fit within an actual human need?
The founders urge retailers to begin testing intent-based shopping models against their own shoppers and inventory now, while the approach is still developing. Grocery commerce has spent decades perfecting the art of helping people find products, Lylyk argues. The next phase may depend on technology learning to understand the person searching for them.
Source: The Next Web
