AI moves into the grocery aisle
New research shows that AI has moved from abstract hype into the most routine part of retail: buying groceries. According to Rithum, 36% of shoppers have used AI to help buy groceries in the past six months, and 28% have already completed a grocery purchase with the help of AI tools. The primary use cases are price discovery and decision support: 66% of these shoppers use AI to compare prices or weigh up options before buying, 47% use it to research product information.
This shift aligns with a broader pattern: AI is becoming a mainstream discovery and decision layer in retail. McKinsey estimates that AI-powered search could influence around $750 billion in revenue by 2028, while an IBM–NRF study reports that 41% of grocery shoppers use AI to research products, 33% to interpret reviews, and 31% to hunt for deals. Grocery and consumer packaged goods are among the leading categories in AI-driven purchases, indicating that this is not a niche experiment but a structural change in how everyday shopping decisions are made.
From search engines to AI agents: a new discovery layer
The core change is not just that shoppers “also use AI,” but that AI tools and large language models are increasingly competing with traditional discovery channels: search engines, marketplaces and retailer websites. When a shopper asks an AI agent where to find the best price on a specific product, the agent becomes the first point of interaction and filters which offers and retailers are even considered.
In this model, the classic funnel is inverted. Instead of a shopper navigating category trees and filters, an AI system pre-aggregates information, evaluates options against user constraints (price, delivery time, dietary restrictions, brand preferences), and presents a narrowed set of candidates. Product feeds, catalog quality and pricing logic no longer work only “downstream” (inside retailer systems); they must be optimized for upstream consumption by AI agents that continuously crawl, normalize and compare offers across the market.
For e-commerce and SaaS ecosystems, this effectively turns AI agents into a new kind of meta-marketplace: not owning inventory, but owning attention and decision logic.
Why this matters for product feeds
If more than a third of grocery shoppers already involve AI in purchasing decisions, the operational implications for product feeds become immediate:
- Feeds are no longer only a channel to marketplaces and ad platforms; they are input data for AI-driven comparison and recommendation engines.
- The dominant AI use cases in grocery—price comparison, option evaluation, deal hunting—are highly sensitive to feed quality and latency.
Four dimensions of product feed readiness become critical:
Granularity and structure
AI systems rely on machine-readable, standardized attributes to compare alternatives: unit price, pack size, weight, nutritional values, allergens, origin, expiry or freshness windows, delivery promises, and promotion rules. Incomplete or unstructured fields limit the agent’s ability to evaluate options and may cause an offer to be down-ranked or ignored in AI responses.Accuracy and consistency
If a shopper asks an AI for the best deal on a specific product, the agent will reconcile multiple sources: merchant feeds, public product data, user reviews, and historical price information. Inconsistent prices across channels, misaligned pack sizes, or ambiguous naming create conflicts that either require the agent to ignore an offer or to treat it as lower confidence. In a world where AI filters most of the catalog, “low confidence” often means “not shown.”Latency and update frequency
Grocery pricing is dynamic: promotions, loyalty offers, and surge demand shift prices in short cycles. For AI-based comparison to return up-to-date answers, product feeds must support high-frequency updates and clear promotion logic. Delayed or batch-only feeds risk making offers look uncompetitive when agents compare them to fresher data from other sources.Coverage across assortment
With AI, long-tail SKUs gain visibility if their data is robust. However, if only a subset of the catalog is fully structured and enriched, agents will disproportionately favour those SKUs and comparable competitor products. This creates pressure to raise the “minimum viable completeness” level across the entire assortment, not just for hero SKUs.
For SaaS vendors in feed management and PIM, this trend turns data quality from a cost-control topic into a growth driver: the richer and more consistent the feed, the more often products surface in AI-assisted shopping journeys.
Catalog standards under AI scrutiny
The rise of AI-driven grocery discovery amplifies the importance of catalog standardization. Where human shoppers can sometimes compensate for messy naming, AI agents depend heavily on consistent taxonomies and attributes to interpret and compare products.
Several shifts are already visible or logically follow from this trend:
Convergence around attribute schemas
To compare offers across retailers, AI models effectively construct cross-merchant attribute maps. The closer a retailer’s internal taxonomy is to emerging de facto standards (in naming, units, and categorization), the less normalization work the model must do, and the fewer errors or ambiguities it introduces. This raises the value of adopting and maintaining unified schemas across internal systems and external channels.Increased importance of canonical identifiers
Consistent use of global identifiers (e.g., GTINs) or stable internal IDs mapped across systems helps AI agents match offers to the same underlying product. Where codes are missing or fragmented, the agent must rely on fuzzy matching using titles, brands, pack sizes and images, which is error-prone in grocery (small naming differences, private labels, local brands). Reliable identifiers increase the chance that a retailer’s offer is correctly grouped and compared.Normalization of units and measures
Many AI use cases in grocery hinge on true price comparison per standardized unit (per kilogram, litre, piece), as well as on nutritional and ingredient comparisons per serving or per 100 g/ml. Catalog standards that enforce consistent unit representation and conversion rules directly support more accurate AI-driven recommendations.Explicit encoding of complex attributes
Requirements such as dietary suitability (vegan, halal, gluten-free), allergen presence, organic certification, or sustainability scores must move from marketing text into structured fields with predictable values. Without that, AI agents either skip these dimensions or infer them from descriptions and packaging images, with limited reliability.
As AI agents become a default intermediary between consumers and products, catalog standards are no longer an internal housekeeping exercise; they become an external competitive factor that shapes which products are deemed relevant to a given prompt.
Product content quality: from human-readable to model-ready
The research shows that shoppers use AI both for rational tasks (price comparisons, deal hunting) and informational ones (product research, review interpretation). This places new demands on product content.
Three layers of content quality gain importance:
Core factual accuracy
Product titles, descriptions, and key attributes must be strictly aligned. Any mismatch between description and structured data (e.g., a “sugar-free” claim vs. nutritional table) becomes a potential source of confusion for AI, which aggregates information from multiple fields and external sources. Factual conflicts can cause conservative models to avoid recommending the product.Semantic richness without redundancy
AI models benefit from descriptions that clearly express use cases, form factors, packaging details, and differentiating features, but that avoid marketing noise and uncontrolled keyword stuffing. Overly promotional text can obscure the underlying facts the model needs to match a product to a shopper’s explicit or inferred intent.Alignment across languages and locales
In cross-border or multilingual environments, AI systems often synthesize product information across language versions. Inconsistent translations of ingredients, allergens or usage instructions can introduce risk or misclassification. This makes centralized, model-consistent content governance more important: one canonical source of truth, propagated via APIs to all storefronts and feeds.
For content operations, this accelerates the move toward structured, component-based product content, where descriptive elements (features, benefits, use cases) are templated, centrally managed and exposed in a way that is easy for both humans and models to parse.
Speed to shelf: AI as an accelerator and a new bottleneck
The spread of AI-assisted shopping also affects how quickly new products must appear with complete, reliable data.
On one hand, AI and no-code tools significantly shorten time-to-market:
- Automated enrichment: Models can generate draft descriptions, feature bullets, and basic categorization for new SKUs based on supplier data, packaging images and existing taxonomies.
- Smart validation: AI can flag missing critical attributes (e.g., allergens, net weight), inconsistent units, or conflicting claims before a product goes live.
- Workflow automation: No-code platforms allow business teams to define rules for content approval, feed mapping and channel-specific transformations without waiting for engineering changes.
On the other hand, AI discovery raises the bar: a product that is technically live but poorly enriched, miscategorized or inconsistently priced risks becoming invisible in AI-mediated journeys. “Speed to shelf” becomes “speed to AI readiness”: not just how fast a product can be listed, but how fast it can be listed with enough structured, accurate data to be reliably surfaced by agents.
This creates a new optimization problem for e-grocery and CPG:
- How to minimize the gap between listing a SKU and reaching a threshold of AI-ready content completeness.
- How to design PIM and feed workflows so that AI-based enrichment and validation are built-in, not added post-factum.
- How to coordinate between suppliers, internal teams and external channels so that critical attributes are available from day one.
No-code and AI in content infrastructure
The data points on consumer behavior imply that AI is now a critical external interface; internally, the same technologies are reshaping content and catalog operations.
In content infrastructure, several patterns are emerging:
AI-assisted PIM and catalog ops
AI models are increasingly embedded into product information management systems to automate classification, attribute extraction from supplier docs and images, and cross-channel mapping. This is especially relevant in grocery, where thousands of near-duplicate SKUs differ in size, flavour or packaging, and manual handling is costly and slow.Rule-based and AI-augmented feed management
No-code rule builders allow merchandising and pricing teams to set complex feed rules (e.g., promotion eligibility, channel-specific assortment, fallback images) without developer intervention, while AI modules suggest optimal attribute mappings or detect anomalies. This combination keeps feeds current enough to be trustworthy inputs for AI comparison tools.Continuous content monitoring
Since AI agents surface issues that might have gone unnoticed in classic web analytics (e.g., misaligned net weight leading to unfavourable per-unit price), retailers are beginning to treat product content as a living system that requires continuous monitoring. AI-powered QA checks can review catalog changes at scale and simulate “agentic queries” to see which products are returned and why.
In practice, this pushes e-commerce stacks toward modular architectures: core product and pricing data in centralized services, surrounded by specialized SaaS tools that handle enrichment, validation and feed orchestration, all with AI support.
Competitive implications for retailers
With more than a third of grocery shoppers already involving AI in the buying process, competition is gradually shifting from being visible in search results to being selected by AI agents as one of the “best answers” to a shopping prompt.
Several competitive dynamics follow:
Price transparency intensifies
If AI agents aggregate and normalize prices across channels, superficial promotions that rely on complexity or obscurity lose effectiveness. True value (price per unit, total basket cost including fees) becomes more visible, forcing pricing and promotion strategies to be coherent and data-driven.Differentiation via data quality
Two retailers offering similar prices can be treated very differently by AI if one provides richer, more consistent product and availability data. Reliability of delivery windows, clear substitution policies, and accurate stock data can become differentiating factors that models learn to prioritize.Localization of decision logic
Groceries are highly local: assortment, pricing, and delivery capabilities vary by region. For AI agents to give precise recommendations, they must access localized feeds and constraints. Retailers able to expose this granularity via APIs and standardized feeds are better positioned to feature in hyper-local AI recommendations.
At the strategic level, this means treating AI not only as an internal optimization tool, but as an external channel with its own “SEO” equivalent: optimizing product data, pricing signals and operational reliability so that AI agents consistently surface the retailer’s offers in response to relevant consumer intents.
The next phase of AI-native commerce
The current numbers—36% of shoppers using AI to help buy groceries, strong usage of AI for product research and deal hunting—indicate an inflection point rather than an endpoint. As AI interfaces become embedded into devices, cars, voice assistants and retailer apps, the distinction between “classic” online shopping and AI-assisted shopping will blur.
For e-commerce and SaaS providers, the strategic question is less whether consumers will use AI in shopping—they already do—and more how quickly product data, catalog standards, and content operations can adapt to a world where AI agents intermediate a significant share of purchase decisions.
In that world, the foundational assets are clear, machine-readable product data; robust, AI-ready content infrastructure; and flexible, no-code-enabled workflows that can keep pace with both consumer expectations and model capabilities. Groceries, by virtue of their frequency and complexity, are simply the first category where these pressures become impossible to ignore.
Here at NotPIM, we recognize the immediate impact of AI on e-commerce. This shift towards AI-driven shopping amplifies the critical need for high-quality, standardized product data. Without clean, consistent data, products risk being overlooked by AI agents. Our platform empowers businesses to enhance their product feeds through automated enrichment and validation, ensuring their products are easily discoverable and competitive within the AI-driven landscape.