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Agentic grocery shopping is emerging, but today’s evidence points mainly to AI helping shoppers plan, research and compare—not routinely choosing and buying an entire grocery basket on their behalf. Retailers should prepare for AI-assisted discovery while treating autonomous checkout as a capability to prove through bounded, well-governed pilots.
What agentic grocery shopping means—and what it does not
An AI shopping tool can do different amounts of work. It might answer a product question, suggest recipes, build a basket from a meal plan or, at the most autonomous end, place an order under rules set by the shopper. Calling every AI feature an “agent” blurs an important distinction: helping with a decision is not the same as having permission to execute a purchase.
For grocers, the practical question is not simply whether to add AI. It is which shopping tasks to support, how much authority to give the system, and how to keep the retailer and shopper in control as the task moves from discovery toward checkout.
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What shoppers are using AI for now
The State of Grocery Retail Europe 2026, published by EuroCommerce and McKinsey & Company in June 2026, reports that about 12% of consumers in its European survey had used AI during their grocery-shopping journey. The report describes a survey of 15,566 adults across 14 European countries; 1,903 responses were relevant to the chart. Among respondents who used AI for grocery shopping, 40% used it to create meal plans, 37% to research specific products, and 36% to find prices or recipes aligned with dietary preferences. These are reports of AI use, not rates of completed purchases by autonomous agents.
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The same survey points to generational differences in stated intent: 28% of Gen Z shoppers said they intended to use more AI for grocery shopping, compared with 21% of millennials, 13% of Gen X and 6% of baby boomers. Intent is not observed adoption, but it suggests that readiness may vary across customer groups.
Other studies reinforce the distinction between using an AI feature and delegating the whole trip. NielsenIQ reported in September 2026 that 51% of U.S. consumers had used at least one AI-powered tool to support shopping in the prior month. Separately, Walmart’s 2025 Retail Rewired research found that 46% of its surveyed respondents were somewhat or very unlikely to let an assistant or agent handle an entire shopping trip. These figures measure different things, in different years and geographies, so they should not be compared as if they describe one population or a single adoption curve.
Why grocery is a harder agent problem than product search
Grocery combines frequent shopping with diverse baskets, many low-price items and demanding last-mile fulfillment. A system that can recommend one product still has to make a much more involved set of decisions to assemble and execute a useful basket.
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In practice, a grocery agent may need to balance price against quality, honor dietary preferences, check item availability, handle substitutions and meet delivery or pickup expectations. This is an operational implication of the market constraints identified in the 2026 EuroCommerce and McKinsey report—not evidence that current agents reliably solve those tasks.
Basket-level errors also have consequences. A poor recommendation for one durable product may be easy to ignore; a wrong ingredient, unavailable staple or unwanted substitute can undermine a meal plan or the whole order. That makes reliable product data and clear shopper rules central to grocery automation, not just technical refinements.
Retailer agents are appearing, but scale is not established
At NRF in January 2026, Google CEO Sundar Pichai said Google was working with Kroger and other retailers on an AI Mode shopping agent for retailers’ apps. He described Gemini Enterprise for Customer Experience as available in preview and characterized the intended assistant as “a knowledgeable sales associate” that can answer detailed questions and offer proactive, personalized recommendations. The announcement is evidence of a retailer-agent initiative; it does not establish broad grocery deployment or routine autonomous order completion.
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Across commerce more broadly, Salesforce reported that traffic referred from AI chats grew between 150% and 428% year over year in each measured quarter, based on behavioral data covering more than 1.5 billion global shoppers. Salesforce says the shift is appearing first in discovery, while fully autonomous purchasing remains early. This is a broad commerce measure, not a grocery-specific adoption or conversion rate.
Together, these signals support a measured interpretation: AI is becoming part of how shoppers discover and assess products, and retailers are testing new interfaces. They do not establish what share of grocery orders is placed autonomously. Consumer use of AI to shop should not be reported as consumer willingness to delegate checkout.
Will AI agents replace grocery apps?
There is not enough evidence to say that agents will replace retailer apps. The likely near-term strategic question is whether an agent becomes another way to enter a retailer’s shopping experience, whether it sits inside the retailer’s own app or site, or whether a third-party platform mediates discovery and basket-building.
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EuroCommerce and McKinsey identify choices for grocers around owning the customer interface or joining third-party agent ecosystems, sharing data openly or retaining it as a proprietary advantage, and accepting more risk in exchange for faster scaling. Those choices affect more than interface design: they shape who owns the customer context, what product and service data are exposed, and how quickly a retailer can learn from interactions.
Strategic choices retailers should make deliberately
| Decision | Option A | Option B | What to weigh |
|---|---|---|---|
| Customer interface | Build an assistant into the retailer’s app or site. | Participate in third-party agent ecosystems. | Consider control of the customer relationship and context against potential reach and interoperability. |
| Data posture | Share catalog and service data to support external discovery. | Retain differentiated data as a proprietary advantage. | Decide what information is useful to expose, what creates a competitive advantage and what safeguards sharing requires. |
| Autonomy | Answer questions and make recommendations. | Build a basket or transact under shopper-defined authority. | Increase authority only when the retailer can make permissions, constraints and actions understandable to the shopper. |
| Risk and scaling speed | Expand quickly across channels. | Validate in bounded pilots before broad rollout. | Balance speed with governance, security, accuracy and evidence of successful outcomes. |
| Operational readiness | Start with discovery and planning. | Support item-level basket decisions and fulfillment. | Assess product-data quality, price and availability accuracy, basket complexity and fulfillment reliability before advancing. |
The interface, data and risk-versus-speed tradeoffs are identified in the 2026 EuroCommerce and McKinsey report. The autonomy, trust and operational-readiness dimensions are practical decision criteria derived from the use cases and constraints described across the cited work.
How grocers can prepare without overpromising
- Separate discovery from transaction execution. Label and measure each stage: product questions, recommendations, basket creation, cart submission and order placement. Do not call an experience autonomous purchasing unless the agent has authority to place the order.
- Make product information useful to machines and people. Review structured product attributes, natural-language descriptions and the feeds used for AI discovery. For grocery, practical priorities include current prices and availability, pack sizes, ingredients, dietary attributes and substitution rules. These are implementation recommendations, not measured outcomes reported by the studies.
- Choose a customer-relationship strategy. Decide whether to develop an assistant in the retailer’s own channels, participate in external agent ecosystems, or test both. Set explicit rules for data access and sharing rather than treating them as an incidental integration detail.
- Make permissions and controls visible. Give shoppers understandable ways to set spending limits, dietary constraints and substitution preferences. Show what the agent has done, what it proposes to do next and when it needs confirmation. Treat privacy, security and governance as foundations for the experience, consistent with the National Retail Federation and PwC’s March 2026 work on governing agentic AI in retail.
- Pilot a bounded job before granting broader authority. Meal planning, product research and price or recipe discovery align with the activities already reported by grocery AI users. A retailer could test one such task and require shopper confirmation before cart submission, then decide whether evidence supports expanding the agent’s role.
What to measure in a grocery-agent pilot
Measure the task the agent is actually allowed to perform, rather than collapsing every interaction into a single “AI adoption” number. A discovery assistant and a transaction-capable agent have different success criteria.
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- Task quality: whether recommendations and meal plans match the request, including stated dietary needs and budget constraints.
- Basket quality: whether selected items, quantities and pack sizes fit the shopper’s intent, and whether suggested substitutions are accepted.
- Commercial effect: basket economics and the rate at which shoppers continue from a recommendation to a cart or completed order.
- Operational result: availability, fulfillment and the outcome of substitutions for orders involving the agent.
- Control and trust: whether shoppers understand permissions, confirm actions where required and can review or change the agent’s decisions.
These are suggested evaluation criteria, not published results from the cited studies. A pilot should set its baseline and success thresholds before rollout so that a rise in AI interactions is not mistaken for better shopping outcomes.
What the available evidence does not establish
The sources cited here do not provide a defensible grocery-specific percentage of purchases completed autonomously by agents, a comparative ranking of scaled grocery-agent deployments or retailer-level measured commercial outcomes from those deployments. They do support a distinction between growing AI-assisted shopping activity and the still-early shift toward fully autonomous purchasing.
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