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AI companions are more likely to become shopping guides before they become autonomous buyers. People are already using AI to research products, find reviews, and search for deals, but studies show substantially less willingness to let it make the final purchase decision. For brands, readiness starts with accurate product information, consistent prices and availability, and clear limits on what an AI can do for a customer.
What would an AI sales associate actually do?
“AI companion” is a broad label. In shopping, the useful distinction is between an assistant that responds to a person’s requests and an agent that can take actions for that person within permissions they set. A conversational assistant might compare two products or surface reviews. An agent might also add an item to a cart or complete a purchase if the consumer has authorized that action.
Accenture’s 2026 report describes agent behavior as ranging from task execution to decisions and autonomous purchasing. That range matters: helping someone choose is not the same as choosing and buying for them. The more consequential the action, the more important it is to make the agent’s permissions, information, and approval requirements understandable.
Where are consumers using AI in shopping today?
The strongest evidence of current interest is in discovery and comparison. In a 2026 global consumer study of 18,000 people, the National Retail Federation (NRF) and IBM reported that 41% had used AI assistants to research products, 33% to look for reviews, and 31% to search for deals. The figures describe reported uses in that study, not the share of all shoppers who routinely rely on AI.
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AI assistance is not replacing physical retail. In the same NRF/IBM study, 72% said they still shop in stores. The emerging pattern is a mixed journey: consumers may use AI to gather information and then continue shopping through a retailer’s website or in person.
Will people let AI make the purchase?
Evidence points to a gradual handoff, not a single moment when shoppers either trust AI completely or reject it. In Gartner’s January 2026 U.S. survey of 322 consumers, 31% were willing to let AI narrow household-supplies choices and 28% were willing to let it narrow personal-electronics choices. Gartner reported that willingness to let AI make purchase decisions topped out at 11% across the lower-stakes categories it cited.
A separate Gartner U.S. survey, conducted in November and December 2025 with 846 consumers, found that among people who had used AI while shopping for a recent purchase, 54% said they had to double-check the accuracy of all information GenAI tools provided. In that same group, 62% said the information ended up being a waste of their time. These are separate questions about recent AI-shopping users, not measures of all consumers’ attitudes toward AI.
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Other surveys show that willingness can look different depending on how a question is framed. Accenture’s 2026 survey of 25,590 consumers across 16 countries reported that 74% would trust a personal AI agent more than their best friend to make a purchase on their behalf. The report separately said 32% were ready to hand over purchase decisions and 9% were open to fully autonomous shopping. Walmart’s 2025 report found that 46% of respondents were somewhat or very unlikely to use a digital assistant or agent to handle an entire shopping trip.
Those percentages should not be read as a direct contest between studies: the surveys used different populations and questions, and “trust,” willingness to delegate a decision, and willingness to hand over an entire trip are not interchangeable. Together, they show why consumers may welcome help while still wanting control over what gets bought.
Why do use and trust diverge?
A tool can be useful for finding options and still create extra work if its recommendations are hard to verify. Gartner analyst Kate Muhl summarized the distinction this way: “Consumers are not looking to outsource shopping decisions to AI. They want AI to help them find better information, compare prices, identify deals and narrow choices, while keeping final decision-making control for themselves.”
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Privacy adds another tension. NRF and IBM reported that 52% of consumers in their 2026 study were comfortable sharing their data, while 83% reported multiple overlapping concerns involving privacy, misuse, and unwanted marketing. These figures can coexist: someone may be willing to share some information for a useful service and still worry about how it will be used or who will receive it.
For an AI sales associate, trust therefore depends on more than a friendly conversation. A shopper needs to be able to check whether a product fits their needs, whether its price and availability are current, what information the system used, and whether a person must approve a purchase.
Are brands ready for AI-assisted shopping?
Readiness is not just adding a chatbot. AI tools can only represent a brand reliably when they can access clear, current product facts and when those facts agree across the places a customer shops.
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Salesforce’s 2026 release reports that organizations are responding to AI-assisted discovery by improving product content and making information easier for AI systems to find. In a survey of commerce organizations in Singapore, 42% reported improving product-content quality, 40% optimizing for conversational queries, and 39% submitting data feeds to AI search platforms. Those operational percentages are specific to the Singapore findings and should not be generalized to businesses everywhere.
Salesforce also analyzed more than 1.5 billion shoppers across 37 countries from Q1 2024 through Q1 2026. Its report says AI-originated referral traffic grew between 150% and 428% year over year in every quarter measured. The same release separately describes agentic search as growing 200% year over year; that is a different metric and should not be substituted for the referral-traffic range.
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What should brands prepare before agents can buy?
- Make product facts complete and verifiable. Keep descriptions, specifications, fit information, and other decision-relevant details accurate and current. IBM’s Institute for Business Value and NRF identify the visibility and credibility of product information as a brand concern as AI tools gain purchasing capabilities.
- Write for real questions, not just category labels. Help product content answer natural-language queries such as whether an item suits a particular use or how it differs from another option. Salesforce reports that commerce organizations are improving content quality and optimizing for conversational queries.
- Keep offers and availability consistent across channels. Price, promotions, stock status, and product facts should not contradict one another between a brand’s own channels and retail partners. Salesforce’s Singapore findings identify pricing and promotion inconsistencies and inventory synchronization as operational issues.
- Make permissions and approvals explicit. A recommendation, a cart addition, and a completed purchase are different levels of authority. Let customers understand what the system may do, require approval at consequential steps, and make it straightforward to change or withdraw permissions. This is a practical response to the gap between interest in assistance and willingness to delegate purchasing.
- Explain data use and limit collection. Tell people what information is being used and why, avoid collecting data that is not needed for the service, and provide meaningful privacy controls. NRF/IBM’s findings on overlapping concerns make transparency part of the shopping experience, not a back-office detail.
- Connect product information across systems. Product feeds, inventory, promotions, and customer-facing channels need to stay aligned. Fragmented data makes it harder for an AI tool to return a reliable answer and harder for customers to verify one.
How should shoppers judge an AI shopping agent?
Before relying on an assistant or allowing an agent to act, check the boundaries of its authority and the quality of the information it uses:
- Action: Does it only suggest options, or can it change a cart or place an order?
- Approval: Must you confirm before checkout, and can you review the exact item, seller, price, and delivery details?
- Accuracy: Can you verify product fit, current price, promotions, and availability with the seller?
- Privacy: Is it clear what data the system uses, how it may be used, and how to manage permissions?
- Coverage: Does it search the categories and retailers relevant to your decision, or only a limited set?
These checks are especially useful when the system moves beyond finding information into making commitments on a shopper’s behalf.
What the evidence does—and does not—show
The cited figures come from different studies: U.S. consumer surveys by Gartner, a global consumer study by NRF and IBM, a multinational survey by Accenture, Walmart’s 2025 report, and Salesforce research spanning commerce professionals, consumers, and platform behavior. They do not establish one universal adoption rate or prove that all AI companions will become sales associates. They do support a more specific conclusion: AI is already part of some consumers’ product research, while trust, accuracy, privacy, and control remain central to whether consumers will delegate decisions or purchases.
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