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AI companions could become a new kind of sales associate, but the strongest evidence points first to research, comparison and discovery—not handing over the checkout decision. Consumers are already using AI to find products, reviews and deals; trust, accuracy and clear permission controls will determine whether brands earn a place in those conversations.
What would an AI sales associate actually do?
“AI companion” is the broad idea of an AI that helps a person over time. In shopping, the more useful distinction is between an assistant that responds to requests and an agent that can act on a shopper’s behalf. Accenture describes agent behavior as a range, from executing tasks to making decisions and purchasing autonomously within permissions set by the consumer.
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| Shopping role | What it does | Where the shopper stays involved |
|---|---|---|
| Research assistant | Finds product information, reviews, prices or deals in response to a question. | The shopper evaluates the results and chooses what to do next. |
| Comparison assistant | Filters options against stated needs, such as features, budget or intended use. | The shopper checks whether the options and trade-offs are right. |
| Shopping agent | Can take actions for the shopper, potentially including a purchase, within granted permissions. | The shopper’s control depends on the permission settings and whether approval is required before checkout. |
These are degrees of delegation, not necessarily separate products. A single system could move from answering questions to taking action, so the key question is not simply whether it uses AI: it is what the system is allowed to do, and when it must ask.
How ready are consumers to let AI shop for them?
Interest is clearest before the purchase: finding candidates, narrowing a list and gathering information. Survey results differ because they asked different questions, used different samples and covered different markets. They should be read as evidence of varied comfort levels, not combined into one universal adoption rate.
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| Source and sample | Finding | What it indicates |
|---|---|---|
| Gartner, January 2026 survey of 322 U.S. consumers | 31% were willing to let AI narrow household-supplies choices; 28% were willing to let it narrow personal-electronics choices. Across the lower-stakes categories Gartner cited, willingness to let AI make purchase decisions topped out at 11. | For these respondents, help narrowing choices was more acceptable than handing over the decision. |
| National Retail Federation and IBM, 2026 global consumer study of 18,000 people | 41% used AI assistants to research products, 33% to look for reviews and 31% to search for deals. The study also reports that 72% still shop in stores. | AI can be part of product discovery alongside established shopping channels; the figures do not imply that AI replaces store shopping. |
| Walmart, 2025 report | 46% of respondents were somewhat or very unlikely to use a digital assistant or agent to handle an entire shopping trip. | Delegating a whole trip is a different proposition from asking for help with one step. |
| Accenture, 2026 survey of 25,590 consumers across 16 countries | 74% said they would trust a personal AI agent more than their best friend to make a purchase on their behalf; the report separately says 32% were ready to hand over purchase decisions and 9% were open to fully autonomous shopping. | Accenture’s findings suggest some openness to delegation, but its wording and measures are not directly comparable with Gartner’s or Walmart’s. |
The contrast is a useful reminder: a stated willingness to trust an agent in one survey does not mean the same thing as readiness to delegate a purchase in another. Gartner’s VP Analyst Kate Muhl summed up the preference reflected in Gartner’s findings: “They want AI to help them find better information, compare prices, identify deals and narrow choices, while keeping final decision-making control for themselves.”
Why does using AI not automatically mean trusting it?
People can find an AI tool useful enough to try and still have to verify its answers. In a separate Gartner survey of 846 U.S. consumers conducted in November and December 2025, among those 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, and 62% said that information ended up being a waste of their time. These results describe that surveyed group and question context, not all AI users.
Privacy is another part of the trust equation. In the NRF/IBM 2026 global study, 52% of consumers said they were comfortable sharing their data, while 83% reported multiple overlapping concerns about privacy, misuse and unwanted marketing. Those figures are not opposites: someone may be comfortable sharing data in some circumstances and still have several concerns about how it could be used.
For shopping assistance to feel dependable, a recommendation needs to be checkable. Product fit, current price and availability are especially consequential: if an answer is wrong or hard to verify, the system can create extra work instead of saving time. Muhl called accuracy “a brand issue” for that reason—not just a technical matter for the AI provider.
Are brands ready for AI-assisted shopping?
Readiness is uneven, and it is more than appearing in an AI-generated answer. Brands need product information that is complete and credible, and that stays consistent when shoppers move between channels. IBM’s Institute for Business Value and the National Retail Federation frame the visibility and credibility of product information as a brand concern as AI tools gain purchasing capabilities.
Salesforce’s September 2026 release reports AI-originated referral traffic grew between 150% and 428% year over year in every quarter measured, in an analysis of more than 1.5 billion shoppers across 37 countries from Q1 2024 through Q1 2026. Its separate statement that agentic search grew 200% year over year is a different metric; neither figure by itself establishes how many shoppers completed a purchase through an agent.
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Salesforce also reports that surveyed Singapore commerce organizations were taking steps to prepare:
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- 40% said they were optimizing for conversational queries.
- 39% said they were submitting data feeds to AI search platforms.
Those percentages are specific to the Singapore findings, not a global estimate of brand readiness. Salesforce’s same release describes operational problems in Singapore, including inconsistent pricing and promotions and inventory synchronization issues. They illustrate why AI discovery depends on dependable information across systems, not only polished product descriptions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should brands do to prepare?
- Make product facts complete and current. Keep descriptions, specifications, prices, promotions and availability accurate. Write content that answers the natural-language questions shoppers actually ask, while retaining enough detail for a person to verify a recommendation.
- Keep information consistent across channels. Align product facts, prices, promotions and inventory between the systems and storefronts where customers encounter them. A conversational answer is only as dependable as the data behind it.
- Make data use understandable. Explain what information a shopping assistant uses and why, collect only what is needed for the task, and give people meaningful controls over their information and marketing preferences.
- Set explicit boundaries on action. Distinguish recommendations from actions that commit the shopper. Make permissions understandable and require clear approval before consequential steps such as checkout unless the consumer has deliberately authorized a narrower, defined action.
- Check whether the experience reduces effort. Assess whether shoppers can verify product fit, price and availability, and whether they can correct an assistant or regain control easily. Treat these as practical readiness checks, not as proof that a particular AI platform will recommend or sell a brand’s products.
The first two priorities align with actions Salesforce says commerce organizations are already taking. The approval and control guidance follows from the distinction between assistance and delegated action, as well as the consumer-control concerns reported by Gartner and Walmart.
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What is the likely path from companion to sales associate?
The plausible near-term role is an informed guide: helping shoppers discover options, compare trade-offs and locate reviews or deals. More capable agents may handle defined tasks when a person grants permission, but broader autonomy is not the same as a conversational interface and is not yet supported as a universal consumer preference by these studies.
For brands, readiness means being understandable to both people and AI systems without asking shoppers to surrender control. The brands best placed to participate will be those whose product information is accurate, consistent and easy to verify—and whose AI-assisted experiences make the boundary between advice and action clear.
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