Agentic AI could add a new layer to retail, helping companies coordinate internal work and giving shoppers tools that can research, compare and potentially buy products for them. But shoppers’ current use of AI assistants is not the same as autonomous shopping at scale. For retailers, the opportunity is emerging; reliable data, security, governance and customer trust are prerequisites for pursuing it responsibly.
What agentic AI means in retail
An AI assistant typically responds to a person’s question or request. An agent is intended to pursue a goal through multiple steps, such as gathering information, comparing options and taking an action. The distinction is a matter of capability and authorization, not just what a product is called: an agent that recommends a product is doing less than one permitted to place an order.
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In retail, this creates two connected arenas. Agents may support a retailer’s internal operations, or act in a customer’s shopping journey. The National Retail Federation Center for Digital Risk & Innovation’s March 19, 2026 report frames agentic AI across both fronts, covering internal AI transformation as well as agentic commerce, governance and security.
What shoppers are doing with AI today
AI already appears in parts of product discovery, but the available figures describe assistance—not widespread autonomous purchasing. In the IBM Institute for Business Value and NRF consumer study, 41% of surveyed consumers said they use AI assistants to research products, 33% to look for reviews, and 31% to search for deals. Those activities can inform a purchase without an agent selecting a product, checking out or managing the transaction.
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The study page also reports that 72% of surveyed consumers still shop in stores. That makes the likely retail challenge an omnichannel one: shoppers may move among AI interfaces, retailer websites and physical locations, rather than replacing stores with a single digital channel.
IBM and NRF describe the work as two global surveys conducted in Q3 2025, covering 18,000 consumers and 200 industry executives. The figures are survey responses, not a census of all shoppers or proof of adoption across every market. IBM’s January 7, 2026 newsroom summary uses different wording for AI help during buying journeys; its broad “almost half” characterization should not be substituted for the specific research, review and deal-seeking measures above.
Where agents could fit into retail
Internal operations
For a retailer, an agent could be designed to gather information across systems, surface a problem and propose or take a permitted next step. Potential areas to examine include operational analysis, insight gathering and workflow coordination—the kinds of productivity and streamlining opportunities described in the NRF’s 2026 report framing. Whether a particular task is suitable depends on the quality of the underlying data, the consequences of an error and the controls around the agent. The available survey summaries do not establish a ranking of retail use cases or a measured return on investment.
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Shopper-facing commerce
A shopping agent could help a customer state a need, compare products and move toward a purchase. More ambitious versions could transact on the customer’s behalf, but that requires clear permission, accurate product and availability information, and a checkout process that preserves retailer control and customer protections.
NVIDIA describes an open-source retail agentic-commerce reference architecture for agent-to-agent communication between merchants and AI platforms, including secure checkout on a customer’s behalf while retailers retain control. That is a vendor-described architecture, not evidence that these capabilities are universally deployed or delivering market-wide results.
Assisted shopping and autonomous buying are different milestones
| Shopping behavior | What the shopper delegates | What the evidence establishes |
|---|---|---|
| Researching products with an AI assistant | Finding or organizing information; the shopper remains responsible for decisions and purchasing. | 41% of surveyed consumers reported using AI assistants for product research in the IBM/NRF study. |
| Looking for reviews or deals | Discovering input or offers; the shopper may still compare and decide independently. | 33% reported using AI assistants to look for reviews, and 31% to search for deals. |
| Agentic commerce | Potentially comparing options and taking purchase steps under shopper-granted authority. | The cited consumer figures do not establish autonomous checkout at scale. NVIDIA describes a reference architecture for this kind of commerce, not universal deployment. |
The stages matter because adoption of an assistant for discovery cannot be used as a proxy for willingness to let software make consequential choices. Retailers and technology providers need to be explicit about what an agent can see, recommend, change or purchase, and when a person must approve an action.
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Trust, privacy and governance are part of the product
In the IBM/NRF consumer study, 52% of respondents said they were comfortable sharing data, while 83% reported concerns spanning privacy, misuse or unwanted marketing. The concerns overlap; 83% is not a single mutually exclusive response category. The contrast shows why personalization or convenience alone cannot be assumed to earn permission to use customer data.
The NRF’s March 2026 report puts governance and security alongside both internal AI transformation and agentic commerce. In practice, the relevant questions include what data an agent can access, which actions it is authorized to take, how a retailer can audit or stop those actions, and how the customer can understand and control the experience. The sources establish these as foundational issues, but do not prescribe a single technical or legal control set for every retailer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How retailers can evaluate an agentic AI use case
The NRF’s Retail AI Trends 2025 page says its Center surveyed 56 AI leaders at U.S.-based retailers during summer 2025 about strategy, investment, challenges and potential. The page does not publish detailed findings that support adoption percentages or a ranked list of use cases. Retailers can still use the following questions to scope a pilot without treating them as survey findings:
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- Where does the work happen? Separate internal employee workflows from shopper-facing commerce; they have different users, permissions and failure consequences.
- How much autonomy is justified? Decide whether the system may only summarize and recommend, take reversible steps, or execute a consequential action only after human approval.
- Can it rely on accurate data? Check that product information, inventory and customer data are sufficiently current and consistent for the decisions the agent is expected to support.
- What are the security and privacy boundaries? Define data access, permitted actions, oversight and customer controls before expanding the agent’s remit.
- Will it work across the actual journey? Consider how the experience connects with stores, retailer sites, marketplaces and AI interfaces, rather than assuming a single online channel.
These are evaluation dimensions, not a vendor ranking or a guarantee of results. A narrowly scoped task with clear approval boundaries may be easier to assess than a broad promise of fully autonomous shopping.
Why agentic AI is a strategic thesis, not a proven retail outcome
Calling agentic AI the “fuel” for retail’s next engine is a metaphor for a possible new operating and shopping layer. The evidence points to active consumer use of AI for product research, reviews and deal discovery, alongside industry attention to internal applications, governance and agentic-commerce architectures. It does not show that agents have already transformed retail as a whole, replaced stores, or delivered a defined return.
The more useful near-term question for retailers is where an agent can make a specific journey or workflow better without weakening customer control. Progress depends not only on the model, but also on trustworthy data, secure integrations, clear authority and a customer who understands what the system is doing.
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