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Agentic AI could become a new layer in retail, helping companies coordinate internal work and giving shoppers assistants that can do more than answer questions. But “fuel powering retail’s next engine” is a strategic metaphor, not a proven result: today’s evidence shows consumers using AI to research products, find reviews, and look for deals—not widespread autonomous purchasing. Retailers have an opportunity to experiment, but the technology’s value depends on trustworthy data, security, governance, and shopper confidence.
What agentic AI means for retail
Retail AI is often described as though every system were an independent digital worker. The more useful distinction is between AI that helps a person complete a task and an agent that can pursue a goal across multiple steps, such as browsing options, comparing them, and potentially taking an action on someone’s behalf.
The National Retail Federation’s Center for Digital Risk & Innovation frames the opportunity on two fronts in Managing and Governing Agentic AI in Retail (March 19, 2026): agents can support work inside retail companies and may change shopping journeys outside them. That framing is a map of emerging possibilities, not evidence that autonomous agents are already standard across the industry.
Where agents could change retail
| Area | Potential role | What to keep in view |
|---|---|---|
| Retailer operations | Assist employees with productivity, insight generation, and operational workflows. | Keep people accountable for decisions that affect customers, employees, money, or business operations. |
| Shopping journeys | Help shoppers discover products, compare choices, and potentially complete purchases. | Make recommendations, data use, retailer identity, and approval for consequential actions clear to the shopper. |
These are different implementation problems, even if they use similar underlying technology. An internal assistant operates within company processes; a shopping agent must work across product information and customer-facing channels while preserving a clear relationship between the shopper and the retailer.
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Inside the retailer
NRF and PwC’s March 2026 report describes internal AI transformation as part of the same strategic picture as agentic commerce, and says its workshops brought together cybersecurity, technology, legal, and business leaders from U.S. retailers in late 2025. That mix matters: an agent that can act within a workflow raises questions about permissions, data access, oversight, and responsibility—not just whether it can produce a useful answer.
NRF’s Retail AI Trends 2025 page says the Center surveyed 56 AI leaders at U.S.-based retailers during summer 2025 about strategy, investment, challenges, and potential. The page does not provide detailed findings that support ranking use cases or assigning adoption rates, so the survey is evidence of industry attention, not proof that a particular deployment is common.
In the shopping journey
Shopper-facing agents could eventually do more than surface search results: they might compare options and, with authorization, initiate a purchase. The important boundary is the level of autonomy. An assistant that suggests a product is not the same as one that selects it, submits an order, or spends a shopper’s money.
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For a retailer, this raises practical questions about whether its product, price, availability, and policy information can be accurately represented wherever an agent encounters it. A seamless experience also needs to account for shoppers who move between retailer websites, marketplaces, AI interfaces, and physical stores.
What shoppers are doing with AI now
The NRF/IBM study page Own the agentic commerce experience (January 7, 2026) reports that 41% of surveyed consumers use AI assistants to research products, 33% use them to look for reviews, and 31% use them to search for deals. These figures describe AI-assisted shopping activities; they do not show that those respondents delegated purchases to autonomous agents.
The same study reports that 72% of surveyed consumers still shop in stores. That is a reason to think in omnichannel terms: agent-assisted discovery may influence a journey that still ends in a store, or one that moves between online and in-person shopping. It does not establish that physical retail is disappearing or that AI alone is changing store traffic.
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IBM’s January 7, 2026 newsroom summary describes two global surveys conducted in Q3 2025 and says almost half of surveyed consumers turn to AI for help during their buying journeys. That is a broader summary than the individual research, review, and deal-seeking measures above. The measures have different wording and should not be collapsed into one universal AI-adoption rate. The study materials report 18,000 global consumers and 200 industry executives surveyed; those figures describe the study’s respondents, not the whole population of shoppers or retailers.
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Trust is part of the product
In the NRF/IBM consumer study, 52% of surveyed consumers said they were comfortable sharing data, while 83% reported overlapping concerns about privacy, misuse, or unwanted marketing. The 83% figure covers concerns that can overlap; it is not a single mutually exclusive response category. Together, the results show why convenience alone is not enough to establish shopper trust.
For an agent to act usefully, it may need access to sensitive context or permission to take actions. Retailers should make it understandable what information is being used, what the agent is allowed to do, and when a person will be asked to approve an action. That is a design and governance recommendation, not a claim that every system currently has those controls.
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NRF and PwC place governance and security among the foundations for both internal AI transformation and agentic commerce. In practice, retailers should treat controls as part of the system from the start: define access boundaries, assign responsibility for outcomes, and determine how people can review or stop an agent’s work. An agent’s ability to act should not exceed the authority the retailer can monitor and explain.
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NVIDIA describes its retail agentic-commerce blueprint as an open-source reference architecture for communication between agents across merchants and AI platforms. The vendor says the architecture supports secure checkout on a customer’s behalf while allowing retailers to retain control. This is a vendor-described design, not independent evidence of universal deployment or realized market-wide results.
The significance of that kind of architecture is the coordination problem it aims to address. If a shopper’s agent and a retailer’s systems interact, each side needs a reliable way to exchange relevant information and handle checkout without obscuring who authorized the purchase. A blueprint can illustrate one approach; it does not by itself establish that merchants, platforms, and shoppers have adopted a common standard.
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How retailers can evaluate an agentic AI project
A sensible evaluation starts with a specific problem and a defined boundary for what the system may do. The following questions apply whether an agent supports employees or shoppers:
- Purpose: Is this an internal workflow or a shopper-facing experience, and what task is it meant to improve?
- Autonomy: Does the system provide information, recommend an action, or take action? Which consequential actions require human approval?
- Data quality: Can it access accurate, current product, inventory, policy, and customer data for the task?
- Security and governance: Are permissions, accountability, and oversight defined before the agent receives access?
- Channel coverage: Can the experience work across the retailer’s relevant stores, website, marketplaces, and AI interfaces?
- Trust and recovery: Can a customer or employee understand what happened, correct a mistake, and reach a person when needed?
These are evaluation criteria, not a vendor ranking or a guarantee of results. Retailers should measure a project against its own defined purpose and operating conditions rather than assume that adding autonomy automatically improves an experience.
The opportunity—and the limit of the engine metaphor
Agentic AI is best understood as an emerging capability that may connect internal operations with new forms of assisted shopping. Consumer surveys show that AI already plays a role in some product research and deal discovery, while the same evidence shows continued in-store shopping and substantial privacy concerns. The available figures do not establish widespread autonomous buying, a specific return on investment, or a completed transformation of retail.
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The strategic opportunity is real enough to justify careful experimentation. The durable advantage, if agents become more capable and widely used, is likely to depend not just on the agent itself but on whether the retailer can provide accurate information, secure access, accountable actions, and a trustworthy experience across channels.
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