The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →AI is changing retail in three places at once: how shoppers find products, how stores and head offices run, and how retailers make decisions. The evidence supports real adoption and some early reported returns in specific areas. It does not support the claim that AI has transformed every retailer, or that the large projected payoffs are guaranteed. Most of the numbers below come from surveys and consultancy models, so each one is labelled with who produced it and how far it reaches.
Where AI is already used in retail
The National Retail Federation (NRF) Center for Digital Risk & Innovation surveyed 56 AI leaders at U.S.-based retailers in summer 2025. That is a snapshot of those respondents, not a census of all retailers. Even so, it shows where deployment is concentrated:
| Area | Share of respondents | Status |
|---|---|---|
| IT coding and application development | 75% | Currently implemented |
| Office productivity tools | 73% | Currently implemented |
| Cybersecurity and fraud prevention | 66% | Currently implemented |
| Supply chain operations | 59% | Planned priority |
| Marketing and advertising | 45% | Planned priority |
The pattern is worth noticing. The most common uses are internal and low-visibility: writing code, drafting documents, catching fraud. The shopper-facing and supply-chain uses that get the most attention are, in this sample, still more often a plan than an established practice.
How AI is changing the shopping experience
Shoppers are using AI assistants alongside stores
The NRF and IBM study, published January 7, 2026, draws on IBM research with 18,000 consumers worldwide and a survey of retail and consumer-product leaders. It found that 72% of consumers still shop in stores. Alongside that, 41% use AI assistants to research products, 33% to look for reviews, and 31% to search for deals. AI is therefore being added to the shopping journey, not replacing the physical store.
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Keep two things separate here. Consumers using a general AI assistant is not the same as a retailer deploying AI. A shopper can research a product in an assistant and still buy from a retailer that has done nothing with AI.
Retailer chatbots, recommendations and personalization
McKinsey describes retailer assistants that connect natural-language requests to the retailer’s own product (SKU) data and personalization systems. Its analysis stresses that these need careful product design and frequent testing with real users. In the NRF survey, 48% of respondents reported their strongest returns in customer personalization. That is a respondent-reported result, and it does not show that personalization automatically lifts sales or improves customer outcomes. Marketing and advertising were an emerging priority rather than a mature one.
Agentic commerce: an emerging direction
The next step discussed is AI agents that browse, compare or even buy for a shopper. NRF’s March 19, 2026 report with PwC, Managing and Governing Agentic AI in Retail, opens with the statement that “AI agents are about to change retail on two fronts.” It draws on late-2025 workshops with U.S. retail leaders and pairs the prospect with a call for governance and security foundations. Treat agentic buying as an emerging direction, not settled mainstream behavior.
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How AI is changing store and corporate operations
Software development and office productivity
These lead current deployments, and the NRF respondents rated IT application development highest for returns (50%). Note this is the share of respondents reporting strong returns, not a measured productivity gain.
Fraud prevention and security
Two-thirds of respondents reported using AI for cybersecurity and fraud prevention. The same technology also creates new threats, which is why NRF and PwC put security controls at the center of AI preparation.
Supply chain, inventory and merchandising
AI can support forecasting and stocking decisions, and respondents flagged supply chain as a coming priority (59%). The available evidence does not justify crediting AI for a specific retailer’s stock availability or sales gains without case-specific proof. McKinsey’s work also describes AI-supported operational analysis that synthesizes performance data and suggests actions. That is a use case, not evidence of universal time savings.
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How AI is changing decision-making and investment
Budgets are still modest. In the NRF survey, 77% allocated 5% or less of their technology budget to AI, though 39% expected AI to exceed 10% of technology spending within three years. Governance is ahead of spending: 86% had AI governance policies and 93% planned to develop or continue developing them over the next 12 months.
Other surveys point the same way on intent. KPMG’s 2026 report AI in retail: Global lessons from strategy to storefront collects figures from separate KPMG studies. All of these are expectations, not realized outcomes:
- 74% of organizations expected to scale AI use cases in 2025, versus 29% in 2024, with 42% projected for 2026 (KPMG Consumer & Retail Technology Survey, 2023–2025).
- 64% of consumer and retail CEOs called AI a top investment priority, and 73% expected to spend 10%–20% of budgets on it in the next 12 months (KPMG Consumer & Retail CEO Outlook 2025).
- 82% of retail executives believed AI adopters would gain a competitive edge (KPMG, Intelligent Retail, 2025).
How big is the prize? Modeled estimates, not realized revenue
| Estimate | Source and scope | What it is |
|---|---|---|
| $240 billion to $390 billion in potential economic value from generative AI, equal to a 1.2 to 1.9 percentage-point margin increase | McKinsey & Company, 2023 analysis of retail | Modeled potential |
| €240 billion to €320 billion opportunity from AI | McKinsey and EuroCommerce, Rewiring retail in Europe: The AI imperative, June 10, 2026; European retail only | Modeled opportunity |
Do not read either as money retailers have already earned. The European report also stresses nonfinancial value: customer and employee experience, capacity building and supply chain collaboration.
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What is holding retailers back
Cost, accuracy and skills
In the NRF survey, the leading internal concerns were cost (57%), model accuracy (57%) and workforce expertise gaps (55%). McKinsey adds that results depend on company-specific data, design, testing, technical architecture and employee skills.
Legal exposure
71% of respondents were concerned about consumer class actions and 71% about intellectual-property litigation.
Consumer trust and privacy
In the IBM-NRF consumer study, 52% of consumers are comfortable sharing their data, yet 83% share overlapping concerns about privacy, misuse and unwanted marketing. Personalization depends on data that many shoppers are wary of handing over.
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How to judge a retail AI deployment
Whether you are a retailer evaluating a tool or a reader weighing a claim, six questions separate substance from hype. These synthesize the considerations raised by NRF, McKinsey and NRF/PwC; they are not a vendor ranking.
- Task: What retail job does it do, and what outcome should the customer or employee see?
- Data: Is it connected to accurate, current product, inventory and pricing data specific to that retailer?
- Measurement: Are financial and nonfinancial outcomes actually measured, and against what baseline?
- Cost: What do implementation and running costs look like?
- Safeguards: How are accuracy, human oversight, privacy and security handled?
- People and governance: Are staff trained, and is there a policy owner?
What the evidence cannot tell us
The sources are industry surveys and consultancy analyses. None is a representative global census or a controlled evaluation of every retail use case. They do not establish job elimination, universal productivity gains or guaranteed returns, and claims along those lines should be treated with caution.
The Bottom Line
AI in retail is real but uneven. Today it is strongest in internal work such as coding, productivity and fraud prevention, with personalization the main customer-facing area reporting returns. Supply chain, marketing and agent-driven shopping are the next frontier. Big value figures remain projections, so judge any claim by its data, measurement and safeguards.
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