Brief Overview
AI In Retail Stores Statistics: AI in retail stores includes tools that predict demand, personalize deals, run self-checkout without cashiers, catch fraud, and power chatbot-style shopping helpers. Big retailers like Amazon, Walmart, Home Depot, and Albertsons are now teaming up with AI companies so shoppers can find and buy products just by chatting with an AI, instead of searching and browsing the old way. But surveys show a real gap between words and action: company leaders say AI is a top priority, yet most of it is still being tested rather than fully used, and spending hasn’t caught up with the talk.
Shopping assistants are already helping stores sell more and get bigger orders, but the same technology that helps retailers is also helping scammers, leading to more stolen-account and payment fraud. Retailers are slowly rolling out AI, focusing first on stopping fraud and personalizing shopping, while they figure out how to manage newer tools like AI agents that can shop on their own. This article explores the newest facts about AI in retail stores. Let’s dive in.
Best of the Best
- The global AI-in-retail market reached USD 20.4 billion in 2026, projected to reach USD 127.2 billion by 2033.
- IBM’s 2025 survey found 90% of retail executives plan extensive AI use in supply chains.
- 58% of retailers actively deployed AI in 2026, up from 42% in 2025.
- Walmart’s Sparky users have average order values 35% higher than non-users.
- Amazon’s Rufus users are about 60% more likely to complete a purchase.
- 67% of retailers use generative AI to create marketing and advertising content.
- Account-takeover attacks rose 78% year over year in early 2026, driven by automation.
- Retailers lose an average of USD 9.3 million annually to fraud, one survey found.
- Global retail AI spending is projected to reach USD 19.9 billion in 2026.
Recent AI in Retail Stores Developments
- On September 30, 2026, Albertsons expanded its OpenAI partnership, introducing AI shopping services across Safeway and 6 additional grocery brands.
- On September 23, 2026, Meta announced AI-shopping partnerships with Walmart, Gap, Sephora, Expedia, Wayfair, Best Buy, and other companies.
- On June 4, 2026, Walmart reported automated distribution centers served over 60% of stores and handled over 50% of e-commerce fulfillment.
- On January 11, 2026, Walmart and Google partnered to integrate Gemini shopping with deliveries under 3 hours or within 30 minutes.
- On January 11, 2026, Home Depot expanded its Google Cloud partnership and began equipping thousands of associates with Gemini Enterprise.
- On January 7, 2026, an IBM-NRF study found 45% of 18,000 surveyed consumers used AI during shopping journeys.
- On December 10, 2025, Amazon announced an investment exceeding USD 35 billion in India through 2030, covering AI and logistics expansion.
- On November 29, 2025, AI influenced USD 14.2 billion in global Black Friday sales, including USD 3 billion in the United States.
- On October 14, 2025, Walmart partnered with OpenAI for ChatGPT checkout; referral traffic increased from 9.5% in August to 15% in September.
Global AI in Retail Market Growth
(Source: market.us)
- The global AI-in-retail market was valued at USD 12.1 billion in 2024, USD 15.7 billion in 2025, and USD 20.4 billion in 2026.
- Market value is expected to rise to USD 26.5 billion in 2027, USD 34.4 billion in 2028, and USD 44.7 billion in 2029.
- By 2033, the global AI-in-retail market is projected to reach USD 127.2 billion.
- The market is expected to grow at a compound annual growth rate of 29.9% from 2024 through 2033.
AI Adoption in Retail and Consumer Goods
- IBM’s 2025 survey of 1,500 global retail and consumer-goods executives found that 90% plan extensive AI use in supply-chain operations.
- 87% of executives expect to use AI extensively in sustainability initiatives.
- 86% plan widespread AI deployment in procurement activities.
- 83% expect AI to be used extensively in production and manufacturing.
- 89% of executives expect AI to support innovation in products and services, while 85% expect it to support business-model innovation.
- 54% expect AI to contribute to operational innovation.
- 88% of organizations use AI moderately or extensively for demand forecasting.
- 89% use AI to support human-resources help desks.
- 84% have integrated AI into IT support and issue resolution.
- 84% use AI to develop and manage sales promotions.
- 81% use AI for inventory and order management.
- 80% have incorporated AI into production processes.
(Reference: mecalux.com)
Retail Stores Impacted by AI Implementation
- 89% of retail and consumer-packaged-goods companies were actively using or testing AI in 2026.
- 58% of retailers were actively deploying AI solutions in 2026, up from 42% in the prior year, representing a 16-point increase in active deployment.
- Approximately 90% of retailers planned to increase their AI budgets in 2026, while about 50% expected to raise investment by 10% or more.
- Only 33% of online stores had fully implemented AI across their operations, showing that many retailers remain in the pilot, testing, or partial-deployment stage.
- 47% of retailers were using or assessing agentic AI, and 20% had AI agents already operating in their business processes.
- 48% of U.S. retailers were developing AI-enabled demand-forecasting capabilities.
- 74% of consumer and retail CEOs identified AI as a top investment priority, and 10%–20% of consumer and retail CEOs expected their companies to allocate this share of their budgets to AI during the following 12 months.
- Among retail and e-commerce brands, 91% were deploying or piloting AI in 2026, compared with 56% in 2024.
- 74% of pure-play e-commerce businesses, 41% of omnichannel retailers with physical stores, and 28% of grocery and discount retailers were using AI, according to one 2026 industry assessment.
AI Adoption in Corporate and B2B Retail Operations
| Finding | Figure(s) |
| Improvement in logistics efficiency from AI in supply-chain operations | 15% |
| Executives who see AI as essential for competitiveness | 66% |
| Decision-makers implementing or expanding AI use | 84% |
| Reduction in fraud-related losses from AI fraud detection | up to 30% |
| Improvement in forecast accuracy from AI demand forecasting | 10% to 20% |
| Companies using AI for customer-service activities | 61% |
| Revenue increase from AI-based dynamic pricing | 2% to 7% |
| Reduction in logistics labor costs from AI warehouse robots | 20% to 40% |
- AI is increasingly being used across retail operations, including warehouses, supply chains, workforce management, and customer-facing activities.
- AI adoption in retail supply-chain operations could improve logistics efficiency by 15% within the next five years.
- 66% of retail executives believe that AI is essential for maintaining competitiveness in the industry.
- 84% of retail and consumer-goods decision-makers are either implementing AI or expanding its use across their operations.
- Retailers using AI for fraud detection can reduce fraud-related losses by up to 30%.
- AI-powered demand forecasting can improve forecast accuracy by 10% to 20%, helping retailers optimize inventory levels and reduce product waste.
- 61% of retail and consumer-goods companies already use AI for customer-service activities, reflecting growing adoption in B2B support operations.
- Retailers using AI-based dynamic pricing can increase revenue by 2% to 7%.
- AI-driven warehouse robots can reduce logistics labor costs by 20% to 40%.
AI Shopping Assistants Boost Sales at Walmart and Amazon
- Walmart and Amazon report that their AI shopping assistants are helping increase online sales by improving product discovery, recommendations, and purchase completion.
- Walmart shoppers who use the company’s AI assistant, Sparky, have average order values that are 35% higher than shoppers who do not use the tool.
- Amazon customers who use its AI shopping assistant, Rufus, are approximately 60% more likely to complete a purchase.
- Around 50% of Walmart app users have interacted with Sparky, indicating broad adoption of the retailer’s AI shopping tool.
- More than 300 million Amazon customers used Rufus during the previous year.
- AI assistants such as Sparky and Rufus provide more relevant product recommendations, which can increase conversion rates, raise basket sizes, and encourage repeat purchases.
- 25% of U.S. consumers trust retailers to manage the complete shopping experience, compared with 7% who trust AI platforms such as ChatGPT.
- AI-platform checkouts are expected to account for only 0.1% of U.S. retail e-commerce sales in 2026 and are forecast to remain below 2% through 2029.
Generative AI Use Cases in Retail Stores
- 74% of physical retailers use AI for store and customer analytics, helping teams understand shopper behavior and store performance.
- 58% of physical retailers use AI to optimize sales-associate activities, including task guidance and customer support.
- Among brands already using conversational AI, 96% deploy it for customer-service support.
- 67% of retailers use generative AI to develop marketing and advertising content, including promotional copy and campaign assets.
- 50% of retailers use AI assistants for customer service, such as answering product questions and handling routine requests.
- Around 28% of retailers use AI-powered visual search, allowing shoppers to find similar items using images.
- Around 30% of retailers use AI-based dynamic pricing to adjust prices using demand, inventory, and competitive conditions.
- 71% of retailers plan to deploy AI concierges or digital stylists during 2026.
- Operational efficiency: 51% of retail respondents identify AI for supply-chain efficiency and throughput as a major operational use case.
| Finding | Figure(s) |
| Physical retailers using AI for store and customer analytics | 74% |
| Physical retailers using AI to optimize sales-associate activities | 58% |
| Brands using conversational AI that deploy it for customer-service support | 96% |
| Retailers using generative AI for marketing and advertising content | 67% |
| Retailers using AI assistants for customer service | 50% |
| Retailers using AI-powered visual search | around 28% |
| Retailers using AI-based dynamic pricing | around 30% |
| Retailers planning to deploy AI concierges or digital stylists during 2026 | 71% |
| Retail respondents identifying AI for supply-chain efficiency and throughput as a major use case | 51% |
Fraud Activity in Retail After AI Adoption
- Ecommerce fraud pressure rose 33% year over year during the first four months of 2026 across Signifyd’s network of thousands of online merchants.
- Account-takeover attacks increased 78% year over year in early 2026, driven by automated credential-testing and identity-based fraud.
- Card-testing attacks surged 175% year over year, as AI-enabled tools accelerated the validation of stolen payment details.
- Buy-online-pickup-in-store fraud rose 65% in the first third of 2026, extending digital fraud into physical-store fulfillment.
- First-party fraud and customer abuse grew 9% year over year during the first four months of 2026.
- “Item not as described” claims increased 49% in early 2026 compared with the same period in 2025.
- Fraudulent order values rose 3% year over year in the first four months of 2026.
- Retailers lose an average of USD 9.3 million annually to fraud, according to a 2026 survey of 1,504 fraud and payments professionals.
- 78% of retail merchants said fraud has negatively affected business growth, while 52% delayed expansion or new-product launches due to fraud concerns.
- Account takeovers represented 26% of reported retail fraud losses, and promotion or discount abuse represented 18%.
Retail Stores’ Investment in AI
- Global retail AI spending is projected to reach USD 19.9 billion in 2026, compared with USD 6.4 billion in 2021.
- About 90% of retailers plan to increase AI budgets in 2026, and roughly 50% expect budget growth of 10% or more.
- 82% of retail and consumer-product executives plan to raise AI investment over the next 12 months.
- Around 50% of surveyed retail and consumer-product organizations invest less than 0.5% of revenue in AI.
- 77% of retailers allocate 5% or less of their technology budgets to AI, suggesting investment remains cautious despite broad interest.
- 52% of consumer and retail businesses invest at least USD 50 million per year in digital technology.
- 28% of consumer and retail businesses invest between USD 100 million and USD 250 million annually in digital technology.
Wrapping Up
AI has moved from pilot projects to core retail operations, powering demand forecasting, fraud detection, pricing, and shopping assistants that lift conversion and order value. Retailer adoption is near universal, and most plan bigger AI budgets, though spending as a share of revenue still lags enthusiasm.
Shopping assistants like Sparky and Rufus already drive measurable sales gains, while fraud, including account takeovers and card testing, keeps rising alongside automation. As investment deepens, closing the gap between adoption and full-scale deployment will define retail AI’s next phase.
FAQ
Retailers use AI for shelf monitoring, smart checkout, demand forecasting, in-store product recommendations, staff scheduling, and security, helping stores operate more efficiently and respond faster to customer needs.
AI analyzes sales data, seasonality, and local trends to predict demand, automate restocking, reduce out-of-stock items, and cut excess inventory, which lowers waste and improves product availability.
Cashierless checkout uses cameras, sensors, and AI to track items shoppers pick up and charge them automatically as they leave, reducing waiting times and eliminating traditional checkout lines.
AI connects customer data from loyalty programs, mobile apps, and past purchases to deliver personalized offers, product suggestions, and digital signage, helping stores create more relevant experiences for individual shoppers.
Yes, AI-powered video analytics and sensors can detect suspicious behavior, flag unscanned items at self-checkout, and monitor inventory discrepancies, helping retailers reduce theft and loss.
Common challenges include high implementation costs, data privacy concerns, integration with legacy systems, employee training needs, and customer comfort with in-store cameras and data collection.
