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Retailers are applying AI to shopping assistance, customer service, visual commerce, merchandising, and fulfillment. The 15 examples below include capabilities described as in use as well as pilots, developing features, and plans; they are not 15 independently audited production deployments.
Most details come from Walmart, Amazon, or AWS. Their pages are useful for identifying features and stated status, but company and vendor descriptions do not independently establish how well a system works.
What are 15 examples of AI in retail and ecommerce?
The table separates customer-facing tools from internal operations and labels each item by the maturity described in its source. Several Walmart examples are capabilities inside a broader program, not standalone products.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →| Example | Retail task and context | Status described by the source | Source and evidence |
|---|---|---|---|
| 1. Sparky shopping assistant | Helps app users find and compare products, build lists, get recommendations, and plan occasions using product information and reviews. | Described as in use. | Walmart’s current technology overview describes the capabilities; it does not provide independent performance evidence. |
| 2. Customer Support Assistant | Routes customer inquiries and handles common issues, including order lookup and returns; Walmart says it is progressing toward completing more tasks end to end. | Described as in use and evolving. | Walmart’s October 2024 account and current technology overview describe its functions. |
| 3. Generative-AI product search | Lets a shopper search by an occasion, such as a football watch party, to find relevant items across categories. | Announced example; current availability is not established by the cited source. | Walmart, January 2024. |
| 4. Individualized homepage and Content Decision Platform | Would tailor the U.S. homepage and recommendations to individual shoppers; Walmart also described planned recommendations for Canada and Mexico. | Plan stated in October 2024. The reviewed sources do not verify that the individualized homepage launched as expected. | Walmart, October 2024. |
| 5. Retina augmented-reality experiences | Supports visual shopping experiences, including View in Your Home, across Walmart U.S. and Sam’s Club. | Reported in use. Walmart said Retina powered 10 experiences in 2024 and reported a 10x increase in customer and member adoption, reduced returns, and improved conversion. The announcement gives no baseline or measurement method for those outcomes. | Walmart, 2024; outcomes are Walmart-reported. |
| 6. Immersive commerce experiences | Extends shopping into virtual and social environments, including Roblox, Walmart Realm, Unity, and ZEPETO. | Mixed status: Walmart described a Roblox commerce pilot and Walmart Realm launch, said Unity was in alpha testing, and described ZEPETO as planned. | Walmart, October 2024. These are not all equivalent launches. |
| 7. Shop with Friends virtual try-on | A proposed social-shopping experience combining augmented-reality try-on and generative AI so shoppers can create outfits and ask friends for feedback. | Building/announced; the source describes how it would work, not a completed launch. | Walmart, January 2024. |
| 8. Merchant tools | Automate time-consuming data entry and analysis for retail merchants. | Described as in use. | Walmart’s current technology overview; no comparative outcome metric is stated. |
| 9. Trend-to-Product | Uses AI in fashion workflows to move from emerging trends toward product development. | Described as in use. Walmart says it can shorten traditional fashion production timelines by as much as 18 weeks; this is Walmart’s claim, not a result established for other retailers. | Walmart’s current technology page, accessed October 7, 2026; the accessed view does not give a publication date. |
| 10. Agentic inventory and delivery workflows | Supply-chain systems help anticipate demand, decide where inventory should be located, and coordinate delivery. | Walmart describes these workflows as operating in its supply chain. | Walmart’s current technology overview describes its Agentic End-to-End Workflow. |
| 11. Task-specific retail agents | Potential agents for comparison, personalization, and completing parts of a shopping journey. | Strategy and development, with early testing described; not evidence of a broadly autonomous live service. | Walmart U.S. CTO Hari Vasudev, May 29, 2025. |
| 12. Rufus shopping assistant | A real-time generative-AI assistant for Amazon shoppers. | Identified as an Amazon customer-facing application. | AWS’s Amazon customer story describes the application; the captured page text does not establish a comparable outcome metric. |
| 13. AI review highlights | Creates summaries of customer reviews to help shoppers scan product feedback. | Identified as an Amazon customer-facing generative-AI feature; the source describes summaries generated offline. | AWS’s Amazon customer story. |
| 14. Long-term demand forecasting | Supports inventory management across Amazon fulfillment centers. | Identified as an application at Amazon. The source describes increased forecasting accuracy but the captured text supplies no percentage. | AWS’s Amazon customer story; no numerical accuracy claim is included here. |
| 15. Kate Spade AI Gift Concierge | Provides conversational gift recommendations for Kate Spade shoppers. | Amazon describes it as production-ready and its first production-ready retail AI assistant built with Bedrock AgentCore. | Amazon’s launch account identifies Kate Spade as using the AWS Agentic Shopping Assistant foundation. |
How should you read the deployment labels?
“In use,” “launched,” “pilot,” and “planned” are not interchangeable. A company may describe a capability in an official overview without publishing details that let an outside reader verify its reach, usage, or results. A dated announcement can establish what the company said it was doing at that time, but does not by itself confirm that a planned feature later shipped.
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One related Walmart item is not counted as a separate shopper deployment: Wallaby, Walmart’s retail-specific large-language-model family. Walmart says the models draw on decades of its data and are intended to support customer-facing experiences. That makes Wallaby an underlying platform capability; a particular product experience, such as Sparky, is the more useful unit when asking what a shopper can do.
What do these examples show about retail AI?
AI can change the interface, not just automate a task
Shopping assistants, occasion-based search, review summaries, and gift recommendations let shoppers express intent in conversational or contextual terms rather than relying only on conventional product queries. Their practical value depends on whether the answers are relevant and grounded in accurate product information—details the cited descriptions do not consistently measure.
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Retailers also apply AI behind the storefront
Merchant analysis, fashion development, demand forecasting, inventory placement, and delivery coordination are operational uses rather than shopper-facing chat features. They address different workflows and should not be judged by the same success measure as a shopping assistant.
Visual and social commerce is at mixed stages
Augmented reality and virtual environments can help shoppers visualize products or involve other people in a purchase. The examples here range from experiences Walmart reports using to initiatives it described as pilots, testing, or plans; grouping them under “immersive commerce” should not imply that each is equally available.
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Can these examples be compared by results?
Not on the evidence described here. The sources are primarily official company pages and vendor case material, and they do not provide a consistent, independently evaluated measure across the examples. Walmart’s 2024 Retina announcement reports adoption growth, reduced returns, and improved conversion, but does not disclose the baseline or method. AWS’s Amazon story describes forecasting accuracy gains without a percentage in the captured text. Those claims should remain attributed to their publishers rather than treated as a like-for-like benchmark.
For a retailer evaluating a similar system, a useful comparison would start with the task and operating context, then ask what maturity evidence exists and how results were measured. For example, a pilot should be distinguished from a production launch, and a stated capability from a measured outcome. A credible evaluation would define a baseline and a relevant comparison, such as task completion, customer-service resolution, return rates, forecast error, or time spent on a workflow, before claiming impact.
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