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Retailers are using AI for shopping assistance, customer service, visual shopping, merchandising and supply-chain work—but these examples are at different stages. The available evidence does not establish 15 comparable, independently verified deployments. It does identify named capabilities at Walmart and Amazon, plus Kate Spade’s production-ready gift concierge. Here is what each example does, and what its source says about its status.
How to tell a live deployment from a plan
A retailer’s announcement can describe a capability already in use, a launched experience, a pilot, a feature under development or a future plan. Those categories are not interchangeable. The examples below preserve the maturity language in Walmart’s and Amazon’s accounts; most sources are company or vendor descriptions, not independent evaluations.
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- Described as in use: the company says the capability is operating, though that does not independently verify its reach or effectiveness.
- Production-ready or launched: a specific experience is described as ready for production or launched, which is stronger evidence of deployment than an announcement of intent.
- Pilot, testing or building: the experience is being trialled or developed, and may not be broadly available.
- Planned or announced: the company has described an intended experience or target date; that alone does not establish that it launched.
What are retailers using AI for?
The examples span the shopper journey and the work behind it. The table uses the status and scope stated by Walmart, Amazon or AWS; it does not imply that every feature is available to every customer or in every market.
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| Retailer and example | Task and operating context | Status described by source |
|---|---|---|
| Walmart Sparky | In Walmart’s app, helps shoppers find and compare products, build lists, receive personalized recommendations and plan occasions using reviews and product data. | Walmart’s current technology overview describes it as in use. |
| Walmart Customer Support Assistant | Routes inquiries and resolves common issues; Walmart says it is increasingly able to complete tasks end to end. Its October 2024 account described finding orders and managing returns. | Described as in use and evolving by Walmart. |
| Walmart generative-AI product search | Lets shoppers search using an occasion, such as a football watch party, to find relevant products across categories. | Described in Walmart’s January 2024 announcement; the cited account does not establish current availability. |
| Walmart Retina AR experiences | Augmented-reality shopping experiences, including View in Your Home, across Walmart U.S. and Sam’s Club. | Walmart reported 10 experiences in use in 2024. |
| Walmart Shop with Friends | A proposed social-shopping experience combining AR try-on and generative AI so shoppers could assemble outfits and ask friends for feedback. | Described as building or announced; the account explains how it would work, not that it launched. |
| Walmart Roblox commerce pilot and Walmart Realm | Immersive commerce experiences in virtual environments. | Walmart described a Roblox pilot and the launch of Walmart Realm; those are distinct maturity stages. |
| Walmart Unity and ZEPETO experiences | Further immersive-commerce initiatives. | Walmart said it was entering alpha testing with Unity and planned a ZEPETO experience. |
| Walmart merchant tools | Automate time-intensive data entry and analysis for internal merchant workflows. | Described as in use on Walmart’s current technology page. |
| Walmart Trend-to-Product | Uses AI in fashion product development to connect emerging trends with products. | Described as in use by Walmart. The company says it can compress traditional fashion production timelines by up to 18 weeks. |
| Walmart agentic inventory and delivery workflows | Supply-chain systems that help anticipate demand, decide where inventory should be located and coordinate delivery through the Agentic End-to-End Workflow. | Walmart’s current technology overview describes these workflows as operating. |
| Walmart task-specific retail agents | Potential agents for comparison, personalization and completing parts of the shopping journey. | Walmart U.S. CTO Hari Vasudev discussed strategy, early testing and a path from copilots toward greater autonomy in May 2025; this is not evidence that all such agents are broadly deployed. |
| Walmart Wallaby models | A family of retail-specific large language models, which Walmart says draws on decades of its data and is intended to support customer-facing experiences. | A platform capability, not a standalone customer deployment. The cited description does not specify which individual experiences it powers. |
| Walmart Content Decision Platform and individualized homepage | Planned personalization of the U.S. homepage, with recommendations also planned for Canada and Mexico. | In October 2024, Walmart said it expected the U.S. experience by the end of 2025. The cited material does not verify that it launched as planned. |
| Amazon Rufus | A generative-AI shopping assistant described as helping customers shop in real time. | Identified as an Amazon application in an AWS customer story. |
| Amazon review highlights | AI-generated summaries of customer reviews to help shoppers understand product feedback. | Identified as a customer-facing generative-AI application in the AWS customer story. |
| Amazon long-term demand forecasting | Forecasting for inventory management across Amazon fulfillment centers. | The AWS customer story describes the application but does not provide a verifiable percentage improvement in the captured text. |
| Kate Spade AI Gift Concierge | Conversational gift recommendations for shoppers. | Amazon describes it as production-ready and as the first production-ready retail AI assistant built with Bedrock AgentCore, using the AWS Agentic Shopping Assistant foundation. |
What do the reported results show?
Walmart’s reported AR metrics
Walmart said in 2024 that its Retina platform powered 10 AR experiences across Walmart U.S. and Sam’s Club, and reported a 10x increase in customer and member adoption, reduced returns and improved conversion. Those are Walmart’s claims: the announcement does not provide the adoption baseline, absolute figures or measurement method. The results therefore should not be read as independently verified or as proof that AR will produce the same outcomes elsewhere.
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ScanAvenger Wireless Portable 1D&2D with Stand Bluetooth Barcode Scanner: 3-in-1 Handheld Scanner, Rechargeable Battery for Inventory - USB Bar Code/QR Reader (1D&2D with Next Gen Stand)
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Walmart’s fashion-timeline claim
Walmart’s current technology page says Trend-to-Product can compress traditional fashion production timelines by up to 18 weeks. That is a Walmart-stated maximum, not a measured result that can be generalized to other retailers or processes.
Amazon forecasting figures
The AWS customer story identifies demand forecasting as an Amazon use case, but the captured page text does not establish a numerical accuracy gain. A percentage should not be inferred from the description.
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What these examples can—and cannot—tell a retailer
The mix of use cases suggests that AI can sit at multiple points in retail operations: helping a shopper discover products, assisting with service, presenting visual or social shopping experiences, supporting merchant decisions, and coordinating inventory and delivery. That is a map of applications, not a ranking of their business value.
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- Match the tool to the task. A shopping assistant, a review summary, a merchant workflow and a demand-forecasting system solve different problems and need different measures of success.
- Check maturity before treating an announcement as precedent. A pilot, an alpha test and a plan do not establish broad production use. Walmart’s October 2024 homepage target, for example, is not confirmed by the cited material as a completed launch.
- Separate capability from outcome. A company description can show what a feature is intended to do. It does not by itself prove that the feature improves conversion, lowers returns or saves time.
- Ask what evidence supports any performance claim. For a useful comparison, look for a defined baseline, the population and period measured, and a method that can distinguish the AI’s effect from other changes.
The evidence here is strongest for identifying named capabilities and their stated status. It does not supply a consistent independent metric for comparing effectiveness across retailers, and it does not support a defensible ranking by return on investment.
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Sources and scope
The examples and status descriptions above come from official Walmart technology and announcement pages, an AWS customer story about Amazon, and Amazon’s account of Kate Spade’s concierge. The descriptions and performance claims are attributed to those companies or vendors; they are not independent audits. No direct source URLs were included with the material for this article, so no external links are supplied.
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