Retail AI personalizes more than product recommendations: it can shape search and discovery, product descriptions, offers, customer support, and help after a purchase. Its value depends on whether it connects relevant shopping context to accurate product information—and whether shoppers can understand and control how their data is used.
How AI personalizes the shopping experience
Retailers can use shopping activity, stated preferences, product details, and the immediate shopping context to make a large catalog easier to navigate. The aim is to surface items, information, or assistance that fit a shopper’s needs rather than showing every customer the same path.
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Amazon said in a September 19, 2024 company article that it uses machine learning and AI for recommendations across the shopping journey. It also described using generative AI to tailor recommendation types and product descriptions based on a customer’s shopping activity. These are Amazon’s descriptions of its own systems, not an independent assessment of their impact. Amazon’s account of personalized shopping
- Discovery: Search and browsing can help identify products that match a shopper’s expressed needs or activity.
- Recommendations: A retailer can suggest related or complementary items, rather than relying only on a generic “you may also like” row.
- Product information: Tailored descriptions or answers can bring relevant attributes and explanations forward.
- Offers and service: Personalization can also influence promotions, customer support, and post-purchase assistance.
What conversational shopping assistants add
A conversational assistant gives shoppers a natural-language way to describe what they need, compare options, or ask follow-up questions. In McKinsey’s 2023 description, a large language model can interpret a customer’s message and connect it to retailer data such as the SKU catalog and to other systems, including a personalization engine. The language model is the interface; current, well-structured retail information is what makes its answers useful.
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A deployed example: Best Buy’s Gift Finder
Google Cloud’s customer case study describes Best Buy’s deployed Gift Finder, built on Gemini Enterprise Agent Platform. The case says the tool uses shoppers’ preferences and summaries of thousands of product reviews. It also describes product answers grounded in manuals and vendor-provided information. Best Buy’s Aamer Charania said the aim was to bring aspects of the in-store experience into customers’ homes through a conversational online interface. This is a provider-published case description; it establishes what Google Cloud and Best Buy say was deployed, not independently verified business results. Google Cloud’s Best Buy case study
What grounding means for shoppers
An assistant should draw product claims from reliable information about the actual item—not merely generate plausible-sounding text. If it cannot confirm a feature, compatibility, or availability, it should say so or direct the shopper to a person. That distinction matters most when a wrong answer could lead to an unsuitable purchase.
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Retail examples: deployments and announced plans
| Retailer | What the source describes | Status and qualification |
|---|---|---|
| Amazon | AI and machine-learning recommendations across the shopping journey; generative AI used to tailor recommendation types and product descriptions based on shopping activity. | Described by Amazon in a company article dated September 19, 2024. The article is a company account, not an independent impact evaluation. |
| Walmart | Wallaby, a series of retail-specific large language models trained with Walmart data; a more personalized customer-support assistant; and a content decision platform used in selected areas of Walmart.com. | Walmart’s October 9, 2024 announcement also said an individualized homepage was planned for U.S. rollout by the end of 2025. That dated plan does not establish that the full rollout is currently available. |
| Best Buy | A Gift Finder, personalized recommendations, review summaries, and product answers based on manuals and vendor information. | Described as deployed in Google Cloud’s customer case study. The case page does not show a publication date in the captured result. |
Walmart’s announcement framed its work around adapting technology to individual preferences and needs. Its descriptions distinguish tools it said were already in use in selected areas from a planned homepage rollout; a plan should not be mistaken for a confirmed current feature. Walmart’s October 2024 announcement
What the reported business results do—and do not—show
AI personalization can create commercial opportunities, but published figures refer to particular analyses or modeled scenarios. They are not guaranteed results for a retailer adopting a tool.
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| Source and date | Reported figure | Scope |
|---|---|---|
| Boston Consulting Group, 2024 | Up to three times the returns from mass promotions | BCG said returns on personalized offers in its work with large retailers could be as much as three times higher than returns on mass promotions. It also observed that retailers averaged less than 5% of promotional spending on personalized offers. These are BCG’s reported analysis and observation, not a universal controlled benchmark. |
| Boston Consulting Group, 2024 | 10–20 percentage points of cross-sell | BCG said personalized recommendations can drive this amount of cross-sell for multi-category retailers. |
| McKinsey & Company, 2023 | Up to 5% of incremental sales and 0.2–0.4 percentage points of EBIT margin improvement | McKinsey presented these as expectations based on early work with retailers on GenAI-powered decision-making systems, not as guaranteed outcomes. |
| McKinsey & Company, 2023 | 2–4% basket uplift | McKinsey said this uplift can justify large-language-model costs in scenarios based on its experience building retail chatbots. It is not a general realized result. |
These estimates do not establish that AI alone caused an outcome, or that results will transfer across categories, retailers, and customer groups. Retailers need to measure business outcomes against an appropriate comparison and track customer experience as well. BCG’s 2024 analysis of personalized offers · McKinsey’s analysis of GenAI in retail
What shoppers want from AI-assisted retail
Convenience matters, but it does not mean shoppers want to hand over every decision. In Walmart’s Retail Rewired Report 2025, 69% of respondents said the speed of the entire shopping experience was very or somewhat important when deciding where to shop. The same report found that 46% said they were somewhat or very unlikely to use a digital assistant or agent to handle an entire shopping trip. These are findings from Walmart’s report; the captured summary does not provide its full sampling methodology.
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In that report, 27% wanted clear transparency about data use and third-party involvement. Walmart’s stated conclusion was that personalization should preserve consumer control and consent alongside convenience and speed. Walmart’s Retail Rewired Report 2025
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What retailers need to get right
Product data and availability
Recommendations and conversational answers are only as dependable as the product information behind them. Attributes, manuals, vendor details, and current assortment information need to be accurate and connected to the shopping experience. An answer that sounds confident but describes a feature the item does not have can undermine trust.
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Relevant, controlled use of customer data
Retailers should be clear about which signals inform personalization and give shoppers meaningful control over data use. Use the context needed to help with the task, rather than collecting or inferring more than the experience requires. Walmart’s 2025 report specifically highlights transparency, privacy settings, and data minimization as consumer concerns.
Integration, expertise, and cost
McKinsey identifies data quality and privacy concerns, limited resources and expertise, and implementation expenses as barriers to scaling retail AI. It describes three approaches: a “taker” uses existing tools, a “shaper” customizes available models with proprietary data, and a “maker” builds foundation models. McKinsey says many retailers are likely to use existing tools for internal value-chain tasks, while customer-experience changes may call for greater customization. The right choice depends on the job, available data, capabilities, and the degree of control a retailer needs.
Testing and human escalation
McKinsey emphasizes product design resources and frequent user testing to calibrate conversational systems. Retailers should check whether answers are relevant and grounded, whether review summaries fairly represent the source material, and whether uncertain or high-stakes questions can reach a knowledgeable person. The customer should retain the final choice.
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Evaluate a personalization feature against a sound comparison rather than attributing every change in sales to AI. Track relevant business outcomes, such as conversion or basket size, alongside satisfaction, trust, returns, and customer-support resolution. A system that drives clicks but increases unsuitable purchases or erodes confidence is not a good customer experience.
Quick Recap
How to judge whether a personalized experience is useful
- Start with the shopping job: Is the tool meant to help discover products, find a gift, get service, replenish an item, or understand an offer?
- Check the category: Does the decision need expert assurance, sensory judgment, or careful compatibility checks?
- Look for grounded answers: Can the retailer connect recommendations and responses to accurate product information and relevant context?
- Consider control: Can shoppers understand how personalization works, adjust relevant preferences, and choose not to use an assistant?
- Keep a human route: Is there a way to get help when the system is uncertain or the shopper wants assurance?
- Judge outcomes together: Does the experience help shoppers make sound decisions as well as improve a retailer’s measured business results?
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