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AI personalizes retail shopping by using signals such as browsing activity, stated preferences, product details and shopping context to make discovery, recommendations, offers and support more relevant. The most useful systems connect those signals to accurate product information and let shoppers keep control; a fluent chatbot alone is not enough.
Where AI can personalize a shopping trip
Personalization can appear at several points, not just in a row of recommended products. Amazon says its shopping experience uses activity to tailor recommendation types and product descriptions. Retailers may also tailor product discovery, promotional offers, customer support and post-purchase help.
- Discovery: Help shoppers narrow a broad assortment using their needs, preferences or occasion.
- Recommendations: Connect products to shopping activity or stated interests, including related items across categories.
- Product information: Adapt descriptions or summarize relevant details, provided the underlying catalog information is accurate.
- Offers: Present promotions that fit a shopper or context rather than relying only on mass promotions.
- Support: Answer questions before or after purchase, with an option to escalate when an answer is uncertain or a person is preferred.
Amazon’s account of personalized recommendations and descriptions is a company description of its own systems, not an independent measurement of their effects: Amazon on personalized shopping recommendations.
How conversational AI connects a question to products
A conversational assistant gives shoppers a natural-language way to explain what they need. In McKinsey’s description, an LLM agent interprets a message and connects to retailer data such as the SKU catalog and to other models, including an analytical personalization engine. The model is therefore one part of a larger system: it needs relevant, current product data and carefully designed connections to commerce systems.
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That grounding matters. A polished answer can still mislead if product attributes are wrong, a product is unavailable, or the system invents a feature. Retailers need product design resources and frequent user testing to calibrate responses, according to McKinsey’s analysis.
Examples: deployed features and announced plans
Retailer and provider descriptions show different levels of implementation. A case study describes a particular deployed experience, while a company announcement may describe a plan that should not be treated as a completed rollout.
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| Retailer | What the source describes | Status and qualification |
|---|---|---|
| Amazon | Machine learning and AI for recommendations across the shopping journey, plus generative AI to tailor recommendation types and product descriptions based on shopping activity. | Described by Amazon in a company article dated September 19, 2024. |
| Walmart | Wallaby, a series of retail-specific LLMs trained with Walmart data; a more personalized support assistant; and a content decision platform used in selected areas of Walmart.com. | Walmart’s October 9, 2024 announcement said an individualized homepage was planned for U.S. rollout by the end of the following year. That dated plan does not confirm the full rollout is currently available. |
| Best Buy | A Gift Finder using preferences, summaries of thousands of product reviews, and product answers based on manuals and vendor-provided information. | Google Cloud’s customer case describes the Gift Finder as deployed and built on Gemini Enterprise Agent Platform. The case is a provider-published account, and its captured page does not show a publication date. |
Sources: Amazon’s September 2024 description, Walmart’s October 2024 announcement and Google Cloud’s Best Buy case study. These descriptions establish what the companies say they built or planned; they do not independently prove customer or financial impact.
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What reported business results do—and do not—show
Published estimates suggest opportunities, but they are not guarantees for an individual retailer. Their methods and scopes differ, so they should not be treated as directly comparable benchmarks.
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- Personalized promotions: Boston Consulting Group said returns on personalized offers in its work with large retailers could be as much as three times higher than 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.
- Cross-selling: BCG said personalized product recommendations can drive 10–20 percentage points of cross-sell for multi-category retailers.
- GenAI decision systems: McKinsey’s 2023 analysis framed up to 5% in incremental sales and 0.2–0.4 percentage points in EBIT margin improvement as an expectation based on early work with retailers, not a generally realized result.
- Retail chatbots: McKinsey said a 2–4% basket uplift can justify LLM costs in scenarios based on its experience building retail chatbots; this is not a result to assume for every deployment.
Sources: BCG on retail personalization and McKinsey’s guide to GenAI for retailers. A retailer evaluating its own system should measure outcomes in its own context rather than treat these figures as a forecast.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why speed and control both matter to shoppers
Convenience is a meaningful part of the experience, but shoppers may not want an agent to make 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 in deciding where to shop. In the same report, 46% said they were somewhat or very unlikely to use a digital assistant or agent to handle an entire shopping trip, while 27% wanted clear transparency about data use and third-party involvement.
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These are findings from Walmart’s 2025 report; the available page summary does not provide its full sampling methodology. They point to a practical design principle, not a universal preference: make assistance useful without requiring shoppers to surrender the final choice. The report states, “AI holds a lot of promise in transforming the retail landscape, but only if we don’t lose sight of what consumers want: convenience without compromise, speed without risk, personalization with control and consent.”
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What retailers should check before deploying personalization
A retailer choosing a system or deciding whether to build one can evaluate the experience against the job it needs to do, the data it depends on and the safeguards shoppers need.
- Define the job and category: Is the goal discovery, gift-finding, service, replenishment or offers? Consider whether the category depends on expert judgment or sensory evaluation.
- Check data grounding: Confirm catalog attributes, manuals, availability and inventory connections are accurate and current. Determine which customer preferences are needed and whether less data could do the job.
- Test answer quality: Check factual accuracy, recommendation relevance and review summaries. Decide how the system should signal uncertainty and when it should hand off to a person.
- Choose an operating model: McKinsey describes a spectrum from using existing tools (“taker”), to customizing available models with proprietary data (“shaper”), to building foundation models (“maker”). Its analysis suggests most retailers are likely to use existing tools for internal value-chain tasks, while customer-experience transformations may call for more customization.
- Plan for integration and capability: Account for data quality, privacy, expertise, implementation expense and the connections needed between the assistant, product catalog and commerce systems. McKinsey identifies these as constraints on scaling.
- Measure more than sales: Compare conversion or basket outcomes alongside satisfaction, trust, returns and support resolution. Use a sound comparison design; a change after launch alone does not show that AI caused it.
- Keep human control: Let shoppers retain the purchase decision and reach a person when the situation or their preference calls for it.
Best Buy’s case illustrates the value of connecting a gift-finding experience to preferences, review summaries, manuals and vendor information. Best Buy’s Aamer Charania described the goal as bringing “humanity to an online interface in a conversational way that people can trust.” The operational lesson is broader than any one platform: relevance and trust depend on the quality of the information and the design around the model.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
