the best AI shopping assistants used by retailers in 2026

This is about the AI shopping assistants retailers run themselves. The ones a store builds into its own app or website, that recommend from its own range, and where the shopper buys without leaving. Not the general assistants like ChatGPT, which are a separate topic. Those come up in the FAQ at the end.

A quick word of perspective first. This is day-1 territory. Retailer AI assistants are the height of fashion, with stores across the world racing to launch one, but a great deal still falls short of the promise. Many are dolled-up customer service chatbots that answer delivery questions and little else. Some sit inside e-commerce sites that have not yet got semantic search and filtering working properly underneath them. The category is real and growing fast. It is also young, and it shows.

Here is the definitive list of the retailer assistants that matter, followed by the part most lists skip: how to measure whether one is doing a good job.

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The retailer assistants

These belong to the stores. They live inside one retailer's app or website, recommend from that retailer's range, and the buying happens without the shopper leaving. If you sell through these retailers, their assistant decides whether you are in the answer.

Alexa for Shopping

Amazon's AI shopping assistant, which replaced the standalone Rufus assistant in May 2026. It sits in the main Amazon search bar and answers product questions, compares options and can complete purchases, all inside Amazon's ecosystem.

If you sell on Amazon, this is the assistant that decides whether you are in the answer before the shopper has even scrolled.

• Write listing titles for clarity, not keyword density. Lead with brand, product type and the main point of difference.
• Structure bullet points around feature, benefit and use case, so the assistant can match you to a need.
• Invest in A-plus content and genuine reviews. Both feed the recommendation, and weak listing data can lose you the answer slot.

amazon.com

Walmart Sparky

Walmart's AI shopping assistant, launched in 2025 and now live across Walmart's app, website and stores. Sparky finds products, summarises reviews and increasingly handles planning tasks like meal plans and replenishment. Walmart reports that shoppers who use it spend meaningfully more per order.

For any brand selling through Walmart, Sparky is becoming the layer between your product and the customer.

• Keep your Walmart catalogue content complete and accurate. Sparky reads the same product data the site does, but judges it for answer quality.
• Make every product page consistent with how the product appears elsewhere. Contradictions across surfaces cost you eligibility.
• Treat reviews as recommendation fuel. Sparky synthesises them, so the substance of your reviews shapes whether it suggests you.

walmart.com/cp/sparky

Checkers Sixty60 Pixie

Pixie is South Africa's first personalised AI shopping assistant, built in-house by ShopriteX for the Checkers Sixty60 grocery app and launched in beta in April 2026. It learns from a shopper's purchase history and restocking habits to predict what they need, surface relevant products and speed up reordering, with conversational features and meal planning on the roadmap. For now it is a personalisation engine rather than a full conversational agent.

For South African grocery brands, Pixie is the local assistant to watch, and a reminder that this shift is not confined to the US. It also has company: Sixty60 leads the South African market, but Woolworths, Pick n Pay's ASAP and other local players are building in the same direction, and the global grocery assistants will not ignore the region for long.

• If you supply Checkers, keep your product data clean in their catalogue. Pixie recommends from what it can read and categorise.
• Reordering is the core behaviour. Consistent naming and packaging detail help a personalisation engine surface you again.
• Watch the roadmap. As Pixie adds conversational search, the same content depth that helps the global assistants will matter here too.

shopriteholdings.co.za

Bunnings Buddy

Buddy is the agentic AI assistant from Bunnings, the Australian hardware giant, launched in 2026 and built on Google Cloud's Gemini. It is pitched as a digital version of a red-vested store team member. Buddy can interpret a photo of a scrawled handwritten shopping list and find the items, break a complex renovation down into step-by-step tasks, estimate project costs and suggest the relevant products, drawing on Bunnings' large library of how-to guides.

It is one of the more genuinely agentic retailer assistants, and a sign of where the capable end of this category is heading. Bunnings is first to scale in Australia, but not alone: Woolworths is bringing agentic AI into its Olive chatbot, and Coles and other Australian retailers are moving the same way.

• If you supply Bunnings, structure your product and project content so it maps to tasks. Buddy thinks in jobs, not just items.
• How-to and project content is the fuel here. The assistant draws on guides, so the depth of that content shapes recommendations.
• Make product attributes precise. Buddy estimates quantities and costs, so accurate sizing and coverage data matters.

bunnings.com.au/buddy

Tesco app assistant

Tesco, the UK's largest supermarket, began a large-scale trial of an AI assistant inside the Tesco app in April 2026. It launched as a beta with around 280,000 staff testing it before a customer rollout later in the year. The first version focuses on meal planning, offering personalised recipe ideas through a two-way conversation, then helping build the matching basket in the app using shopping history and preferences, with a stated aim of cutting cost, time and food waste.

It is a clear example of a major grocer treating the assistant as a careful trial rather than a finished launch. Tesco is not alone in UK grocery: Sainsbury's, Asda, Ocado and others are all developing AI shopping features, so the British supermarket aisle is becoming a contested space for assistants.

• If you supply Tesco, get your recipe-relevant product data right. Meal planning is the entry point, so ingredient-level clarity matters.
• Structured product information drives basket building. The assistant matches recipes to specific buyable products.
• Track the rollout. A beta today becomes a customer-facing surface later this year, so the groundwork is worth doing now.

tescoplc.com

Sephora AI Beauty Chat

Sephora's own in-house assistant, built into its site and app. It gives skincare and makeup routines, product recommendations and step-by-step advice, drawing on millions of reviews and Sephora's own product data. Sephora has also launched a separate Sephora app inside ChatGPT, so the retailer now appears both on its own storefront and inside a general assistant.

For a beauty brand stocked at Sephora, this assistant is a direct route into a high-intent shopper's routine. Other beauty retailers, from Ulta to regional chains, are building comparable tools, so it is becoming a category norm rather than a Sephora advantage.

• Get your ingredient and benefit detail right in your Sephora content. The assistant matches products to specific skin and hair concerns.
• Make sure your products are clearly categorised. Beauty Chat recommends by concern and use, not by brand name.
• Invest in genuine reviews on Sephora. The assistant is explicitly built on review data, so review quality drives recommendations.

sephora.com/beauty/ai-beauty-chat

Target and Instacart assistants

Two more retailer assistants worth tracking. Target is rolling out AI shopping help and integrating Google's Gemini into its experience. Instacart's Cart Assistant handles grocery, with meal planning and product recommendations, and is being white-labelled into chains including Kroger and Sprouts, so one assistant ends up powering many storefronts.

If you sell groceries or sell through Target, these belong on your radar now, not later.

• Keep your retailer-side product data complete wherever you are stocked. These assistants read the catalogue they are given.
• Watch the white-label pattern. One assistant powering many grocers means one content standard reaches all of them.
• Treat each retailer relationship as a separate visibility surface, with its own feed and its own content to maintain.

instacart.com · target.com

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A note on brand-owned assistants

Sitting just next to the retailer assistants is a related category: single brands building their own. L'Oreal Paris runs Beauty Genius, an assistant trained on its own catalogue and dermatologist data that runs skin diagnostics and recommends from over 750 L'Oreal products. Ralph Lauren runs Ask Ralph, a styling assistant that builds shoppable outfits from the Polo range.

The difference from a retailer assistant is range. A retailer assistant sells many brands and a brand assistant sells one, so a brand assistant will not tell a shopper a rival would suit them better. The lesson for everyone else is the same either way: an owned assistant is only possible when the product and expertise content behind it is deep and well-structured.

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How to tell if a retail AI shopping assistant is doing a good job

This is the part that matters once an assistant is live. A retailer that judges it on conversion alone will usually be disappointed, because the conversion effect is real but modest. The fuller picture needs 4 signals.

1. Support ticket reduction

Often the clearest sign an assistant is earning its place, and frequently a bigger effect than the conversion lift. A well-trained assistant absorbs the repetitive pre-purchase questions before they ever become a support ticket. The compatibility question is the classic case: will this work with my laptop, will this fit my model, is this the right part. One retailer running an assistant on an electronics store reported roughly 40% fewer of those compatibility-type questions reaching support after 6 months. If your ticket volume has not moved at all, the assistant is probably not trained well enough on your specific products to be answering those questions confidently.

2. Conversion lift, kept in proportion

Conversion does usually rise, but expect a modest number rather than a dramatic one. The same electronics retailer described the conversion bump as fairly modest, in the low single digits, somewhere around 3 to 4%. That is still worth having, and it compounds, but a retailer who has been promised a transformed conversion rate will read a 3% lift as failure. It is not failure. It is the normal shape of the result. Judge it alongside the other 3 signals, not on its own.

3. Escalation quality

A good assistant knows the edge of its own knowledge and hands off to a human cleanly when it reaches it. This is worth measuring directly: how often the assistant escalates, at what point in the conversation, and whether those escalations land with a person who can actually help. An assistant that never escalates is not confident, it is overconfident, and it will be answering some questions wrongly. An assistant that escalates at the right moment is doing exactly what it should. Train it on when to bail out to a human, not just on what to say.

4. Trust signals

The hardest to measure and arguably the most important. Shoppers can tell the difference between an assistant that admits what it does not know and one that invents an answer to seem helpful, and they trust the honest one more. An assistant that says it is not sure and offers to connect the shopper with a person holds trust. One that confidently makes something up loses it, often quietly, with no complaint logged. Watch for the soft indicators: repeat use of the assistant, sentiment in post-chat feedback, and reviews that mention the AI warmly rather than with frustration.

Put the 4 together and the test is simple. A retail AI shopping assistant is doing a good job when it quietly removes repetitive questions, nudges conversion up a little, hands off gracefully when it should, and is honest enough that shoppers come back to it. Any one signal on its own can mislead. The 4 together tell the truth.

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The pattern underneath all of it

Every one of these assistants, and every one of those 4 metrics, traces back to the same thing: the quality of the retailer's product content. An assistant trained on thin, vague or contradictory product data cannot answer the compatibility question, cannot earn the conversion, cannot tell when it is out of its depth, and ends up inventing answers. An assistant trained on clear, accurate, well-structured content can do all 4.

The retailers winning with AI assistants are not the ones who picked the cleverest tool. They are the ones whose product content was good enough for a clever tool to work with. That is the real groundwork, and it is the same groundwork whichever assistant a retailer chooses.

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Frequently asked questions about retail AI shopping assistants

What is a retail AI shopping assistant?

A retail AI shopping assistant is a conversational tool a retailer deploys on its own app or website to help shoppers discover products, compare options, get answers and complete a purchase, all in the retailer's own environment. It recommends from that retailer's range. This is different from a general AI assistant, which is not tied to any retailer and recommends across the whole market.

How is a retail AI shopping assistant different from ChatGPT, Gemini or Perplexity?

This is the key distinction, and it is why the general LLM assistants are not in the main list above. A retail AI shopping assistant lives inside one store and sells that store's range, with the purchase completed there. The general assistants, ChatGPT, Google Gemini, Perplexity and Microsoft Copilot, are not owned by any retailer. They recommend across the whole market and usually send the shopper elsewhere to buy. They are a genuinely separate category, with their own rules for visibility, and they deserve their own treatment rather than a paragraph here. Most shoppers now use both kinds: a general LLM to research broadly, a retailer assistant to complete the purchase.

How do you measure whether a retail AI shopping assistant is doing a good job?

Look at 4 signals rather than 1. Support ticket reduction, since a good assistant absorbs repetitive pre-sales questions before they reach a human. Conversion lift, which is usually real but modest, often in the low single digits. Escalation quality, meaning how cleanly and how appropriately it hands off to a person. And trust signals, including whether the assistant admits what it does not know rather than inventing an answer, and whether shoppers come back to it. Any single metric can mislead. The 4 read together give an honest picture.

Should a retail AI shopping assistant reduce customer service tickets?

Yes, and this is often the clearest sign it is working. A well-trained assistant absorbs the repetitive pre-purchase questions, such as whether a product is compatible with a shopper's existing setup, before they ever become a support ticket. Retailers report this as one of the largest measurable effects, sometimes larger than the conversion lift, with reductions in compatibility-type questions reaching around 40% in one electronics retailer's experience. If ticket volume has not moved, the assistant is probably not trained well enough on the retailer's specific products.

Why does it matter that an AI shopping assistant knows when to escalate to a human?

Because an assistant that tries to answer everything will confidently answer some things wrongly, and confident misinformation scales instantly across thousands of conversations before anyone notices. An assistant trained to recognise the edge of its knowledge and hand off to a human keeps the shopper's trust. Retailers consistently find that customers trust an assistant more when it admits what it does not know than when it makes something up. Knowing when to bail out to a person is a feature, not a shortcoming.

What should a retail AI shopping assistant be trained on?

It should be trained on the retailer's own specific product data, not just general world knowledge. The retailers seeing the strongest results train the assistant deeply on their actual catalogue, including compatibility detail, specifications and genuine reviews, and many test it against past support tickets before launch to measure its accuracy. An assistant is only as good as the product content behind it, which is why content quality, rather than the choice of tool, is the real foundation. We will cover how to prepare retail listings for AI assistants in detail in an upcoming post.