AI is completely changing the way consumers shop.
Traditionally, online shopping has involved searching on Google or visiting multiple retailer websites to compare products. Now, generative AI allows you to combine these actions into a single conversation.
Rather than knowing exactly what they want, customers can describe a problem, preference, requirement, or budget and receive suggestions tailored to that context.
Because of this, AI search has become a route to product discovery that brands need to optimize for. So how do you ensure your products are among those considered and recommended?
Let’s dive into AI shopping and suggest some tips to ensure you’re in the best position to show up in AI recommendations.
Using AI to find and compare products has quickly become the norm for millions of shoppers, mostly due to the advancement of AI models over recent years.
AI allows consumers to search for products by their needs rather than by a specific product name or brand, interpreting detailed requests that include price, intended use, product characteristics, and other personal preferences.
This alone changes the product discovery game completely.
For example, a consumer might ask: "find me a lightweight, waterproof jacket for hiking in Colorado under $200," then ask some follow-up questions to further personalize the results they receive.
Let’s look at some more reasons why shoppers might use AI to find products online.
AI shortens what could once be a lengthy process: comparing competing models and products.
It can evaluate specifications, features, compatibility, sustainability, or anything in between, in just a matter of seconds.
Shoppers might use it to make sense of unfamiliar product categories, collate customer reviews across multiple review sites, or compare products head-to-head in areas such as price or durability.
A shopper comparing laptops, for example, might ask AI to explain the differences between three different models and recommend the best option based on cost versus performance.
It’s a method that’s becoming widespread: McKinsey’s US ConsumerWise research found that, among consumers using AI in their holiday season shopping journey, 49% used it to compare options and 48% to find the best deals or discounts.
As well as comparing product specifications and details, customers often use AI to identify more affordable alternatives to products they’re considering.
Tools can help compare advertised prices and promotions across different retailers in a fraction of the time this style of comparison used to take.
This gives a lot of power back to the consumer, as they’re able to search prices across hundreds of sites in just seconds. Some dedicated shopping assistants even have price-monitoring functionality to take advantage of the best deals in real time.
The NRF and IBM’s January 2026 global consumer research tells us that 41% used AI assistants for product research, 33% to look for reviews, and 31% to search for deals.
AI hasn’t replaced the search bar, but it has given shoppers a new way to answer their important questions in an instant.
Beyond the question-and-answer style research offered by popular LLMs like ChatGPT and Claude, AI shopping is becoming increasingly sophisticated.
AI shopping assistants are AI-powered tools designed to help consumers discover, research, compare, and sometimes purchase products.
Unlike general-purpose AI platforms that can support shopping research, retailer-specific assistants integrate into eCommerce websites or marketplaces, with agents capable of completing defined actions, such as monitoring prices or assembling shopping carts.
Let’s look at some real-world examples and their capabilities.
Consumers can research products, refine requirements through conversation, and compare options.
Shopping research can draw on merchant data, publicly available product information, and other relevant retail sources.
Google’s AI shopping experiences combine conversational discovery with its product catalog and merchant information.
Its Universal Cart launched in the US on October 15, 2026, allowing users to save products from multiple retailers in one place.
Amazon’s assistant helps shoppers ask questions, compare choices, and find relevant products using information from its retail system.
Previously named Rufus, it was renamed in May 2026 to Alexa for Shopping.
Agentic commerce is the step beyond conversational AI assistance, where AI agents shop on behalf of consumers.
A consumer might ask an AI assistant to find a pair of running shoes in their size, monitor the price, and help purchase them when the price falls below a specified amount.
Some services already support elements of this behavior, but the more autonomous side of purchasing remains at an earlier stage of adoption than standard AI product discovery.
McKinsey found that, among US AI users, those who completed a purchase with AI increased from approximately 10% to 17% year over year on average across the measured categories.
However, Gartner’s January 2026 US survey found the willingness to let AI make purchasing decisions reached only 11%, even in lower-stakes categories.
These findings tell us that adoption of more automated shopping behaviors is at the lower end of the scale, meaning brands still have time to get ahead of the curve.
Brands can no longer assume every product discovery journey starts on Google or their own website.
More shoppers are starting their search on platforms like ChatGPT or Google AI Mode, meaning businesses need to change how they position their products.
This is especially true in the case of product information, which should work for both consumers and the systems that retrieve, summarize, and compare it.
It also changes the mindset of shoppers arriving on your website. AI-referred buyers might arrive with stronger purchase intent, as they’ve already evaluated alternatives and narrowed their preferences before visiting your website.
According to Adobe, AI traffic drives 83% higher revenue per visit in the US, with a +90% conversion rate vs. non-AI visits. This suggests that AI-referred shoppers could be further along the purchasing journey, making AI discovery a valuable opportunity for eCommerce brands.
So, the question you’re probably asking at this point is: how do I make my brand show up when users ask questions about my product category?
Firstly, let’s cover how it works. There’s no universal ranking formula for AI recommendations, and different platforms retrieve and evaluate information differently.
Rather than ‘ranking’ in AI search, it’s more of a probability game. The question is: how can we give ourselves the best chance of appearing in AI answers?
Published guidance from many AI platforms tells businesses to prioritize accessible content, relevant product information, accurate catalog data, and established SEO practices.
Let’s look a little deeper into some of these tactics.
AI shopping prompts are often more detailed than traditional product searches. Rather than searching "men’s sneakers," a shopper might request: "find me the best deals for white sneakers with free delivery and returns."
Because the searches contain more detail, product pages should aim to answer the questions shoppers are asking.
Your brand should move beyond generic, promotional product descriptions, including details on specific materials, dimensions, functionality, compatibility, and intended applications.
On top of this, it’s a good idea to address common buying questions on product pages, giving AI tools a direct source for answering the questions shoppers ask most.
A quick example could be a retailer selling skincare. It shouldn’t simply describe its product as a premium moisturizer, but instead explain the skin types it’s designed for, key ingredients, whether it’s fragrance-free, or how it fits into a skincare routine.
A consumer asking AI to ‘find a fragrance-free moisturizer for dry skin’ provides more context than a basic search, meaning the product pages with extra, relevant detail are more likely to be recommended.
AI product discovery depends partly on the quality and freshness of the information available on your website.
Accurate product titles, descriptions, prices, and specifications are key to discovery, while correct stock availability and product variants help create a consistent experience from AI platform to website.
If a product is listed as available on a retailer’s website but out of stock in a marketplace feed, that inconsistency can contribute to incorrect product information or a poor purchasing experience.
OpenAI’s product feed specifications emphasize current pricing, product availability, and seller information, showing the importance of accurate and up-to-date information.
Good content on your website needs to be supported by information that search engines and shopping systems can interpret.
These approaches should complement one another rather than being treated as interchangeable.
To get this in place, you should audit your product markup, merchant feeds, and website crawlability with your eCommerce and development teams.
Your brand needs to do more than publish information about what it sells. You also need to provide reasons to be recommended.
Genuine customer reviews and product ratings have always been huge reputation-builders, but with how often AI tools cite review pages, they’re more important than ever.
Independent coverage and reputable third-party references are also often cited by AI platforms, as well as original product demonstrations and buying guides.
By taking your products further than the product page, you give AI tools more places to find your brand. However, while credible coverage and making useful information accessible are sensible practices, the number of mentions your product has doesn’t guarantee inclusion in LLM-generated recommendations.
OpenAI’s shopping documentation explains that ChatGPT can consider product metadata and third-party information, and may summarize reviews when presenting shopping results. Basically, it’s about more than just your product pages.
Product suitability is only one part of the equation. Customers also want confidence that an order will arrive when expected and can be returned without unnecessary difficulty.
A shopper asking AI to recommend a birthday gift that can arrive in California in three days has requirements that go beyond the product itself.
By displaying accurate stock availability and delivery options on your website, and keeping this information up to date, you make it easier for AI shopping tools to retrieve and communicate relevant details to potential customers.
Returns are also a big driver of sales. As well as delivery cut-offs and product information, it’s a good idea to make returns details easy to discover on product pages.
Google Merchant Center recommends providing accurate shipping speeds, delivery costs, and returns information on your website, as these details are used across its shopping platforms.
It’s a good idea to measure your visibility in AI shopping experiences, even while genuinely useful measurement tools continue to develop.
Here’s a quick approach you can apply immediately:
Dedicated tools can speed up this process by automatically running a selection of prompts each day. If you’re looking to save time while monitoring AI visibility, it might be a good idea to research these tools.
AI helps consumers research and purchase products, but the retailer still has the responsibility of delivering the promised experience.
Here’s where a tech-first, reliable fulfillment provider like fulfilmentcrowd can help.
AI-generated recommendations may persuade a shopper to purchase a product there and then.
Therefore, inventory availability needs to be accurate when the buyer reaches the retailer.
If the AI tool thinks an out-of-stock item is still in stock, it can lead to a frustrating customer experience.
Having real-time inventory information that updates automatically across your website and other sales channels can help your brand avoid discrepancies between reported stock levels and actual availability.
As well as the product itself, customers may make purchasing decisions based on delivery speed, availability, or shipping costs.
But promising and delivering are two different things entirely.
No matter where or how a customer discovers your brand, if fulfillment or delivery fails, customer loyalty often disappears in an instant.
Distributing inventory across multiple fulfillment locations can also help brands reduce delivery times and shipping costs.
A clearly defined returns policy can give customers the confidence to purchase from brands they’ve never interacted with before.
Effective returns remain an important part of the post-purchase experience, regardless of how the customer discovers the product. Research suggests that nearly two-thirds (63%) of US consumers have walked away from a retailer or abandoned a purchase because of its return policy.
As well as returns, live tracking and proactive delivery communication can further build trust with new customers, providing a better experience after checkout.
While AI in eCommerce continues to develop, much of the groundwork retailers need to complete is already relevant today.
Here’s where the industry is likely heading over the coming months and years.
AI shopping recommendations will likely become even more relevant as systems incorporate contextual information, previous interactions, and consumer preferences, subject to privacy choices and platform capabilities.
This creates opportunities for products to be recommended based on suitability rather than broad category popularity alone, opening the door to brands selling products in specialized areas.
Shopping assistants are becoming more common, as buyers use them to monitor prices, create carts, and complete certain purchasing actions on their behalf.
In November 2026, Amazon announced an invitation-only US rollout allowing selected customers to text Alexa for Shopping to find products, build carts, and make purchases.
It offers a glimpse into the future of buyer convenience; however, a solid operational backbone is still essential to protecting your brand’s reputation.
Google’s Universal Commerce Protocol gives you a great idea of how things are developing, as it aims to help retailers make online shopping easier and more connected.
These emerging commerce standards are making product catalogs, carts, and checkout functionality more accessible to authorized AI shopping experiences, allowing AI agents to discover the information they need and complete purchases.
But retailers don’t necessarily need to invest in AI agents of their own. For many brands, the essentials stay the same: getting existing product information, feeds, inventory visibility, and customer experience right.
AI is already having a huge impact on the way US shoppers research, compare, discover, and even purchase products.
Shopping assistants and agentic commerce are already extending these capabilities regularly, but adoption among consumers stays varied.
Brands still have an opportunity to prepare by investing in relevant content, accurate product information, and accessible shopping data.
But no matter where or how your customers buy your products, reliable fulfillment and positive post-purchase experiences remain fundamental to converting first-time interest into long-term customers.