The era of AI shopping is here. Here’s how to adapt your online presence accordingly.

According to Klaviyo’s 2025 AI shopping index, 81% of consumers have used AI tools while shopping.

The same survey found that 60% of consumers who used AI shopping assistants in the past 6 months have switched where they shop because another website or app had an AI shopping assistant.

AI direct shopping is here. In September 2025, OpenAI announced that shoppers can purchase products directly from US Etsy sellers in chats via Instant Checkout—and Shopify brands are coming soon.

AI-driven shopping experiences are changing how consumers discover and buy products online, and conversational commerce is becoming more important for brands to adapt to.

If you’re not yet thinking about AI shopping, your customers are probably already using it to find your competitors.

Here, we walk you through the AI shopping landscape, and offer best practices for preparing your brand to not only adjust to the new reality, but make the most of it.

How does AI shopping work?

Think about the last time you used a search engine to browse products online. Your search likely started out broad, and you probably spent a fair bit of time filtering through results to find what you were looking for.

AI shopping flips that on its head, moving from traditional search-based ecommerce to conversational ecommerce. Customers can now have full conversations with AI assistants about what they need.

ChatGPT, for example, now drops product recommendations and shopping results directly into conversations. Amazon has also developed their own AI shopping assistant named Rufus to help shoppers navigate product catalogs conversationally.

Other platforms are following suit in creating new shopping ecosystems. Google has rebuilt its shopping experience with AI Mode. Perplexity launched its Buy with Pro feature for in-platform purchases. And Microsoft is partnering with brands like Ralph Lauren to create the AI shopping assistant Ask Ralph.

These AI shopping systems analyze massive amounts of structured product metadata like customer reviews, detailed descriptions, and category tags. Then, the AI delivers personalized recommendations that match user requests.

To end up in results, brands need to first make sure they’re presenting well-organized and detailed product information that AI models can easily reference. Unlike traditional SEO practices that optimize for specific keywords, AI shopping requires optimizing for conversational queries. This changes a lot about how brands should present their products online.

What are consumers looking for when shopping with AI?

According to Klaviyo’s consumer AI shopping survey, these are the top benefits consumers want from AI shopping assistants:

1. The best deals and price comparisons: Consumers are most interested in using AI shopping tools to find the best possible deals and discounts, compare prices between brands, and get alerts on other deals.
2. Faster product discovery and check-out: AI assistants can cut down on the time it takes a shopper to go from discovery to check-out, like buying directly from ChatGPT.
3. Personalized product recommendations: Shoppers want AI to understand their preferences, past purchases, and current desires, so they can zero in on products that best fit their needs. Broad “customers also bought” suggestions might not cut it anymore.
4. Simplified access to product information: Price range, reviews, shipping information, and return policies matter most to shoppers when making a purchase decision, according to Klaviyo’s 2025 BFCM forecast. AI shopping assistants surface these quickly and provide the most relevant information to shoppers looking to save time.
5. Decision fatigue and stress reduction: Sometimes less is more. If shoppers are faced with 500 options, it becomes more overwhelming than helpful. Shoppers want AI to narrow things down to the few items worth their consideration.

6 ways to prepare your brand for AI shopping

AI shopping is still new, and you can rest assured that no brand has it all figured out yet. But it’s certainly been long enough since the launch of ChatGPT in November 2022 that we can provide some concrete guidance on how to optimize your online store for AI shopping.

Here’s where to start so you can begin showing up in results:

1. Audit your structured product metadata

AI platforms index your product information similarly to traditional search engines. Large language models (LLMs) use various bots (such as ChatGPT’s GPTBot) to crawl and index the raw HTML response of your structured data.

Keep in mind that, by default, GPTBot adheres to your site’s robots.txt file. If you want your product information to be indexed, you’ll need to make sure LLMs can access it.

As a general rule, make sure your product information pages:

• Are clear and scannable
• Load fast
• Have mentions on high-authority websites

As of now, most AI shopping assistants pull product data from first-party content owners and third-party content to get information on prices, descriptions, and reviews. However, OpenAI has opened applications for merchants to submit their product feeds directly to ChatGPT, which can give brands more control over the accuracy and depth of their product information.

Here’s where you can begin optimizing the flow of product data for AI shopping:

• Existing product feeds: Share as much of your product data as possible, so that shopping platforms can crawl your site and provide accurate recommendations to shoppers. This data typically includes product names, descriptions, prices, images, and availability. This will give your brand a good foundation for LLMs to pull from when they’re crawling for product information.
• Data depth: According to feed management experts, AI shopping assistants use unique product identifiers like Global Trade Item Numbers (GTINs) and Stock Keeping Units (SKUs) to confirm that your products exist and have accurate representation in LLMs. If these identifiers are incorrect or not included, your products may not show up in search results and recommendations.

Check available platform integrations

In September 2025, OpenAI announced an integration between ChatGPT and Stripe that allows shoppers to make purchases directly from ChatGPT conversations. That means businesses using Stripe can opt in to process agentic payments with their existing infrastructure.

Shopify will soon integrate with ChatGPT’s Instant Checkout, allowing more than 1 million merchants to enable direct purchases through ChatGPT. If you’re on Shopify, this integration will automatically share your product data with OpenAI.

If your platform doesn’t yet have a direct integration with AI, you can prepare by properly formatting your product feed. Most ecommerce platforms can export product data in formats like XML, JSON, or CSV—the same formats AI platforms accept.

Set up automated syncs to keep product information current

Nothing hurts your AI shopping visibility faster than outdated inventory data. Use your ecommerce platform’s built-in tools or apps to automatically sync changes when they happen. Connect to feed management services like GoDataFeed that can push product feed updates to multiple channels, including AI platforms.

The tools you’re already using for Google Shopping and social commerce will form the foundation of your AI shopping strategy. The key is making sure your feeds are comprehensive, accurate, and up to date.

2. Optimize your product catalog details for LLMs

AI shopping has changed how people search for product information. Whereas traditional search showed shoppers a list of links to dig through, AI shopping synthesizes information from multiple sources into a single conversational response, compressing what used to be hours of research across review sites, forums, YouTube, and other sites into one interaction.

To meet the demands of this new model, brands need to optimize their product description pages for longer, more conversational queries that resemble actual questions. For example, instead of searching for “leggings for tall people,” shoppers may ask an AI interface, “Are there leggings that won’t become see-through during hot yoga for someone who’s 5’8”?”

These extended queries need detailed information that LLMs can scan and serve up during a product discovery conversation. That means the way online stores present product information needs to change.

Traditional SEO headlines vs. AI-optimized text

Traditional SEO headlines have grown in word count over the years, but they’re still limited compared to AI-optimized text.

A typical product title on Amazon, for example, might be something like, “women’s stretch pants, black yoga leggings, comfortable activewear, size XS–XXL.”

But an AI-optimized approach would be much more encapsulating and conversational. That same example above may turn into something like, “These high-waisted black leggings feature a 4-way stretch fabric that moves with you during yoga, running, or everyday wear. The forgiving waistband makes them ideal for maternity wear or all-day comfort. Available in sizes XS through XXL, with a 28-inch inseam that works well for heights between 5’4” and 5’10”.”

Also, ChatGPT and other AI shopping assistants often surface products based on aggregated reviews, expert recommendations, and community discussions. That means your product descriptions need to contain the kind of information shoppers would normally find by reading through Reddit threads or watching YouTube reviews.

How to write AI-ready product descriptions

• Answer cross-platform questions. Write descriptions that address what customers would normally ask across multiple sources. Don’t just say, “moisture-wicking fabric.” Rather, explain how a product “stays dry during hot yoga sessions, based on customer feedback.”
• Provide comparative context. Help AI understand where your product fits among your competitors. Instead of saying you use “high-quality materials,” say your product is “lighter than cotton but more breathable than synthetic blends.”
• Include specific use cases and benefits. When people interact with AI shopping assistants, they’re often implicitly searching for use cases and benefits. Make sure your product description pages spell these out, with copy like “Works well for yoga, running, or everyday wear, but our customers report the compression isn’t supportive enough for high-impact activities like CrossFit.”
• Structure product details for excerpting. Unlike traditional SEO, where the whole page ranks, AI often pulls specific sentences or paragraphs to serve them up alongside pieces of text from other sources. That means each product attribute should have enough context to be understandable when quoted on its own. So make sure to give enough context in elements like FAQs, table summaries, comparison tables, and customer testimonials quotes, so that when seen on their own, they tell the full story.
• Use consistent naming conventions. Pick one product name and use it everywhere—on your site, in emails, on social media, and in your product feeds. Answer engine optimization (AEO) experts recommend avoiding name variations to reinforce entity relationships. If you call them “comfort leggings” in one place and “stretch pants” in another, AI models struggle to recognize they’re the same product, splitting your brand presence across AI responses.

3. Test how your listings appear in AI shopping results

To see how your brand shows up in AI search results, pretend you’re a shopper. Try searching for your products the way your customers would by asking specific product questions or making general requests. Use existing product page questions or reviews as fodder for your test queries.

To test, open ChatGPT, Claude, Perplexity, or Gemini and search for your products using natural, conversational questions. Perform searches in two ways: as someone who’s heard of your brand (mid- to bottom-funnel searches), and someone who hasn’t but is looking for products in your category (top-of-funnel search).

If you need some inspiration, here are some example test queries:

• “What are the best waterproof hiking boots for wide feet under $200?”
• “I need a moisturizer for sensitive skin that won’t clog pores.”
• “Show me sustainable yoga mats that are good for hot yoga.”
• “What laptop bag fits a 16-inch MacBook Pro and has good organization?”

When analyzing responses, take note of:

• Visibility: Do you appear in results at all? If not, which competitors do?
• Appearance rate: When you do appear, are you in the top 3–5 recommendations? Or do you need to prompt further to be included?
• Context: What types of queries surface your products? How is your brand reflected back at you?
• Accuracy: Is the AI providing correct information about your products, or is it making up details?
• Gaps: What categories, use cases, or customer needs are you not appearing for?

Review this type of analysis monthly, as AI models evolve and your optimization efforts may compound.

4. Post AI-optimized educational content

Your AI content strategy likely needs a revamp, if it exists at all. Beyond product page optimization, a great place to start is creating content that answers frequently asked questions—not the ones you think customers should be asking, but the real ones.

Here are some types of educational content that can boost AI visibility:

• Exhaustive and detailed FAQ sections: Add detailed FAQs to every product page that address real customer questions. For example, if you sell skincare products, your customers probably aren’t just asking, “How will retinol improve my skin?” They’re likely also wondering if using retinol while pregnant is safe, or if it will irritate their skin type. These types of detailed FAQs are information goldmines for LLMs.
• Comparison guides: Help AI tools understand where your product fits in the market. Think “Retinol vs. Vitamin C: Which Should You Use First?” or a guide that illustrates how people can use your product step-by-step. These types of guides provide the context LLMs need to accurately promote your products for the right situations.
• Long-form articles with question-based headers: Create longer product guides that LLMs can reference in snippets. Structure articles with headers that mirror real customer questions and provide clear, concise answers in the opening sentences. Each section should be structured as its own mini-article that can act as input for an LLM.

5. Invest in PR and content marketing partnerships

According to SparkToro, brand mentions on trustworthy, AI query-adjacent websites influence whether or not AI models reference your brand.

Focus on getting your products covered in reputable publications in your industry. The more high-quality mentions your brand gets online, the better.

Here are just a few examples of types of content that can contribute to your AI discoverability:

• Guest posts
• Product reviews
• Expert round-ups
• How-to articles that feature your brand

6. Encourage more reviews and user-generated content (UGC)

It’s no surprise that reviews are one of the top use cases for AI shopping. ChatGPT analyzes reviews and condenses customer opinions into summaries that highlight common likes and dislikes.

Traditional reviews focus on star ratings and simple comments. AI shopping assistants need detailed, specific information so that when a customer asks, “Will these leggings become see-through during squats?”, the AI searches through reviews for that exact concern.

As a comparison, here’s what we mean by detailed review:

• Generic review: “Great quality, 5 stars.”
• AI-optimized review: “I’m 5’8” and ordered a medium. The high waistband stays put during yoga without rolling down, and the fabric doesn’t go sheer during deep squats like my previous leggings did. Washed them 10x and they still look new.”

To get the kind of high-quality reviews that show up in AI conversations, set up an automated review request flow timed to product usage. That way, people have had time to use and (hopefully) enjoy your product before they leave a review. Instead of asking for general feedback, prompt customers with questions that generate AI-useful details, like:

• “How does the [size/fit/performance] compare to similar products you’ve used?”
• “What specific problem did this solve for you?”
• “Who would you recommend this to, and why?”
• “What surprised you (good or bad) about this product?”

Consider offering incentives for customers who provide detailed feedback with photos or videos. Visual user-generated content that shows off the product in the real world gives AI additional context for recommendations.

Once you’re raking in the reviews, use AI tools to:

• Respond to reviews. AI can help craft meaningful responses to your customers faster. This matters because 85% of consumers say that companies responding to negative reviews is an important factor in their general purchase process.
• Analyze review sentiment. Spot patterns in what customers are loving or complaining about across your product catalog. Use these insights to update inaccurate product descriptions or even improve the product itself.

Position your brand as a leader in AI shopping

The future of AI shopping is here: our AI consumer shopping survey reports that 65% of consumers expect AI shopping assistants to become a normal part of shopping with their favorite brands by 2026.

Klaviyo is the AI-first B2C CRM that unifies marketing, service, analytics, and data in one platform. K:AI Customer Agent provides 24/7 assistance, empowering customers to get fast answers to common questions, update their orders and subscriptions, get personalized product recommendations, and escalate issues to a human when necessary. With full access to customer and product data, both AI and human agents always have the context they need to help across chat, email, text messages, and WhatsApp.

Ready to add an AI shopping assistant to your storefront? Start giving your shoppers fast, personal, and on-brand experiences at scale with Klaviyo today.