TL;DR: 59% of Americans now use generative AI tools for online shopping, and 1 in 4 say ChatGPT outperforms Google for product research, per an Omnisend survey of 1,200 U.S. consumers (August 2025). ChatGPT processes 50 million shopping-related queries daily. The first touchpoint in the ecommerce path to purchase is shifting from search engines to conversational AI, and the brands that structure their catalog data for this shift will capture a growing share of high-intent buyer traffic.
The first moment of influence in an ecommerce purchase no longer belongs to a Google search bar or an Amazon category page. For a growing share of buyers, it belongs to a conversation.
ChatGPT now processes 50 million shopping-related queries daily, according to OpenAI’s November 2025 data. A separate Omnisend survey, fielded across 1,200 U.S. consumers in July 2025, found that 59% of Americans use generative AI tools for online shopping and 1 in 4 say ChatGPT outperforms Google for product research. This is not a trend forming on the horizon. It is a structural shift already underway in buyer behavior.
Two real-world integrations show how far this has already moved. Klarna’s conversational shopping tool enables buyers to describe a need across multiple brands, “I need a black leather sneaker under $200,” and receive personalized recommendations with real-time stock and shipping data. The scale: Klarna’s AI handled 2.3 million customer conversations in its first month of operation, equivalent to the output of 700 full-time agents, according to the company’s own press release. Instacart followed in November 2025 with enterprise AI solutions for Kroger and Sprouts, embedding a Cart Assistant that lets customers plan meals and fill grocery carts through natural language. Both are live at scale.
OpenAI extended this shift further with Buy It in ChatGPT, embedding product discovery and checkout directly inside the ChatGPT interface. Etsy sellers were connected at launch, with Shopify’s network of over one million merchants, including Glossier, SKIMS, and Vuori, integrated shortly after. The traditional funnel of homepage, category page, search bar, and cart is being bypassed for a growing share of shoppers. What once required a brand touchpoint now happens inside a chat window the buyer was already using for something else.
How ChatGPT Became a Product Discovery Engine
ChatGPT did not add a shopping feature as an afterthought. It built a layered product discovery infrastructure, and retailers who are not inside that infrastructure are already missing high-intent traffic.
In November 2025, OpenAI launched ChatGPT Shopping Research, a dedicated product discovery mode that processes structured catalog data, review signals, and real-time availability. Unlike keyword search, Shopping Research interprets intent: a query like “what is a good coffee grinder for a small kitchen under $150” retrieves products filtered by dimensions, price, and use case, not just brand name matches. Results appear as recommendation cards with product specs, pricing, and purchase links, with no visit to a retailer’s homepage required.
For brands indexed in this system, the audience is enormous. ChatGPT has 900 million weekly active users as of early 2026, per OpenAI’s reporting. Even a small fraction of those users running product queries represents a discovery channel at a scale that rivals established search platforms. For ecommerce brands accustomed to competing for Google Shopping placements, this is a second front.
The competitive shift is real. Amazon, Google Shopping, and Bing are no longer the only gatekeepers of product discovery. A third major platform now operates on completely different rules: fit over familiarity, structured data over ad spend, and conversation over keyword.
Why Amazon’s Market Position Is Under Real Pressure
Amazon holds 37.6% of the U.S. ecommerce market as of 2025, according to eMarketer estimates. That position is not disappearing. But the mechanism that sustains it, being the default starting point for product search, is under structural challenge for the first time in a decade.
Amazon’s answer to conversational AI is Rufus, an internal shopping assistant launched in 2024. Rufus is capable, and the data shows Rufus users complete purchases at higher rates than non-Rufus users. But Rufus operates only inside Amazon’s ecosystem. It does not integrate with ChatGPT or any GPT-based platform, does not surface products from competing retailers, and is not accessible to buyers who start their research in ChatGPT. Amazon is building impressive conversational AI, but it is doing so behind a walled garden in a market that is increasingly open.
Meanwhile, ChatGPT’s plugin and integration ecosystem connects to thousands of retailers and brands, surfacing recommendations based on fit rather than Amazon’s search algorithm or sponsored placement. When a buyer asks ChatGPT for a product recommendation, Amazon may or may not be part of the answer. Five years ago, Amazon was the near-automatic first destination for product research. That is no longer true for a growing share of buyers.
BrightEdge’s November 2025 data makes this concrete. The categories seeing the fastest AI-driven referral growth are exactly those with the most structured product data. The correlation is direct: grocers who publish complete nutrition data, furniture retailers with precise dimension specs, and electronics brands with detailed compatibility information are the ones benefiting most from AI Overview placement. Product data quality is now a growth lever, not just a catalog hygiene issue.
AI Referral Growth by Ecommerce Category (YoY, 2025) Grocery +900% Furniture +375% Electronics +257% Apparel +104% Source: BrightEdge AI Market Pulse, November 2025
What This Means for Ecommerce Retailers: The Competition Has Already Started
Retailers who treat ChatGPT as a marketing channel are solving the wrong problem. This is not a channel question. It is an infrastructure question. The path to purchase is being restructured, and brands that do not adapt their product data and catalog architecture will find themselves absent from a growing share of high-intent shopping sessions.
According to a Contentsquare survey of consumers published in December 2025, 30% of shoppers are now willing to let an AI agent complete a purchase on their behalf. Gartner projects that AI agents will intermediate $15 trillion in B2B purchases by 2028. The direction of travel is not ambiguous: buyer intent is being captured earlier in the funnel, in a conversational layer that sits between the buyer and the retailer.
This shift creates two concrete problems for retailers who are not prepared.
Fewer brand touchpoints in the early journey. When a buyer starts with ChatGPT, they often do not see the retailer’s site at all during the research phase. The AI curates products using plugin data, external reviews, and structured spec information. Brand design, site UX, and merchandising are invisible unless the underlying catalog data is part of that curation layer.
No traditional opportunity to intercept. A buyer who starts a query in ChatGPT and receives a tailored recommendation is less likely to browse comparison sites, marketplaces, or run a follow-up Google search. If a brand is not part of the AI-generated result, it is not in the running. There is no second-chance PPC impression, no retargeting pixel, no organic ranking to fall back on for that session.
To stay relevant, retailers must reposition their catalog as a data source to be consulted, not just a storefront to be visited. That requires three specific infrastructure changes: product data must be machine-readable and API-accessible; product detail pages must go beyond marketing copy and include structured attributes describing use case, compatibility, and comparison value; and pricing and inventory data must be current and exposed through channels AI systems can query.
For retailers who want to understand how this connects to their existing SEO work, our guide on SEO for AI-based search engines covers the full technical approach, from schema markup to passage-level content architecture. The strategies overlap more than most teams expect.
Retailers who invest in this shift early will benefit from what is essentially organic AI visibility: earning recommendations based on data quality and relevance, not on paid placement or brand awareness spend. Retailers who do not will find themselves invisible to the AI systems their customers now trust most to make decisions.
This is not just another channel. Like the move from desktop to mobile, or from direct traffic to ecommerce marketplace selling, the emergence of AI as a decision layer changes how and where buyer intent gets captured. The rules of the game are shifting, and the window to adapt with a structural advantage is open right now.
How to Prepare: Structuring Your Product Catalog for AI Discovery
The operational changes required to compete in AI-driven product discovery are not simple, but they are concrete. They do not require rebuilding a storefront. They require rethinking what the ecommerce infrastructure is exposing, and to whom.
Turn Product Content Into Structured, Machine-Readable Data
AI does not interpret your product the way a person does. It reads structured fields. If a product detail page contains one large text block with marketing copy, the AI has no way to extract dimensions, compatibility, or use case. The result is that the product does not surface in queries where those attributes are the deciding factor.
Start by decomposing product detail pages into labeled attribute fields: materials, dimensions, compatibility, certifications, intended use cases, and known problems the product solves. Do not organize attributes according to internal taxonomy. Tag and map them to the language real buyers use when searching, comparing, and asking questions. A buyer asking “is this waterproof?” should find the answer in a structured field, not buried in paragraph four of a product description.
Open Your Catalog via API for AI Systems to Query
A product that cannot be queried programmatically cannot be recommended at scale. Whether through GPT plugins, custom integrations, or middleware, making catalog data accessible via API is a prerequisite for AI-driven discovery.
Shopify merchants already have a head start: the platform’s built-in APIs allow direct connection to ChatGPT’s plugin infrastructure and to custom GPTs built through OpenAI’s GPT Builder. For headless setups, BigCommerce’s API layer and similar headless commerce architectures are well-suited to this kind of dynamic exposure. Legacy systems can use middleware to standardize and expose product data in a consistent, real-time format that AI tools can query and reason over.
The mental model shift: think of your product catalog not as inventory, but as a structured knowledge base. AI tools need to query it, reason over it, and recommend from it. If the knowledge base is locked behind a static HTML page, the AI cannot reach it.
Write Product Copy That Answers Questions, Not Just Describes Features
When a buyer asks ChatGPT “what is a good all-terrain stroller for city sidewalks and rough trails?” they are not looking for a SKU. They are looking for the reasoning behind a recommendation. Product copy that describes features without addressing the use case behind them gives AI nothing to work with.
Effective AI-ready product copy includes three types of language. Use-case language anchors the product to specific situations: “built for hiking,” “safe for indoor hardwood floors,” “rated for temperatures below 10 degrees.” Problem-solving language connects the product to the pain a buyer is trying to eliminate: “reduces setup time from 8 minutes to under 2,” “prevents backflow in high-pressure lines.” Comparative language gives AI the context to justify recommending this product over alternatives: “30% lighter than comparable models in the same price range,” “the only model in this category rated for both fresh and saltwater.” The more specific and verifiable these claims, the more useful they are to an AI system constructing a recommendation.
Build a Branded GPT Plugin or AI Shopping Experience
Some brands are not waiting to be discovered by AI systems. They are building their own. A branded GPT plugin creates an intelligent, conversational layer on top of a catalog and store logic, one that pulls live inventory, filters products by user needs, and can launch a cart or checkout session mid-conversation.
A custom GPT trained on a brand’s full catalog, buyer personas, and brand voice gives the brand direct control over how product discovery happens. Shopify and WooCommerce merchants can deploy these tools through OpenAI’s GPT Builder and connect them to ChatGPT’s Plugin Store. This is not theoretical: brands doing this now are designing how discovery works for their customers rather than waiting to be surfaced by a third-party algorithm.
For a technical walkthrough of exactly how to connect products to ChatGPT, see our guide on how to get products listed on ChatGPT and OpenAI. For teams ready to build a custom integration, our ecommerce development services cover API architecture, catalog structuring, and plugin deployment.
Make AI Discoverability a Cross-Functional Priority
Catalog structuring and API access are engineering problems. But they only get solved when product, marketing, merchandising, and SEO teams treat AI discoverability as a shared KPI.
Product marketers need to write content that is interpretable by both people and machines. SEO teams should treat AI models as the new top of funnel and optimize product pages accordingly. Support teams should contribute FAQ content, objections, and troubleshooting language to the knowledge base, especially if that content can be parsed by AI systems. Merchandising teams own the attribute data that determines whether a product surfaces in filtered queries.
The goal is not to replace the existing commerce stack. It is to make sure the stack is visible, legible, and useful to the AI systems now sitting between the brand and its customers.
Five Competitive Advantages for Ecommerce Brands That Move Now
Generative AI is not just another traffic source. It is changing the structure of the purchase decision itself. Brands that adapt their catalog and data infrastructure now will benefit from compounding advantages as AI-mediated commerce grows.
1. Organic Visibility Based on Fit, Not Ad Spend
When ChatGPT recommends a product, it does so because the product data was relevant, complete, and clear. The brand did not buy that placement. This levels the competitive field for mid-sized brands that cannot outbid large competitors in Google Shopping or Amazon ad auctions. An AI-driven world rewards the most useful product, not the loudest advertiser. That is a structural advantage for any brand willing to invest in catalog quality.
Capgemini research across 12,000 consumers in 12 countries found that 75% of consumers are now open to AI-generated product recommendations, up from 63% in 2023. The audience for AI-driven discovery is growing, and the brands in position to serve it are those with complete, well-structured catalog data today.
2. Higher-Intent Traffic with Shorter Sales Cycles
Buyers who arrive at a product through a ChatGPT-style interaction are pre-qualified in a way that organic search traffic is not. They asked a specific question. They received a reasoned recommendation. By the time they reach a product page, they already understand what the product does, why it fits their need, and what it costs. The education cycle that typically happens across multiple site visits happens inside the AI conversation instead.
According to internal conversion data published by Rep AI, shoppers who engage with AI-powered chat convert at 12.3% compared to 3.1% for those who do not. While this figure comes from a vendor dataset and should be treated as directional rather than universal, it aligns with the logical outcome: a buyer who has been guided to a specific product by an AI system arrives at a product page ready to buy, not ready to browse. Reduced bounce rates and higher average order values follow from that difference in buyer state.
3. Protection Against Race-to-the-Bottom Pricing
On traditional marketplaces, price is the primary visibility lever. Lower the price, get the Buy Box, win the sale. Conversational AI breaks that dynamic. If a product solves a specific use case better than cheaper alternatives, and the product data makes that case clearly, ChatGPT will surface it regardless of whether it is the lowest-priced option. The recommendation is based on fit, not on price ranking.
This gives brands room to differentiate on value. A brand that clearly articulates why its product is the right choice for a specific buyer profile, with structured data backing that claim, can compete on relevance rather than discounting. For categories where margin pressure has been severe, that is a meaningful strategic shift.
4. Durable Brand Positioning as the Authoritative Answer for a Category
If a brand controls a well-structured product feed, a branded GPT plugin, and well-indexed catalog data, its products become the default answer for specific types of buyer problems. That is not just a conversion effect. It is a positioning effect. Over time, buyers begin to associate the brand with expertise and relevance for a particular problem type, not just with a transaction they completed once.
This is especially valuable for technical or niche product categories where brand visibility is difficult to build through traditional advertising. A small brand with highly detailed, well-structured product data can establish category authority in AI systems faster than a large brand with generic catalog descriptions, because the AI rewards completeness, not reputation.
5. A Direct Feedback Loop for Product and Content Strategy
Integrating with AI platforms creates a new data stream. Brands can analyze the prompts that lead to their products: what language buyers use to describe needs, which product attributes are most frequently referenced, and where the catalog has gaps. When layered with review sentiment and support ticket analysis, this input feeds directly into product development, PDP copy, and category page strategy.
McKinsey estimates that by 2028, $750 billion in U.S. revenue will flow through AI-powered search. Brands that are already in the system when that volume materializes will have both the positioning and the data advantage. Brands that enter late will be building visibility against competitors who have been compounding for years.
“In a world where the question comes first, the job is not simply to be found. It is to be chosen.”
Consumers Replacing Traditional Search with Gen AI for Product Recs 0% 25% 50% 75% 25% 58% 2023 2025 Source: Capgemini Research Institute, survey of 12,000 consumers across 12 countries, January 2025
The AI-Ready Commerce Toolkit
The shift to AI-mediated commerce is a structural change, not a feature release. Understanding exactly how it differs from the commerce model of the last decade is the first step to adapting for it.
| Dimension | Traditional Commerce | AI-Mediated Commerce |
|---|---|---|
| Where discovery starts | Search engine, marketplace, homepage | Conversational AI prompt (ChatGPT, Perplexity, Google AI Overviews) |
| Visibility driver | Keyword ranking, ad spend, review volume | Structured data quality, schema completeness, use-case alignment |
| Brand control at discovery | High (site design, merchandising, homepage) | Low unless brand owns plugin or GPT integration |
| Buyer intent on arrival | Mixed (browsing to buying spectrum) | High (buyer has been pre-qualified by the AI conversation) |
| Price as ranking signal | Strong (Amazon Buy Box, Google Shopping sort) | Contextual (AI recommends on fit, not cheapest) |
| Optimization target | Page one ranking, CTR, quality score | AI citation, schema completeness, passage extractability |
AI Discovery Optimization Priorities
Use this as a sequenced roadmap. The critical steps unlock the most AI visibility in the shortest time. Next-level and strategic investments compound from there.
Critical First Steps:
- Add Product, Offer, and Review schema markup to all product detail pages
- Break product descriptions into structured attribute fields (not one text block)
- Ensure pricing, inventory, and specs are current across all SKUs
- Connect product feed to Google Merchant Center and verify completeness
Next-Level Enhancements:
- Rewrite product copy using problem-solution and use-case language
- Map product categories to real-world buyer questions and intents
- Add FAQ fields to PDPs to trigger conversational responses in AI systems
- Expose catalog via API to enable plugin and custom GPT connections
Strategic Investments:
- Build a branded GPT plugin for guided shopping on your catalog
- Sync product data updates to plugin endpoints in real time
- Track AI referral traffic and prompt behavior to inform product and copy strategy
Understanding the New Commerce Funnel
In the traditional funnel, awareness flows from advertising and search to the homepage, then down through category and product pages to checkout. In the AI-mediated funnel, a buyer enters a conversational platform, describes a need, and receives a recommendation. The retailer’s site is only part of the journey, and often not the first part. Brands need to be legible to the AI system at the top of this new funnel, before any visit to the storefront.
Google is building toward the same endpoint. The Google Universal Commerce Protocol embeds checkout directly inside Search and AI Overviews, compressing the funnel even further. The direction across all major AI platforms is the same: reduce the steps between intent and purchase, and route buyers to products without requiring a detour through a brand’s homepage.
Frequently Asked Questions About AI Ecommerce and Product Discovery
Is ChatGPT replacing Amazon as the default destination for product search?
Not replacing, but increasingly competing for the first touchpoint in the purchase journey. Amazon holds 37.6% of the U.S. ecommerce market and processes enormous transaction volume. ChatGPT, however, now handles 50 million shopping-related queries daily and has 900 million weekly active users, giving it reach that no single retailer can match. The shift is not about Amazon disappearing. It is about buyers starting their research in a new place, one where Amazon is not the default answer.
What product data does ChatGPT use when recommending products?
ChatGPT Shopping Research draws from structured product catalog data, schema markup (Product, Offer, Review), publicly accessible feeds, and third-party plugin integrations. It prioritizes products with complete, attribute-rich data: dimensions, materials, use cases, compatibility, price, and availability. Products that rely on marketing copy alone, without structured fields and schema, are significantly less likely to surface in AI-generated recommendations. Clean, well-tagged product data is the primary factor.
How do I get my Shopify or BigCommerce products surfaced by ChatGPT?
The most direct routes are: (1) ensure your product catalog is API-accessible and uses structured data including Product and Offer schema; (2) connect your store through OpenAI’s plugin ecosystem or a GPT-powered integration; (3) enrich product detail pages with use-case language, problem-solution framing, and comparison attributes rather than generic marketing descriptions. Shopify merchants can access OpenAI’s plugin infrastructure through existing API connections. For a platform-specific implementation guide, see our step-by-step walkthrough on getting products listed in ChatGPT.
Does AI-driven product discovery only matter for large ecommerce brands?
No. AI-driven discovery levels the field in ways that traditional search does not. On Amazon or Google Shopping, ad spend and review volume give large brands a structural advantage. ChatGPT recommends based on fit: if a mid-sized brand’s product data clearly describes who the product is for and what problem it solves, it can surface ahead of a larger competitor whose data is generic. Forrester’s 2026 commerce predictions identify early movers in agentic commerce, regardless of size, as the brands most likely to build durable positioning in AI-mediated discovery.
How does conversational AI change my existing ecommerce SEO strategy?
Traditional SEO optimizes for ranking on a results page. AI SEO optimizes for being selected as a cited source in a synthesized answer. The two share common foundations: authoritative content, technical hygiene, structured data, and strong E-E-A-T signals. But the execution differs. AI systems extract from passage-level blocks, favor direct answers over keyword density, and prioritize schema-marked content over unmarked prose. Ecommerce sites should treat product detail pages and category content as answer-first documents, not just ranking targets. Our guide on SEO for AI-based search engines covers the full implementation approach.
The Future of Ecommerce Belongs to Brands That Are AI-Legible
Ecommerce is not in decline. But the interface buyers have used for two decades, search bars, filters, category menus, and homepage hero images, is being challenged in a structural way. What once started with a Google search or a visit to Amazon is now just as likely to begin with a prompt: “What is the best tool for cutting copper pipe in tight spaces?” or “I need a gift for a 9-year-old who loves building things.”
That is not a search query. It is a conversation. And in 2026, that conversation is happening more and more through platforms like ChatGPT, at a scale that represents a real share of purchase intent. Gartner’s 2026 predictions include AI agents intermediating $15 trillion in B2B purchases by 2028, and Forrester projects that 5 major brands will unify agentic commerce experiences within the year. The infrastructure supporting these predictions is being built now.
This shift does not erase the existing commerce stack. A storefront, product pages, fulfillment infrastructure: all still matter. But they are no longer where the purchase journey begins for a growing share of buyers. The first moment of influence is now happening in a conversational layer. When the interface changes, the advantage shifts. Not to the biggest brand. Not to the cheapest SKU. To the brand that is legible to the AI: structured, contextual, and clearly relevant to the problem the buyer is trying to solve.
Just as mobile did not eliminate desktop, conversational AI will not eliminate websites. But it is redefining where customers make decisions. The next wave of ecommerce growth is not about launching more storefronts. It is about becoming part of the answer, earning a place in the AI-powered path to purchase before the buyer ever types a URL.
The brands that adapt fastest will own the conversation and the customer. The rest will be competing for the traffic that happens to make it through to a traditional search results page.
Contact Optimum7 if you need help with AI integrations, product catalog structuring, plugin deployment, or preparing your ecommerce infrastructure for AI-driven commerce.
About the author: Duran Inci is the CEO and Co-Founder of Optimum7, an ecommerce development and digital marketing agency. He helps mid-market and enterprise brands scale revenue through conversion optimization, SEO, and custom ecommerce solutions.
