Artificial intelligence is rapidly becoming a new gateway to online shopping, and retailers are facing an uncomfortable trade-off. They need to appear in recommendations generated by AI systems because those platforms are increasingly sending highly engaged shoppers to retail websites. At the same time, retailers do not want artificial intelligence companies to become the owners of the customer relationship, because the data generated during a purchase remains one of the most valuable assets in digital commerce.
This tension is reshaping online retail. Companies such as Walmart, Ulta Beauty, Wayfair and Etsy are adapting product information so artificial intelligence systems can understand and recommend their merchandise. Yet many retailers still want shoppers to leave the AI platform and complete their purchases on the retailer's own website, where the company can collect information about browsing behavior, purchases, preferences, loyalty and spending patterns.
The attraction is understandable. Recent Adobe data showed that shoppers arriving at retail websites through generative artificial intelligence services generated substantially more revenue per visit than visitors arriving through traditional channels. Other industry data also indicates that AI-referred shoppers can show higher purchase intent because the technology often compresses product discovery and comparison into a single conversation.
The emerging battle is therefore not over whether retailers should use AI. They increasingly have little choice. The more important question is whether retailers can benefit from AI-driven demand without surrendering control of the customer data and commercial relationship that make online retail so valuable.
AI Is Becoming a New Shopping Entrance
Traditional online shopping begins with a consumer visiting a search engine, retailer or marketplace and entering a product query. Generative AI changes that sequence by allowing consumers to describe a need in ordinary language and receive recommendations based on several requirements at once.
A shopper might ask for a particular type of product, specify a budget, identify preferred features and request comparisons. The AI system can then narrow the choices before the consumer visits an individual retailer. This changes the role of product discovery because the shopper may no longer examine dozens of search results.
Google is building directly around this behavior. Its AI shopping tools allow consumers to search for products through conversational interfaces, while its Universal Cart is designed to let shoppers add products from different retailers while moving across Google's services. The company is also developing infrastructure intended to allow AI agents and merchants to communicate more directly during the shopping process.
OpenAI has pursued a similar direction through shopping features in ChatGPT. Product recommendations can direct shoppers toward merchant websites, while its commerce infrastructure is designed to support transactions involving merchants and AI agents. For retailers, this creates a new source of demand but also a new intermediary between themselves and consumers.
High-Intent Shoppers Are Worth Chasing
Retailers have a strong financial reason to participate. AI-generated recommendations can arrive later in the shopping journey than conventional search traffic because consumers often use conversational systems after already defining what they want.
That can make AI referrals commercially attractive. Industry data indicates that shoppers arriving from AI services can spend more time evaluating products and generate higher revenue per visit than visitors from conventional channels. Shopify has also reported stronger conversion characteristics among AI-referred shoppers, although traditional search remains a much larger source of traffic for many merchants.
The significance is that AI may be sending fewer visitors but potentially more valuable ones. For retailers, that changes the economics of traffic acquisition. A shopper who asks an AI system to identify the best product within a specific price range may already have eliminated many alternatives before clicking on a retailer's website. The retailer therefore receives a customer who has completed part of the consideration process elsewhere.
That is attractive from a conversion perspective, but it also creates dependence. If the AI platform determines which products are presented and which retailers are recommended, the retailer has less influence over the initial customer relationship.
Product Data Is Becoming Strategic Infrastructure
The rise of AI shopping is also changing what retailers need to do with their websites. Traditional search optimization focused heavily on keywords, links and page rankings. AI systems require much richer information because they need to understand products in context.
Retailers increasingly need accurate descriptions, specifications, prices, availability, sizes, colors, compatibility information and product attributes. Poor or incomplete data can make it harder for an AI system to recommend a product accurately. This creates a new form of competition. Retailers are no longer optimizing only for consumers and search engines. They are also optimizing their product information for machines that interpret and compare thousands of products before presenting a small number of recommendations.
That makes product data an increasingly valuable commercial asset. The better a retailer structures and maintains that information, the greater its ability to appear accurately in AI-generated recommendations. But visibility is only the first objective. Retailers ultimately want to turn that visibility into a direct relationship.
Customer Data Is the Real Prize
The strongest reason retailers prefer their own checkout is not simply avoiding transaction fees. It is the information generated after a customer arrives. When a shopper purchases directly from a retailer, the company can learn what was bought, how frequently the customer returns, which products were considered, what promotions influenced the purchase and how the customer responds to recommendations. That information can improve advertising, inventory decisions, product development and personalized offers.
A general-purpose AI platform may know what the consumer asked for, but the retailer can know what the consumer actually purchased and how that purchase fits into a longer relationship. This distinction is particularly important for businesses built around loyalty programs. A retailer such as Ulta Beauty can connect purchases with rewards accounts, previous preferences and future marketing. If the transaction occurs entirely inside an external AI platform, the retailer could lose some of the information that makes its loyalty ecosystem valuable.
That is why retailers are trying to use AI platforms as customer-acquisition channels rather than allowing them to become the complete retail environment.
AI Companies Want a Larger Role
The conflict is becoming more complicated because AI companies have their own commercial incentives. If an AI system can recommend products, compare prices, assemble a basket and complete payment, it could potentially control a much larger portion of the shopping journey.
Google's development of agentic shopping infrastructure shows the direction of travel. Its Universal Cart is designed to work across merchants and services, while AI systems can assist with product comparisons, price monitoring and purchasing decisions.
That could eventually reduce the importance of individual retailer websites as places where shoppers begin their journeys. A consumer might still buy from the retailer, but the retailer could become the fulfillment layer while the AI company controls discovery and decision-making.
That would represent a major shift in digital commerce. Search engines already influence which websites receive traffic, but AI agents could go further by deciding which products deserve consideration in the first place.
Retailers Still Have Important Advantages
Retailers are not powerless in this transition. They possess several assets that general-purpose AI companies do not control completely, including product inventory, transaction histories, loyalty programs, supplier relationships, fulfillment networks and direct customer-service operations.
Those advantages give retailers reasons to keep their own websites central to the transaction even while embracing AI for discovery. The current behavior of consumers also provides some protection. Evidence from retailers such as Etsy indicates that shoppers who discover products through AI services can still return to the retailer's website to complete purchases. This means AI may initially function more like a new referral channel than a complete replacement for traditional retail websites.
That distinction could give retailers time to adapt. They can improve their product data, build better AI-compatible shopping experiences and develop partnerships with AI platforms while preserving direct checkout and loyalty relationships.
The Next Battle Will Be Over Control
The retail industry's AI transition is therefore creating a new competitive struggle over the customer journey. Retailers want the traffic and sales that AI can generate, while AI companies have an incentive to become increasingly important in discovery, comparison and potentially transactions.
The balance will depend partly on consumer behavior. If shoppers continue using AI primarily to research products and then visit retailer websites, retailers may gain a powerful new source of high-intent traffic without losing much control. If consumers increasingly allow AI agents to complete purchases on their behalf, the economic balance could shift toward the platforms controlling those agents.
For now, retailers are trying to occupy both worlds. They are making themselves visible to AI systems because ignoring the new source of traffic could mean losing customers, while simultaneously protecting their own websites because direct relationships remain central to loyalty and long-term profitability.
The emerging model is therefore less about retailers resisting artificial intelligence than about controlling where its value is captured. AI can bring the shopper to the door, but retailers still want to own what happens after the shopper arrives. The companies that succeed may be those capable of using AI as a powerful acquisition and recommendation engine without allowing it to become the owner of their customers.
(Source:www.tradingview.com)
This tension is reshaping online retail. Companies such as Walmart, Ulta Beauty, Wayfair and Etsy are adapting product information so artificial intelligence systems can understand and recommend their merchandise. Yet many retailers still want shoppers to leave the AI platform and complete their purchases on the retailer's own website, where the company can collect information about browsing behavior, purchases, preferences, loyalty and spending patterns.
The attraction is understandable. Recent Adobe data showed that shoppers arriving at retail websites through generative artificial intelligence services generated substantially more revenue per visit than visitors arriving through traditional channels. Other industry data also indicates that AI-referred shoppers can show higher purchase intent because the technology often compresses product discovery and comparison into a single conversation.
The emerging battle is therefore not over whether retailers should use AI. They increasingly have little choice. The more important question is whether retailers can benefit from AI-driven demand without surrendering control of the customer data and commercial relationship that make online retail so valuable.
AI Is Becoming a New Shopping Entrance
Traditional online shopping begins with a consumer visiting a search engine, retailer or marketplace and entering a product query. Generative AI changes that sequence by allowing consumers to describe a need in ordinary language and receive recommendations based on several requirements at once.
A shopper might ask for a particular type of product, specify a budget, identify preferred features and request comparisons. The AI system can then narrow the choices before the consumer visits an individual retailer. This changes the role of product discovery because the shopper may no longer examine dozens of search results.
Google is building directly around this behavior. Its AI shopping tools allow consumers to search for products through conversational interfaces, while its Universal Cart is designed to let shoppers add products from different retailers while moving across Google's services. The company is also developing infrastructure intended to allow AI agents and merchants to communicate more directly during the shopping process.
OpenAI has pursued a similar direction through shopping features in ChatGPT. Product recommendations can direct shoppers toward merchant websites, while its commerce infrastructure is designed to support transactions involving merchants and AI agents. For retailers, this creates a new source of demand but also a new intermediary between themselves and consumers.
High-Intent Shoppers Are Worth Chasing
Retailers have a strong financial reason to participate. AI-generated recommendations can arrive later in the shopping journey than conventional search traffic because consumers often use conversational systems after already defining what they want.
That can make AI referrals commercially attractive. Industry data indicates that shoppers arriving from AI services can spend more time evaluating products and generate higher revenue per visit than visitors from conventional channels. Shopify has also reported stronger conversion characteristics among AI-referred shoppers, although traditional search remains a much larger source of traffic for many merchants.
The significance is that AI may be sending fewer visitors but potentially more valuable ones. For retailers, that changes the economics of traffic acquisition. A shopper who asks an AI system to identify the best product within a specific price range may already have eliminated many alternatives before clicking on a retailer's website. The retailer therefore receives a customer who has completed part of the consideration process elsewhere.
That is attractive from a conversion perspective, but it also creates dependence. If the AI platform determines which products are presented and which retailers are recommended, the retailer has less influence over the initial customer relationship.
Product Data Is Becoming Strategic Infrastructure
The rise of AI shopping is also changing what retailers need to do with their websites. Traditional search optimization focused heavily on keywords, links and page rankings. AI systems require much richer information because they need to understand products in context.
Retailers increasingly need accurate descriptions, specifications, prices, availability, sizes, colors, compatibility information and product attributes. Poor or incomplete data can make it harder for an AI system to recommend a product accurately. This creates a new form of competition. Retailers are no longer optimizing only for consumers and search engines. They are also optimizing their product information for machines that interpret and compare thousands of products before presenting a small number of recommendations.
That makes product data an increasingly valuable commercial asset. The better a retailer structures and maintains that information, the greater its ability to appear accurately in AI-generated recommendations. But visibility is only the first objective. Retailers ultimately want to turn that visibility into a direct relationship.
Customer Data Is the Real Prize
The strongest reason retailers prefer their own checkout is not simply avoiding transaction fees. It is the information generated after a customer arrives. When a shopper purchases directly from a retailer, the company can learn what was bought, how frequently the customer returns, which products were considered, what promotions influenced the purchase and how the customer responds to recommendations. That information can improve advertising, inventory decisions, product development and personalized offers.
A general-purpose AI platform may know what the consumer asked for, but the retailer can know what the consumer actually purchased and how that purchase fits into a longer relationship. This distinction is particularly important for businesses built around loyalty programs. A retailer such as Ulta Beauty can connect purchases with rewards accounts, previous preferences and future marketing. If the transaction occurs entirely inside an external AI platform, the retailer could lose some of the information that makes its loyalty ecosystem valuable.
That is why retailers are trying to use AI platforms as customer-acquisition channels rather than allowing them to become the complete retail environment.
AI Companies Want a Larger Role
The conflict is becoming more complicated because AI companies have their own commercial incentives. If an AI system can recommend products, compare prices, assemble a basket and complete payment, it could potentially control a much larger portion of the shopping journey.
Google's development of agentic shopping infrastructure shows the direction of travel. Its Universal Cart is designed to work across merchants and services, while AI systems can assist with product comparisons, price monitoring and purchasing decisions.
That could eventually reduce the importance of individual retailer websites as places where shoppers begin their journeys. A consumer might still buy from the retailer, but the retailer could become the fulfillment layer while the AI company controls discovery and decision-making.
That would represent a major shift in digital commerce. Search engines already influence which websites receive traffic, but AI agents could go further by deciding which products deserve consideration in the first place.
Retailers Still Have Important Advantages
Retailers are not powerless in this transition. They possess several assets that general-purpose AI companies do not control completely, including product inventory, transaction histories, loyalty programs, supplier relationships, fulfillment networks and direct customer-service operations.
Those advantages give retailers reasons to keep their own websites central to the transaction even while embracing AI for discovery. The current behavior of consumers also provides some protection. Evidence from retailers such as Etsy indicates that shoppers who discover products through AI services can still return to the retailer's website to complete purchases. This means AI may initially function more like a new referral channel than a complete replacement for traditional retail websites.
That distinction could give retailers time to adapt. They can improve their product data, build better AI-compatible shopping experiences and develop partnerships with AI platforms while preserving direct checkout and loyalty relationships.
The Next Battle Will Be Over Control
The retail industry's AI transition is therefore creating a new competitive struggle over the customer journey. Retailers want the traffic and sales that AI can generate, while AI companies have an incentive to become increasingly important in discovery, comparison and potentially transactions.
The balance will depend partly on consumer behavior. If shoppers continue using AI primarily to research products and then visit retailer websites, retailers may gain a powerful new source of high-intent traffic without losing much control. If consumers increasingly allow AI agents to complete purchases on their behalf, the economic balance could shift toward the platforms controlling those agents.
For now, retailers are trying to occupy both worlds. They are making themselves visible to AI systems because ignoring the new source of traffic could mean losing customers, while simultaneously protecting their own websites because direct relationships remain central to loyalty and long-term profitability.
The emerging model is therefore less about retailers resisting artificial intelligence than about controlling where its value is captured. AI can bring the shopper to the door, but retailers still want to own what happens after the shopper arrives. The companies that succeed may be those capable of using AI as a powerful acquisition and recommendation engine without allowing it to become the owner of their customers.
(Source:www.tradingview.com)