Agentic AI in Retail: How Autonomous Shopping Is Redefining the Customer Journey

Throughout the purchase process, AI agents are starting to take on the heavy lifting for consumers: recommending options, hunting for deals, checking stock for hot items, and finding smart substitutions.

Agentic AI—a set of autonomous systems that acts on a user’s behalf—has the potential to reshape every step of the shopper journey from research to purchase. While timing and depth of adoption are uncertain, autonomous agents are already redefining the rules of search, advertising, personalization, fulfillment, payments, and post-purchase support. The choices retailers make now will determine whether they shape the future or are sidelined by it.

From data aggregators to copilots to closers

Generative AI has already disrupted how people discover and evaluate products. 

What’s more, Bain’s Consumer Lab Generative AI Survey found that today, around 30% of US consumers say they use generative AI for product comparison and recommendations.

Agentic AI goes further than suggestions, combining memory, reasoning, and tool use to act semi- or fully autonomously across the shopping journey. Near term, agents could analyze shopper behavior to recommend personalized options, bundle offers in real time, and streamline checkout. Eventually, agents may execute full transactions without human input.

Consumers aren’t quite there yet: Around half say they aren’t comfortable letting AI handle an end-to-end transaction without their involvement. That’s likely, in part, because they haven’t tried it yet. If trust catches up, the trajectory is clear.

To understand the stakes, it’s pragmatic to consider the bookends. At a minimum, the top of the marketing funnel has already been disrupted. As AI engines become a default shopping starting point, and discovery shifts to agent-curated answers, traditional paid search will be harder to attribute and optimize. Retailers will need new ways to get on agents’ lists and measure results. On the other end of the disruption spectrum, agents could entirely disintermediate multibrand retailers and turn direct-to-consumer brands into indirect ones, becoming next-gen marketplaces that control transactions, compress margins, and capture data. Shopping agents could relegate retailers to drop shippers or commoditized fulfillment mechanisms.

We’ll likely land somewhere in the middle, but either end justifies action. Leading retailers will prepare for the full range of outcomes.

How agents are already acting

Three types of AI agents are emerging, each reshaping retail in different ways.

• Third-party “objective” agents: Platforms such as Perplexity, ChatGPT, and Gemini crawl vendor sites, aggregate listings, compare prices, read reviews, and recommend products. More consumers are starting with these agents for easy, comprehensive research. In fact, according to according to estimates from Similarweb, a digital marketing intelligence company, shopping referrals from ChatGPT have more than doubled in the US, France, UK, and Germany over the past year.

AI now accounts for up to a quarter of referral traffic for some retailers, but it is still less than 1% of total traffic. Even so, as ChatGPT and Perplexity move away from instant checkout and consumers increasingly rely on LLMs for search and discovery, winning the referral is becoming more important than ever.  

• On-site retailer agents: Some retailers are developing proprietary AI assistants to enhance discovery and conversion within their ecosystems. For example, Amazon’s Rufus helps customers research and compare products, and it answers product-specific questions. Amazon estimates that Rufus helped generate nearly $12 billion in incremental annualized sales last year. In its first quarter earnings, the retailer shared that monthly active Rufus users were up 115% and were 60% more likely to complete a purchase. Magalu, the Brazilian electronics giant, launched Lu within WhatsApp to recommend products, process payment, and optimize for delivery and service evaluation. These agents have the added advantage of collecting proprietary retailer data, such as return rates and reasons, expert reviews, and longer-term purchase history.
• Off-site retailer agents. To remain a starting point for search and to bolster loyalty, some retailers are also building agents that help customers shop beyond their own inventory. Amazon’s “Buy for Me” agentic feature can purchase on other brands’ sites, even as the retailer restricts other agents from scraping its own listings.

Of these models, third-party agents pose the greatest immediate threat to the traditional retailer-shopper and retailer-advertiser relationships. The dilemma is real: On one hand, agents can expand reach and reduce customer acquisition costs. If competitors open up, retailers that hold back could lose visibility and relevance. On the other hand, retailers that engage without a differentiated value proposition and the ability to compel direct consumer traffic risk disintermediation and commoditization.

A more consequential shift could be the emergence of agent-assembled baskets. Multi-item, multivendor carts would make agents far more valuable to shoppers with complex missions, such as planning a full family trip across lodging, transit, dining, and activities. But they also raise the stakes for retailers. If agents can fragment the basket across vendors, the concept of a retailer-owned basket could start to disappear, especially in categories such as grocery. And greater price transparency could intensify price competition and put further pressure on margins.

Luckily, shoppers say that they trust retailers’ on-site agents three times more than third-party agents—for now. Retailers have a short opportunity window to develop on-site capabilities that appeal to their customers’ shopping needs before that trust gap closes.

The bottom line is that retailers need to participate on every front, and quickly. Smart retailers will decide now where to build, where to participate, and where to protect.

What shoppers want—and how retailers can respond

The answer, as always, starts with the consumer. Where they turn—to retailer-run or third-party agents—will depend on their shopping mission and where AI can be most supportive.

The implications will differ for each retailer depending on their category mix, shoppers’ missions, and unique value proposition. Weighing these factors, leading retailers will participate in two ways.

• Build owned agentic capabilities. Winning retailers will focus on where they have unique or proprietary data, expertise, or services that will attract customers who are looking for specific guidance. For categories where guidance or post-sale services matter, retail-run agents can beat generalist AI on depth. For example, Home Depot tapped into its project expertise, product knowledge, and proprietary data to create Magic Apron, its AI companion that provides specialized support to potential customers, drawing them onto the site.
• Participate with big agents strategically. In other areas, it will make sense to collaborate, not just compete. As of March, Walmart, Target, and Etsy are among the many retailers that have partnered with OpenAI for ChatGPT commerce integrations. Others, such as John Lewis, have announced intentions for products to be visible on AI apps. Meanwhile, Google recently launched its Universal Commerce Protocol—an open-source standard for agentic commerce codeveloped with Shopify, Etsy, Wayfair, Target, and Walmart—to power its shopping experiences in Google Search’s AI Mode and Gemini. These early alliances and partnerships can help retailers influence the emerging rules of engagement. For instance, retailers can negotiate retaining ownership of customer data and purchase signals or using their own checkout gateway

  Successful retailers will also structure their product catalogs and data so that agents surface their content accurately and route traffic back. Recent research from Columbia and Yale indicates that agents heavily weigh review counts and average ratings, creating a new optimization challenge for retailers and brands. In addition, a retailer can build a “headless” or “bot” version of its own website for agent-to-agent commerce to improve the speed and control over how other agents access its inventory, reviews, ratings, descriptions, prices, and more.

  Agility is essential as the rules of engagement evolve. Leading retailers will continuously assess where and how they partner with third-party agents and appear to customers across agent listings. And they’ll optimize accordingly to assert control over the end-to-end shopper journey and their brand perception.

Think like an agent in 2026

With AI already reshaping the marketing and sales funnel, hesitation isn’t an option. Here are three strategic, practical moves to make now:

Dig your customer moat to protect on-site traffic. Third-party agents are disintermediating high-value direct shopping, with the added threat of multivendor basket assembly. But leading retailers know that scarcity powers demand. They will offer exclusive products, premium bundles, loyalty-point multipliers, lower prices, and other rewards that entice consumers to visit sites and make direct purchases. Exclusive last-mile, value-added services such as installation and protection plans can also protect direct touchpoints. Consider how Best Buy’s product data currently appears on AI platforms, but it reserves services such as Geek Squad protection for its own site.

Redefine retail media monetization models. Bain research shows that in the US and Europe, roughly 65% of advertiser spending on retail media is on-site, mostly driven by sponsored search and product listings. That’s at risk if traffic plummets or shifts to third-party agent recommendations.

Metadata is becoming the new advertising asset. Leading retailers will collaborate with brands on product-display pages that respond to shoppers’ natural language prompting and put their products on third-party agents’ short lists. As AI rewires search and search advertising over time, retailers can use their first-party prompt data to monetize new models on-site, such as sponsored agent recommendations, agent influence fees, and API fees with attribute premiums. This is already happening: Amazon is serving sponsored ads in Rufus chats, Google is surfacing sponsored products in its AI Overviews, and OpenAI is testing ads in ChatGPT in partnership with retailers such as Target. Retailers that do this well can even put a premium on their on-site offering. If demand signals are broad and less specific than a keyword, effectively matching it to a conversion opportunity creates more value and commands higher pricing than a keyword search. The key will be proving and communicating that to brands.

In addition, industry leaders will play defense by emphasizing in-store and off-site media, which capture around 15% and 20% of retail media spending, respectively, and are growing fast. Winners will develop full omnichannel product offerings across the marketing funnel to protect retail media revenue growth. However, all of this means they’ll need to maintain control of customer data and purchase signals.

Win the referral, and retain ownership of data, fulfillment, and checkout. Use watermarking, tiered access to critical software, and tracking to preserve the value of first-party data. Where third-party agents close transactions, use partnerships to retain visibility into consumer behavior. Furthermore, don’t get relegated to a “dumb fulfillment pipe.” Hold onto last-mile logistics and checkout when possible. If not, ensure that agents can accurately reflect pricing and promotions, especially for on-site–only offers.

The core challenge is that AI shifts loyalty from brands and retailers to outcomes. The next generation of winning retailers will make shoppers care about who they transact with and where it happens. They will make their unique value apparent to human and agent alike, enduring in a transparent, comparison-fueled marketplace. Most importantly, they will act now, setting the rules for everyone else.

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The authors would like to thank Leah Johns, Florence Thienpont, Katherine Hall, Ricky Swieton, and Dylan Goldberg.