The Agentic Shift: How AI is Rewriting the Rules of Retail Media

By Kimeko McCoy | May 19, 2026

The traditional retail funnel—a linear path starting with a search query on a retailer’s website and ending at the digital checkout—is undergoing a seismic shift. As Large Language Models (LLMs) evolve from simple chatbots into “agentic” assistants capable of executing complex tasks, the retail industry faces an existential question: What happens to the multi-billion-dollar retail media network (RMN) business when the consumer’s first stop is no longer a retailer’s homepage, but an AI interface?

For Tim Peterson, Digiday’s executive editor of video and audio, this future has already arrived. “I’m very close to actually having AI do my grocery shopping,” Peterson noted on a recent episode of the Digiday Podcast. By leveraging tools like Notion and Claude, Peterson has built an automated system that handles meal planning, recipe sourcing, and the subsequent generation of grocery lists. He is not an outlier; he is a harbinger of a broader consumer migration toward agentic commerce.

The Main Facts: The Rise of the AI Middleman

The core premise of the modern retail media landscape is built on the ownership of the search intent. When a shopper visits Walmart.com or Macy’s to search for “running shoes,” the retailer captures that intent, displays native advertising, and converts the sale. However, the rise of generative AI platforms—ChatGPT, Gemini, and Microsoft Copilot—threatens to intercept this intent.

According to a pivotal April report from Adobe, 55% of consumers now utilize AI tools for shopping inspiration and product discovery. This shift represents a massive migration of "eyeballs" away from destination retailer sites and into the black boxes of LLMs. If the discovery phase of the buyer’s journey occurs within an AI interface, the traditional RMN model—which relies on traffic to the retailer’s own digital storefront—risks becoming obsolete.

Chronology: From Static Search to Predictive Agents

The trajectory toward this disruption has been swift, characterized by three distinct phases:

• Phase 1: The RMN Gold Rush (2020–2023): Retailers realized their first-party transaction data was a goldmine. By building RMNs, companies like Target, Walmart, and Instacart turned their websites into high-value ad platforms, creating a new, high-margin revenue stream that bypassed the volatility of third-party cookie depreciation.
• Phase 2: The Off-site Expansion (2023–2025): Recognizing that on-site inventory was limited, retailers began expanding their reach. Acquisitions like Walmart Connect’s integration of Vizio and partnerships between Instacart and Roku were designed to keep the retailer’s data relevant in environments where consumers spend their time: streaming services and social media.
• Phase 3: The Agentic Disruption (2026–Present): We are now in the era of agentic commerce. AI is no longer just providing links; it is actively making decisions on behalf of the consumer. As AI systems become integrated into personal digital ecosystems, the "retailer-as-a-destination" model is being challenged by the "retailer-as-a-utility" model.

Supporting Data: The Shrinking Funnel

The data suggests a deepening disconnect between traditional retail channels and modern consumer behavior. The Adobe report is perhaps the most significant indicator of this trend, highlighting that more than half of the shopping population is already relying on AI to bypass the manual discovery process.

• Traffic Erosion: As AI becomes the primary entry point for commerce, the "top of funnel" traffic—long the lifeblood of RMNs—is beginning to dry up.
• Conversion Complexity: While retailers have historically relied on direct attribution (a search on our site leads to a sale on our site), agentic commerce obfuscates the path to purchase. If an AI suggests a product based on a complex algorithm, the brand’s ability to attribute that sale to a specific RMN ad campaign becomes significantly harder to prove.
• The Valuation Gap: Investors have pumped billions into RMNs, valuing them based on the premise of exclusive access to high-intent shoppers. If that intent is mediated by a third-party AI, the premium value of that media inventory is at risk of devaluation.

Official Responses and Strategic Pivots

Industry leaders are not sitting idly by as their traffic models are disrupted. Several major players have already begun testing strategies to secure their position within the LLM ecosystem.

Target’s Roundel Strategy

In February, Target’s retail media arm, Roundel, made headlines by becoming one of the first major retailers to aggressively integrate its advertising ecosystem into ChatGPT. By promoting both its own private-label products and select partner brands directly within the LLM’s interface, Roundel is attempting to meet the consumer where they are. This "embedded RMN" strategy seeks to ensure that even if the consumer isn’t visiting Target.com, they are still seeing Target-sponsored recommendations.

The Challenge of Performance

Despite these efforts, there is significant skepticism regarding whether these experimental models will replicate the performance of traditional on-site advertising. “The challenge there is does the value or really the performance of retail media advertising hold up in these environments?” Peterson asks.

In a native retail environment, ads are contextually relevant to the search. In an LLM, the context is generated by the machine. If the AI prioritizes efficiency or generic utility over brand-sponsored placement, the value of the advertising inventory—and the resulting ROI for brands—could plummet.

Implications: The Future of Retail Media Networks

The transition to agentic commerce poses three primary risks to the RMN business model:

1. The Attribution Crisis

Attribution has always been the "holy grail" of retail media. Advertisers buy into RMNs because they can tie spend directly to sales. As AI agents handle the purchasing process, they may use multiple data sources, making it nearly impossible for a brand to isolate the impact of a specific RMN ad. If advertisers cannot prove incrementality, they will likely shift their budgets back toward performance marketing channels that offer better transparency.

2. The Loss of Data Monopoly

The primary leverage of an RMN is its first-party data. However, if LLMs become the intermediaries, they may begin to command their own data sets, effectively turning the retailers into "suppliers" rather than "media owners." Retailers could find themselves in a position where they are paying the AI platforms for access to their own customers, flipping the current power dynamic entirely.

3. The Need for "Agent-Proofing"

To survive, RMNs must shift their focus from being destination websites to being "API-first" platforms. They must provide the data infrastructure that allows AI agents to make the right decisions for the consumer—while ensuring their sponsored partners are part of those decisions.

“If they prove that out—if they can show that their ads actually drive these AI-assisted sales—then that will help them to continue to exist and attract some form of revenue in an agentic commerce world,” Peterson concludes.

Conclusion: The Race to Re-intermediate

The retail media network of 2026 is at a crossroads. The growth of AI agents is not a temporary trend but a fundamental shift in how the digital economy functions. Retailers who view themselves as gatekeepers of their own digital storefronts are likely to see their influence wane. Conversely, those who successfully integrate their data and inventory into the AI-driven discovery process—much like Target is attempting with Roundel—may find a way to thrive.

The future of retail media will not be defined by who owns the most traffic, but by who owns the best data integrations within the AI systems that run the modern consumer’s life. As the line between search, recommendation, and execution continues to blur, the most successful RMNs will be those that effectively "agent-proof" their business, proving that even in an automated world, brand visibility and conversion remain inextricably linked to the power of their data.