AI is fundamentally rewriting how retail media operates, shifting the industry from keyword-driven search and traditional sponsored placements toward intent-based discovery powered by autonomous shopping agents and conversational interfaces. This transformation represents more than an incremental upgrade to existing advertising models—it signals a wholesale reimagining of how retailers monetize discovery, how brands reach consumers, and how product information must be structured to remain visible and competitive in an AI-mediated marketplace.[1][2]
The shift is already underway. As consumers increasingly delegate shopping decisions to AI agents and conversational commerce platforms, the mechanics of retail media are being fundamentally disrupted. Where sponsored product listings once dominated e-commerce advertising, retailers are now preparing for a future where visibility within AI recommendation flows becomes the primary battleground for brand attention. This means the auction dynamics of retail media—the bidding systems, placement hierarchies, and pricing models that have defined the space for the past decade—are being recalibrated for an era where context, intent, and conversational relevance matter more than keyword matching or historical click-through rates.[1]
The Collapse of Keyword-Centric Discovery
Traditional retail media has relied on a relatively straightforward proposition: brands bid on keywords, compete for placement above or beside search results, and pay when their ads drive clicks or conversions. This model has generated enormous value for retailers—U.S. retail media spending is projected to reach $60 billion in 2025 and surpass $100 billion by 2028, growing five times faster than total digital ad spending.[2] Yet the underlying assumption—that consumers actively search for products using specific terms—is being challenged by the rise of agentic commerce.
When a consumer delegates a purchase decision to an AI agent, they are no longer typing keywords. Instead, they are expressing intent through natural language queries, behavioral history, and contextual signals. An autonomous shopping agent might receive an instruction like "find me a professional laptop suitable for video editing," process data about the consumer's budget, technical requirements, and previous purchases, and then autonomously navigate retail catalogs to identify suitable options. In this scenario, traditional keyword bids become irrelevant. What matters instead is whether a product's underlying data—its specifications, attributes, performance characteristics—is structured richly enough for the AI to understand its relevance to the shopper's intent.
This represents a profound shift in how product information must be organized and maintained. Retailers and brands can no longer rely on thin product listings with minimal attributes. The AI agents making purchasing recommendations on behalf of consumers need comprehensive, accurate, and contextually rich product data to function effectively. This means that product feeds, catalog structures, and content standards are becoming critical infrastructure not just for e-commerce operations, but for retail media viability itself.
Retail Media in the Agentic Era
The monetization model for retail media in an agentic marketplace will likely mirror patterns already established in other AI-mediated environments. Just as brands bid for visibility within Google Shopping feeds or search result rankings, retailers will eventually enable brands to bid for prominence within AI agent recommendation flows. However, the nature of these placements will differ significantly from current sponsored product models.
In today's retail media landscape, a brand's visibility often correlates with bid price and historical performance metrics like click-through rates and conversion rates. In an agentic era, visibility will increasingly depend on relevance signals that AI systems can interpret: product-market fit for specific customer segments, accuracy of product attributes, customer satisfaction metrics, inventory availability, and alignment with the customer's expressed or inferred needs.
This shift has profound implications for how retailers structure their media networks and how brands approach product marketing. A brand can no longer simply outbid competitors and guarantee visibility. Instead, they must ensure that their product data is comprehensive, accurate, and optimized for AI interpretation. This introduces new dimensions of competition in retail media—not just a race to bid the highest, but a race to provide the most trustworthy, richly attributed product information.
The Product Content Imperative
The foundation of effective retail media in an AI-driven marketplace is product data quality and completeness. Autonomous shopping agents making recommendations on behalf of consumers need to distinguish between products based on hundreds of attributes, specifications, and contextual signals. A laptop's processor generation, RAM configuration, screen resolution, weight, battery life, warranty terms, and compatibility with specific software all matter when an agent is matching products to customer intent. So does information about sustainability, manufacturing, supply chain transparency, and brand reputation.
This creates an unprecedented demand for rich product catalogs. Retailers and brands that have historically minimized investment in product content—relying instead on consumer reviews, user-generated content, or minimal manufacturer specifications—now face pressure to dramatically expand the breadth and depth of their product information infrastructure.
The implications extend to catalog management and product feed maintenance. Where retailers might once have tolerated occasional data inconsistencies, missing attributes, or delayed updates to product information, an AI-mediated marketplace demands near-perfect accuracy and completeness. An autonomous shopping agent that recommends a product with inaccurate specifications or missing critical information damages not only that specific transaction, but erodes consumer trust in the agent itself, which carries broader consequences for the retailer's media business.
Similarly, the speed at which retailers can bring new products to market becomes increasingly important. In current retail media models, a new product can launch with minimal information and gain visibility through paid promotions. In an agentic marketplace, a new product feed with incomplete or poorly structured data may be invisible to AI recommendation systems until its catalog information is fully matured. This creates pressure to develop faster, more efficient product onboarding processes that front-load content quality rather than treating it as a post-launch consideration.
No-Code and AI Tools in Content Infrastructure
The complexity of managing rich product catalogs at scale has driven adoption of no-code and AI-assisted tools in product content management. Retailers and brands increasingly rely on automation to generate product descriptions, extract and standardize attributes, translate content into multiple languages, and maintain data quality across diverse product categories and global markets.
AI is becoming essential to this infrastructure in several ways. Generative AI tools can accelerate the creation of product descriptions, technical specifications, and marketing copy tailored to different channels and audiences. Machine learning models can identify missing or inconsistent data across product feeds, flag potential errors, and suggest corrections. Natural language processing can extract structured attributes from unstructured content, converting product information into standardized formats compatible with various retail and advertising platforms.
The no-code movement has democratized access to these tools, enabling smaller retailers and brands to manage complex product catalogs without building custom software engineering infrastructure. This matters because the barrier to participation in an agentic retail media marketplace shouldn't be the ability to invest in custom technology. Tools that abstract away technical complexity—allowing merchandising and marketing teams to manage product data through visual interfaces rather than code—make it feasible for diverse retailers and brands to meet the data quality standards that AI agents require.
Standardization and Data Interoperability
The shift toward agentic commerce is creating renewed pressure for product data standardization. When AI agents operate across multiple retailers and platforms, they need consistent, predictable ways of interpreting product information. A laptop's screen resolution needs to mean the same thing whether the data comes from a retailer's proprietary catalog system, a brand's product information management platform, or a third-party data aggregator.
This is driving renewed attention to data standards and schema frameworks that have long existed in e-commerce but have often been inconsistently implemented. Standards like Global Trade Item Number (GTIN), ICECAT, and structured data markup (Schema.org) are becoming less optional and more essential. Retailers and brands that can reliably structure their product data according to widely recognized standards gain advantages in visibility within AI systems, because that data becomes easier for AI agents to interpret and trust.
The implication is that participation in agentic retail media will likely require adherence to more rigorous data standards than many retailers and brands currently maintain. This represents a form of standardization pressure that operates differently from regulatory requirements—it emerges from technical necessity rather than legal mandate, but it will likely prove just as consequential.
The Near-Term Operational Challenge
For retailers managing this transition, the immediate challenge is managing two retail media models simultaneously. The keyword-driven, search-based sponsored placement model remains dominant and highly profitable. Retail media networks built on sponsored products, sponsored brands, and display advertising are generating substantial incremental revenue for major retailers. Simultaneously, retailers must invest in the product data infrastructure, catalog standardization, and agentic interfaces that will power the next generation of retail media.
This dual operating model creates resource allocation challenges. Should a retailer prioritize optimization of its existing retail media business, which currently generates billions in revenue, or invest heavily in infrastructure for an agentic future that remains partially uncertain? The answer, increasingly, is both. Retailers cannot afford to neglect current retail media performance, but they also cannot delay investment in the product data and catalog infrastructure that agentic commerce will require.
The timeline for this transition matters significantly. If agentic commerce accounts for a material portion of e-commerce transactions within three to five years—a plausible scenario given current trends in consumer adoption of AI-assisted shopping—then retailers that delay investment in catalog quality and data standardization will face a severe competitive disadvantage. Brands that begin now to invest in richer product data, standardized attributes, and AI-compatible content infrastructure will possess significant advantages as retail media evolves.
Conclusion: Preparation and Urgency
The podcast conversation highlighting this transformation underscores a critical point for the retail and e-commerce industry: the shift toward agentic commerce is not a distant future scenario, but an emerging reality that demands immediate strategic response. Retailers must begin now to evaluate their product data infrastructure, assess compliance with emerging data standards, and invest in tools and processes that will enable them to compete in a retail media landscape mediated by AI agents rather than keyword searches.
This is not merely a technological upgrade; it is a fundamental reshaping of the relationship between retailers, brands, and consumers in commerce. The retailers and brands that adapt quickly—that invest in product content, standardize their data, and prepare their infrastructure for an AI-mediated marketplace—will be positioned to thrive. Those that delay risk finding themselves marginalized by more agile competitors as agentic commerce becomes mainstream.
As the industry pivots towards AI-driven product discovery, the quality and accessibility of product data will be paramount. At NotPIM, we recognize this shift and provide a no-code solution that simplifies product data management. Our platform enables businesses to enrich, standardize, and optimize product information, ensuring they meet the demands of AI agents and remain competitive in the evolving e-commerce landscape. We see a strong demand for tools that help structure product data, and NotPIM is designed to deliver precisely that.