### AI Agentic Commerce Emerges as Retail Media CatalystAI agentic commerce involves autonomous AI systems that act on behalf of shoppers, handling product discovery, comparison, negotiation, and purchases across platforms. Recent analyses frame this development through bull and bear cases for retail media networks (RMNs), highlighting its potential to either amplify or erode strategies reliant on onsite search and advertising.The trend builds on existing deployments, where AI agents embedded in conversational interfaces influence buying decisions by scanning options, filtering by preferences like budget or nutrition, and executing transactions. Retailers possess enriched first-party data, positioning them to feed these agents structured information for recommendations, while agents could bypass traditional sites, threatening search-driven revenue that constitutes up to 80% of RMN income.### Bull Case: Agents as Demand AmplifiersIn the optimistic scenario, agentic AI generates new revenue streams for RMNs by leveraging retailers' data advantages. Agents require real-time structured data on availability, pricing, and attributes, which retailers control, turning catalogues into licensable assets via APIs. This elevates product content as a differentiator, favoring standardized feeds over visual lifestyle assets.Repeat-purchase categories like grocery or electronics suit automation, channeling demand to reliable fulfillment networks and boosting basket sizes. Retailers can launch proprietary agents for loyalty personalization or replenishment, retaining control within their ecosystems. Conversion rises as agents cut friction, expanding core retail operations and media revenue. Google Cloud emphasizes enriching catalogues with imagery and demand attributes to enable this, creating dynamic digital shelves accessible to agents.### Bear Case: Disintermediation RisksConversely, agentic AI poses an existential threat by shifting discovery to chat interfaces, collapsing onsite traffic. Shoppers describing needs in natural language—now 37% using over eight words, up from 4% last year—bypass keyword-sponsored listings. Onsite ads with 70-80% margins vanish, offsite data monetization dilutes as agents aggregate cross-retailer records, leaving in-store as the resilient stream.Third-party agents aggregate and rank results outside retailer control, commoditizing choice and eroding loyalty. Experts note retailers resist broad third-party access to protect customer relationships and data monetization, limiting agentic scope to partnerships. This mirrors past disruptions but accelerates with conversational search rivaling keyword eras.### Implications for E-Commerce Content InfrastructureAgentic commerce demands transformation in content systems central to e-commerce scalability.Product feeds must evolve from static exports to AI-readable structures with real-time metadata on features, inventory, and promotions. Standardization accelerates as agents parse attributes for comparisons, penalizing incomplete data and favoring marketplaces with broad distribution.Card quality intensifies: agents prioritize depth—reviews, visuals, specs—over curation, requiring fuller, consistent entries to rank in recommendations. Speed to shelf shortens; no-code tools and AI automate enrichment, cutting creative cycles from weeks to hours while ensuring accuracy across channels.No-code platforms gain traction for rapid feed optimization, integrating generative AI to generate attributes or summaries. API connectivity becomes mandatory, treating agents as VIP customers for autonomous negotiation and fulfillment. Bain & Company. McKinsey & QuantumBlack.### Strategic Realities Across CategoriesAdoption varies: low-interest repeats delegate easily, while passion-driven buys like makeup or decor resist full automation due to emotional factors. Retailers balance blocking agent access to safeguard ads against opening for discoverability.Hybrid paths emerge—proprietary agents for branded experiences, optimized data for generative outputs (GXO over SEO). RMNs hedge by fortifying omnichannel, tracking LLM ad formats, and monetizing metadata via sponsored recommendations or influence fees. Both cases coexist: traffic dips offset by licensing gains, demanding flexible infrastructures.###The rise of agentic commerce underscores the critical need for robust product information management. As AI agents increasingly dictate product discovery and comparison, the quality and accuracy of product data become paramount. This trend emphasizes the importance of standardized, AI-readable product feeds, which simplifies the process of data ingestion, enrichment, and transformation. Consequently, retailers can benefit from a unified platform which streamlines the creation of high-quality, comprehensive product data which can be shared seamlessly across channels, including agent-driven interfaces. A well-structured data feed is covered in detail in our article on <a href="/blog/product_feed/">product feeds</a>. In e-commerce, the <a href="/blog/product_feed/">product feed</a> is a critical piece, and it's important to avoid <a href="/blog/common-mistakes-in-product-feed-uploads/">common mistakes</a>. Understanding how to manage your data is covered in other articles, for instance, <a href="/blog/json-format-how-one-store-turned-chaos-into-fast-synchronization/">JSON Format: How One Store Turned Chaos into Fast Synchronization</a>, or using a <a href="/tools/deltafeed/">delta feed</a>. And in developing these feeds, it's crucial to understand <a href="/blog/how-to-create-sales-driving-product-descriptions-without-spending-a-fortune/">how to create sales-driving product descriptions.</a>