Adobe Launches AI-Powered Product Discovery to Optimize LLM Shopping Experiences

Adobe rolls out AI-powered product discovery for LLM shopping

Adobe has launched AI-powered product discovery for large language model (LLM)-based shopping through Adobe Commerce. The capability uses Adobe Catalog Agent to provide structured product information to AI-powered discovery systems. For more than two decades, ecommerce product discovery has primarily relied on search engines, marketplaces, and on-site search. Conversational AI is adding another channel, with customers increasingly using platforms such as ChatGPT, Microsoft Copilot, Claude, Gemini, and other LLMs to recommend products, compare options, and answer buying-related questions before visiting an online store. According to Adobe Digital Insights, traffic from AI sources to US retail sites grew 125% year-over-year from April through June 2026. During the November-December 2025 holiday shopping period, AI traffic to US retail sites was up 693% year-over-year. Adobe Catalog Agent adds structured product information Adobe Commerce now includes Adobe Catalog Agent, which enriches product detail pages with structured information drawn directly from the Commerce catalog. The information is provided through a machine-readable layer intended for AI crawlers and LLM-powered discovery systems. The structured product information can include: * Product names and descriptions * Attributes and specifications * Categories and variants * Compatibility information * Pricing and availability * Product relationships * Use-case information This gives AI applications additional context to interpret products, connect customer queries with relevant products, and generate recommendations. The capability works behind the existing storefront, so product detail pages, imagery, and the existing buying journey do not need to be changed. Consistent product information across channels Catalog Agent can enrich product names, descriptions, and use-case phrases directly within the Commerce product catalog. Making these changes at the source allows the same product information to be used across: * Storefronts * Advertising pipelines * Marketplaces * AI-powered discovery experiences As product information changes, the updated catalog information can be used across these different surfaces, giving LLM-powered systems more context about each product and helping maintain consistent product information across channels. Product discovery for LLM surfaces Traditional product discovery includes: * Search engine optimization (SEO) * Product feed optimization * Marketplace visibility * On-site merchandising AI-powered commerce adds another discovery layer in which applications can interpret shopper intent and use structured product information rather than relying only on keyword matching. Product discovery for LLM surfaces in Adobe Commerce exposes structured commerce data that AI applications can use when responding to conversational shopping queries. Examples include: * "Show me lightweight trail running shoes suitable for marathon training." * "Compare these two laptops for video editing." * "Find accessories compatible with this camera." The approach supports product discovery through conversational AI alongside existing ecommerce discovery methods. Agentic AI and Adobe Commerce Adobe positions Catalog Agent as part of its agentic AI capabilities for commerce. The agent provides a product knowledge layer that allows AI applications to work with structured catalog information when products are surfaced, understood, and recommended through AI-powered shopping experiences. Adobe said its AI commerce roadmap will continue to include catalog intelligence, enrichment, governance, and discovery capabilities as LLM-powered shopping develops. Business and customer use cases Adobe identifies several outcomes from making structured product information available to AI-powered shopping systems. These include making products easier for AI applications to find, understand, and recommend, while providing AI assistants with more product context when answering customer questions. The structured information can also support conversational responses that help customers evaluate products and make purchasing decisions. It complements traditional SEO by providing product information for intent-based discovery through AI applications. As AI agents become more involved in the buying journey, product catalogs can also serve as a source of structured information for those systems. Support for technical teams Adobe Commerce provides native capabilities for AI agents to work with structured catalog data, reducing the need for businesses to build custom integrations or manually expose product information to AI applications. Catalog Agent can use existing Commerce services and data, including: * Catalog information * Product attributes * Inventory * Pricing * Product relationships This enables AI applications to retrieve current product information instead of relying on outdated or inferred data. The capability is designed to work with existing Commerce investments without requiring organizations to redesign their catalog architecture or replace their existing commerce systems. Availability Adobe Catalog Agent for product discovery on LLM surfaces is available as a native capability in Adobe Commerce on Cloud (PaaS). It is not a separate third-party platform or integration. Adobe says existing Adobe Commerce customers can use the capability without introducing a separate solution or duplicating product data.

Adobe Launches AI-Powered Product Discovery for LLM Shopping

The way customers discover products is changing. For more than two decades, ecommerce has been optimized for search engines, marketplaces, and on-site search. Today, a new discovery channel is rapidly emerging: conversational AI. Customers are increasingly asking ChatGPT, Microsoft Copilot, Claude, Gemini, and other large language models (LLMs) to recommend products, compare options, and answer buying questions before they ever visit a storefront. This shift represents a fundamental change in how products are discovered. From April 2026 through June 2026, traffic from AI sources to US retail sites grew 125% compared to the same period last year, according to Adobe Digital Insights. This continues the momentum that was observed during the most recent holiday shopping season (November to December 2025) where AI traffic was up 693% year-over-year. Success is no longer determined solely by search engine rankings or merchandising rules -- it depends on whether AI systems can understand, reason about, and confidently recommend your products. This is where Adobe Commerce with product discovery on LLM surfaces can deliver value. Richer product context without changing the storefront. Optimizing for AI discovery doesn't mean redesigning your storefront. Customers still experience the same product detail pages, imagery, and buying journey. Behind the scenes, however, AI search assistants need far more context than what's typically presented to human shoppers. Adobe Commerce now features Adobe Catalog Agent to help enrich product detail pages with structured product information drawn directly from the Commerce catalog. This enhancement is delivered in a machine-readable layer that is intended for AI crawlers and LLM-powered discovery systems, without changing the human shopping experience. By exposing richer product names, attributes, specifications, compatibility, availability, pricing, and other relevant catalog data, this agentic capability gives AI applications greater confidence when interpreting and recommending products. It provides them with the context they need to connect the customer search to the product. The result is improved product visibility across AI-powered discovery experiences, helping brands appear more frequently and more accurately when shoppers begin their buying journey through conversational AI. Consistent product messaging across every sales channel. AI recommendations are only as good as the product information they understand. A product name, description, and supporting use cases tell the story of what a product is, who it's for, and when it should be recommended. If that story varies across channels -- or lacks the context AI needs -- valuable products can be overlooked. Catalog Agent in Adobe Commerce enriches product names, descriptions, and use case phrases directly within the Commerce product catalog. By making these enhancements at the source, every downstream sales surface benefits from the same high-quality product messaging, from storefronts and advertising pipelines to marketplaces and AI-powered discovery experiences. This source-first approach ensures brands maintain consistent, governed product narratives everywhere their catalog appears. As product information evolves, every channel stays aligned, giving LLMs a clearer understanding of each product while helping merchants deliver accurate, consistent messaging wherever customers discover and purchase their products. Better product visibility in the age of AI commerce. As AI search assistants become a preferred interface for product discovery and shopping, product visibility takes on a new meaning. For years, merchants have invested in making products discoverable through: * Search engine optimization (SEO) * Product feed optimization * Marketplace visibility * On-site merchandising These remain essential, but AI-powered commerce introduces an entirely new discovery layer. Product discovery for LLM surfaces in Adobe Commerce helps prepare your product catalog for this shift by exposing rich, structured commerce data -- including product attributes, specifications, categories, variants, pricing, availability, and product relationships -- in a format that AI-powered experiences can understand and use. Rather than relying on keyword matching alone, AI applications can interpret shopper intent and reason about products using trusted, governed catalog data from Adobe Commerce. This enables more natural shopping experiences, where customers can ask questions like: * "Show me lightweight trail running shoes suitable for marathon training." * "Compare these two laptops for video editing." * "Find accessories compatible with this camera." These conversational buying journeys represent the next evolution of digital commerce, and they depend on high-quality, AI-ready product data. Agentic AI is the foundation for this new era of AI-powered commerce in Adobe Commerce. Catalog Agent establishes the trusted product knowledge layer that enables merchants to participate in emerging AI shopping experiences today, while creating a platform for future innovation. As Adobe continues to expand its AI commerce roadmap, customers can expect additional catalog intelligence, enrichment, governance, and discovery capabilities that will further enhance how products are surfaced, understood, and recommended across the growing ecosystem of AI-powered shopping experiences. Why this matters for business leaders. For commerce executives, agentic AI isn't just another feature in Adobe Commerce. It is an investment in future-proofing product discovery. As more purchasing journeys begin upstream in conversational AI experiences, brands that expose rich, structured product information will be better positioned to compete for customer attention. The business outcomes include: Greater product discoverability. Products become easier for AI-powered shopping experiences to find, understand, and recommend. Improved recommendation quality. AI assistants can provide more relevant answers because they have access to richer product context rather than relying on limited public information. Higher-quality customer experiences. Customers receive accurate, conversational responses that help them make purchase decisions faster. Reduced dependency on keyword optimization. Structured product intelligence complements traditional SEO by supporting intent-based product discovery. A stronger foundation for Agentic Commerce. As AI agents increasingly assist customers throughout the buying journey, your catalog becomes a strategic business asset rather than simply a product database. Value for technical teams. For technical teams, the agentic AI in Adobe Commerce removes much of the complexity involved in making commerce data AI-ready. Rather than building custom integrations or manually exposing catalog information to AI applications, Adobe Commerce provides native capabilities that enable AI agents to work with structured catalog data. The agent can leverage existing Commerce services, including catalog information, product attributes, inventory, pricing, and relationships, enabling AI applications to retrieve live, accurate information rather than relying on outdated or inferred data. Since the solution is built into Adobe Commerce, organizations can adopt AI-powered commerce capabilities without redesigning their catalog architecture or replacing existing commerce investments. Available natively across Adobe Commerce. One of the biggest advantages of product discovery on LLM surfaces with Catalog Agent in Adobe Commerce is that it is not an add-on platform or third-party integration. The agent is a native capability of Adobe Commerce and is available in Adobe Commerce on Cloud (PaaS). Organizations can take advantage of AI-ready catalog capabilities while continuing to use the Adobe Commerce platform they already know, helping reduce implementation complexity and accelerating time to value. For existing Adobe Commerce customers, this means AI-powered product discovery can become part of their commerce strategy without introducing a separate solutions or duplicating product data. Preparing for the next generation of commerce. Commerce is entering a new era where customers increasingly interact with AI before they interact with your brand. The businesses that succeed will be those whose products are not only searchable, but understandable. Adobe Catalog Agent in Adobe Commerce helps transform your product catalog into an AI-ready foundation that supports conversational shopping, richer product discovery, and future agentic commerce experiences. As LLM-powered shopping continues to evolve, product visibility will depend less on who has the most keywords and more on who has the best structured product knowledge.