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

Adobe Commerce Introduces Catalog Agent for AI-Powered Product Discovery

Adobe has launched AI-powered product discovery capabilities through Adobe Commerce, introducing Adobe Catalog Agent to provide structured product information to large language model (LLM)-based shopping platforms.1

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The capability enriches product detail pages with machine-readable information designed for AI systems including ChatGPT, Microsoft Copilot, Claude, and Gemini.2

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Customers increasingly turn to conversational AI for shopping, asking LLMs to recommend products, compare options, and answer buying questions before visiting storefronts. According to Adobe Digital Insights, AI-driven traffic to US retail sites grew 125% year-over-year from April through June 2026.1

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During the November-December 2025 holiday shopping period, AI traffic to retail sites surged 693% year-over-year, marking a fundamental shift in ecommerce product discovery.2

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How Adobe Catalog Agent Structures Product Information

Adobe Catalog Agent works behind existing storefronts, adding a machine-readable layer intended for AI crawlers and LLM-powered discovery systems without changing product detail pages, imagery, or the buying journey customers experience.1

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The structured product information includes product names and descriptions, product attributes and specifications, categories and variants, compatibility information, pricing and availability, product relationships, and use-case information.1

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This agentic AI capability gives AI applications additional context to interpret products, connect customer queries with relevant products, and generate recommendations.2

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By exposing richer product attributes, specifications, compatibility, availability, and pricing, Adobe Catalog Agent helps AI systems understand and confidently recommend products during conversational shopping experiences.2

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Consistent Product Messaging Across All Sales Channels

Catalog Agent enriches product names, descriptions, and use-case phrases directly within the Commerce product catalog, ensuring consistent product information across multiple channels.1

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Making these changes at the source allows the same product information to be used across storefronts, advertising pipelines, marketplaces, and AI-powered discovery experiences.1

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As product information changes, updated catalog information flows across different surfaces, giving LLM-powered systems more context about each product while maintaining governed product narratives everywhere catalogs appear.2

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This source-first approach ensures brands deliver accurate, consistent messaging wherever customers discover and purchase products.2

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AI Systems Enable Intent-Based Product Discovery

Product discovery for LLM surfaces in Adobe Commerce exposes structured commerce data that AI applications can use when responding to conversational shopping queries.1

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Rather than relying solely on keyword matching, AI systems can interpret shopper intent and reason about products using trusted catalog data.2

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Customers can now ask natural questions like "Show me lightweight trail running shoes suitable for marathon training," "Compare these two laptops for video editing," or "Find accessories compatible with this camera."1

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These conversational buying journeys represent the next evolution of digital commerce, complementing traditional search engine optimization, product feed optimization, marketplace visibility, and on-site merchandising.2

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Technical Implementation and Future Roadmap

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.1

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Catalog Agent uses existing Commerce services and data, including catalog information, product attributes, inventory, pricing, and product relationships, enabling AI applications to retrieve current product information instead of relying on outdated or inferred data.1

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Adobe positions Catalog Agent as part of its agentic AI capabilities for commerce, establishing a product knowledge layer that allows AI applications to surface, understand, and recommend products through AI-powered shopping experiences.1

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Adobe's AI commerce roadmap will continue to include catalog intelligence, enrichment, governance, and discovery capabilities as LLM-powered shopping develops.1

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