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, a nd 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.
