• by Laurie Sullivan , Yesterday

Brands that continue to focus on their traditional websites rather than AI assistants have created what Adobe calls a "visibility gap" between brands and customers, making any type of change toward agentic more challenging. 

AI will become the first point of interaction in the customer journey when making purchases.

Consumers have not stopped using traditional search engines to find information, which has become a challenge for brands. Emarketer found that 82% of weekly AI users rely on Google Search daily, compared with 71% of all internet users.

The data from a May 2026 survey also shows that 63% of AI searchers turn to an assistant mainly for a direct answer to a specific question, while search engines are used more for browsing and comparing multiple sources, according to YouGov.

Brand success now depends on whether AI systems can understand and confidently recommend products.

Adobe Commerce has created a new product that makes it easier for large language models (LLMs) to read data stored in the backend of an online storefront. Consumers do not see it, but the LLMs and agents do.

Customers still experience the same product detail pages, imagery, and buying journey, but behind the scenes AI search assistants have more context to make better decisions.

Adobe calls the feature an "Adobe Catalog Agent." It is intended to help provide more product details with structured product information in a machine-readable format that draws the data directly from the Commerce catalog.

Agentic AI is the foundation for the next buying era, but as of now traditional search is not evaporating.

Product discovery for LLM in Adobe Commerce helps prepare the product catalog for this shift through structured commerce data that includes 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, AI applications can interpret shopper intent and reason about products using Adobe Commerce data.

This enables a natural shopping experience, where customers can ask lengthy questions such as "Show me lightweight trail running shoes suitable for marathon training."

It also can compare two laptops for video editing or find accessories compatible with a specific camera. These conversational buying experiences represent the next evolution of digital commerce, and they depend on high quality, AI-ready product data.

But combining traditional and AI driven search is not just for consumers purchasing the latest clothing trends.

Salesforce recently rolled out a suite for B2B commerce buying agents that combine agentic commerce, intent-driven search, omnichannel buying, giving companies the tools to meet buyers on any media channel.

Built into B2B Commerce on the Agentforce platform, acts as an intelligent buying assistant, and is available wherever the buyer needs to make a purchase on sites like WhatsApp and SMS.

They can find products, manage orders and complete purchases entirely through natural conversation, without calling a sales rep. It also will recommend products and include images with text.

Some companies have pushed back on the idea of making data readable to human and machines, even those responsible for building out services and when these formats are essential for optimization, interoperability and automated processing.