Business-to-business (B2B) and business-to-consumer (B2C) ecommerce companies are at different levels when it comes to incorporating agentic artificial intelligence (AI) into their businesses, according to Paul do Forno, global commerce practice lead at Deloitte.
He gave an example of a Deloitte workshop in late 2025 in which less than 24% of suppliers said they had used agentic AI in the selling process. Do Forno described the scale of agentic AI as being from not having any agentic capabilities to being fully autonomous. He said based on his discussions with companies’ representatives, agentic AI in ecommerce is still “a long ways away in B2B for autonomous, let alone B2C, which will get there much quicker.”
“In B2B, there’s a bifurcated world,” do Forno told Digital Commerce 360. “B2B vs. B2C, and B2B always have been behind.”
He said Deloitte has found that companies are using agentic AI to solve and knock off individual problems as part of a larger digital transformation process.
“What we see is agentic attacking very specific friction points for different channels,” do Forno said. Instead of one chat solution addressing everything, he assessed that successful agentic options target friction points across different business processes.
Using AI agents to solve common B2B pain points
One such example could be reordering. A shopper can have an AI agent check if a product is available for delivery. The user can specify that they need the product in a week.
“An agent can go off and go look about the different systems, come back and [say]: sorry, that’s not available, but this is available. It’s an alternative but fits your needs,” do Forno said. “The availability to promise, that’s agents that we’re working [on] already.”
He also noted that B2B buyers can use AI agents for reordering. Buyers often make purchases through emails, PDFs and purchase orders, he said.
“That’s the No. 1 case of people starting with agentic in B2B. ‘I’ve got my PO. It’s attached as a PDF. Can you convert this into an order?’” do Forno posed.
He said B2B companies seeking to add agentic commerce to their technology stacks must have a core commerce platform or cloud. From there, those companies will “have the base capabilities then to roll out to the marketplaces, connect to the marketplace, connect to the punchout — connect to all these things. This is a lot like the baby steps. You gotta have, underneath, the core commerce, and then start building across all the different channels.”
How can B2B companies use agentic AI for discovery?
Among the different use cases for agentic AI are retailers and B2B companies using the technology on their own sites. There’s also the type that powers large language models (LLMs) such as OpenAI’s ChatGPT, Google Gemini, or Perplexity.
Discoverability in both cases is key to succeeding in an agentic AI-powered landscape, do Forno stated. He explained that B2B companies can get more from AI agents than basic-level chatbot answers to consumers’ and business buyers’ questions. With the right foundation, he said, they can help shoppers build orders. He gave an example of a supplier’s tests with discoverability.
“A lot of people are still doing the discovery, but now by the models,” do Forno said. “What they found was FAQs are more important — having the content that goes through that discoverability and all the different build intents in all the B2B cases.”
It could take 12 products to comprise a full component for a build, do Forno offered as an example. But if a company doesn’t make that clear by either autogenerating content or actually producing the content from scratch and associating it with products, then B2B buyers won’t find it. Companies should provide scenarios and use cases for products that they can feed to the AI, enabling more contextual, longtail queries. For example, he said, a company could specify that a product can be used outdoors for certain services, and then associate it with a known expert. That can help to influence agents in discovering products, he said.
“The visibility is actually now way more complex — the GEO of it all,” do Forno said. “That’s a lot of initial steps for a lot of companies that still just want to be under consideration.”
SEO vs. GEO practices for product data
Search engine optimization (SEO) establishes a baseline of best practices for generative engine optimization (GEO). A notable difference between the two is that SEO focuses largely on keywords. GEO, on the other hand, builds on that in LLMs to surface results that show more context.
“What’s different is you need to understand the intent versus just the keywords,” do Forno said. “Keywords might get very far. Maybe cursory and almost like a game. ‘What’s my keyword I can get?’ You need people who can validate and provide that intent, that connect the dots. And intent in lots of different complex ways, not just product information.”
If a buyer needs to build a house, and that project includes products that are associated, companies need to break all that down, do Forno said. If a marketing team has only done some of the basics of SEO and has not pushed most of a company’s catalog out, then there is still a lot of work to do, he added. In such a case, the company must expand its product information and all the different build types that buyers can use those products for.
And a major difference between B2B and B2C product data, he said, is the involvement of different regulatory standards. If a certain material can only legally be used in certain instances or handled a certain way, that makes the product data “super complex,” do Forno said.
Another difference is buying channels, he said. A buyer might not have access to certain products because they’re not approved to buy them.
Fitment matters, too. There can be a dozen permutations for a product, including color, size, shape and more. How a company identifies those permutations for the AI factors into how often an agent can or will surface a product.
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