Where the Agentic AI Opportunity Lies for B2B Shopping in 2026

While B2B online buying habits may still be catching up with B2C, that doesn’t mean B2B can’t take advantage of the same advancements that are making waves in B2C shopping. Agentic AI, which differs from the more popular generative approach to AI, refers to an autonomous AI system that is engineered to research, compare, recommend and execute actions on behalf of very real humans. It is increasingly known as the more difficult-to-achieve proactive approach to AI, while generative AI — which responds to prompts, queries and requests — is quick, easy and reactive.

In online B2B and B2C commerce, agentic AI is touted as transformational for the buying and selling process, with AI agents deployed in systems that can study context, apply contract logic, understand customer needs, anticipate buyer behavior and more. Think of agentic AI as a step up from automation, which follows scripted steps but doesn’t necessarily compute who the customer is or what customizations they might like. If your business uses online commerce to move products and/or services, you may already be considering agentic AI systems for quicker, more efficient buying.

Agentic AI in B2B applications

If your company manufactures and sells various configurable parts to other businesses, you likely have mountains of spec sheets and product catalogs at the ready for customers. Traditionally, customers would peruse your offerings and consult with sales representatives to determine the best parts or configurations for their use case. While there are a ton of variables at play — such as timing, budget and resources — this process could derail projects if buyers are unable to come to a confident purchasing decision in time.

Agentic AI commerce promises to change all that and speed up the process from discovery to purchase. Instead of trying to sift through piles of information, potential customers can start with an initial request or spec. AI agents would then pull the best matches from your portfolio, along with an explanation of what they are and why they are a potentially good fit for the request. They would be able to tell if the product is in stock, what the lead times are, and potentially even initiate transactions when ready to move forward. Customers get the answers they need in a more timely fashion, and your products get recommended from the start.

Unlocking the key to agentic AI with data

As with most best practices when it comes to AI, the quality of the experience will depend on the quality of the data put into it. Organizations looking to deploy agentic AI shopping will need to ensure teams across the board can normalize and structure product and customer data across multiple sources. AI agents won’t be able to offer recommended matches if you don’t have all your technical specifications and configurations for every product in your catalog accessible and organized.

The process of completing purchases on company websites should also already be optimized at this point, and not relying on traditional purchase orders that are sent via email or fax. Since AI agents would be working independently, in contrast to an automated system responding to pre-defined sets of rules, they would need a structured digital environment to operate successfully.

With a more complex and variable purchasing process than B2C, B2B shopping relies on companies that can quickly offer up-to-date spec comparison charts, compliance with industry standards and regulations, budget options, and contract readiness.

Curious to see where your website currently stands? Try a quick search in ChatGPT (e.g., “Show me the best commercial flooring options for humid climates”) and see where or if your brand or product gets recommended.

Potential risks and drawbacks of agentic AI shopping

Robert Rose from the Content Marketing Institute recently likened the emergence of agentic AI to railroads in the 19^th century — an innovation touted as also transforming how commerce could be conducted. While railroads and trains brought the world closer together, they also brought about numerous deadly crashes that shocked the public. While today’s society benefits from over a century of engineering progress and safety regulations, the fundamental danger in railroads remains the same.

The same could be said for agentic AI. While not as dramatic as railroad failings, one misstep or misalignment by an AI shopping agent can drastically increase costs or turn off customers and impact your business. Last year, Gartner predicted that over 40% of agentic AI projects will be canceled by the end of 2027 “due to escalating costs, unclear business value, or inadequate risk controls.”

Much of agentic AI’s success will also depend on the foundations on which it is built. Rose cautions that agentic AI projects will feel less like a “feature upgrade” and more of a “governance test of your business strategy.” After all, agentic AI can’t necessarily fix existing strategies with gaping holes or unclear directives, nor can it resolve operational confusion or create alignment where it doesn’t exist.

Where agentic AI commerce will go next

It’s clear that a new era for buyers and sellers has arrived, bringing with it new technologies, tools, and paradigms that businesses of all kinds will have to contend with. While many agentic AI projects are still in the early stages of deployment — with advanced business cases yet to come — the promise of an autonomous AI agent that can handle simple tasks like breaking down color finishes or identifying pipe fittings of a certain size is alluring to many. Forward-looking teams should remember to consider agentic AI commerce not as a tool to improve operational efficiencies, but as a true next step in their growth.