Selling to Bots: How B2B Companies Can Compete When AI Agents Sit Between Sellers and Buyers

1) Build a Dedicated Bot-Centric Enterprise Unit

Stand up a dedicated unit with its own product, sales, technology, and operating model built around serving bots as key stakeholders.

When algorithmic trading reshaped equity markets in the early 2000s, financial information vendors faced the same choice: start investing more for machines or keep the focus on human traders. The internal debate over allocating capital to data feeds for algorithms or terminals for people played out for years. Many vendors eventually chose to build dedicated enterprise units with their own product, R&D, sales, technology, and operations model that focused on selling to machines.

Retail is one example. Companies selling directly to consumers who are using LLMs to conduct product research face the greatest immediate pressure to engage with bot capabilities. BDO’s A Retailer’s Guide to Leading in the Agent Economy describes cross-functional teams formed to test bot-to-bot transactions, but the same restructuring logic applies wherever an agent starts sitting between the seller and the buyer.

Product management gets more difficult when the machine is the customer. Before AI and bots, when a person was conducting product research, a product manager could use data exhaust from website logs (e.g., search terms, filtering) and/or feedback from salespeople to observe the behavior. As human buyers conduct product research via a third-party bot, the product manager may have limited-to-no-visibility into buyer behavior. Product managers will need to work with bot operators for access to prompt history and other signals of buyer intent. Expect buyer behavior to keep shifting as buyers, sellers, and bots learn new ways to interact.