How B2B Buyers Are Using AI to Research Tools Like VidWorth

Two separate 2026 buyer surveys point in the same direction. A Gartner survey of 645 B2B buyers found 45% had used generative AI tools in their most recent purchase process, primarily to gather information on vendors and products. Separately, G2's research with over 1,000 buyers found 51% now start their research with an AI chatbot rather than a traditional search engine.

If that's where evaluation increasingly starts, being accurately represented inside an AI assistant's answer matters as much as ranking on a search results page — arguably more, for buyers who never see a traditional results page at all.

What actually makes a product easier for an AI system to represent accurately:

• A clear, first-party statement of what the product is, in plain language, published where it can be found and read.
• Explicit statements of fit and non-fit — what the product is for, and what it isn't, so a model doesn't have to guess or generalize.
• Verifiable, specific claims (a documented mechanism, a named integration, an actual API or protocol) rather than adjectives like "best" or "leading," which carry no evidence a model can check.

VidWorth's own answer to "what is it, and who is it for": a YouTube video-to-revenue attribution platform for creators and agencies, built around a video-level tracked-link join, not a general analytics dashboard. It also exposes a workspace-scoped MCP endpoint, so an AI assistant like Claude can be connected directly to a customer's own data — not just informed about the product from the outside. Read what that connection can actually do in what can an AI assistant do inside VidWorth via MCP.