New Study Finds 73 Percent of B2B Buyers Now Use AI Tools Before Contacting Sales

A March 2026 multi-source analysis covering 680 million citations found that 73 percent of B2B buyers now use AI tools as part of their vendor research process before making contact with sales. A separate finding from the same period puts the number higher for enterprise software evaluation specifically, with 94 percent of enterprise B2B buyers using large language models to research vendors. The conversion data adds context: visitors arriving from ChatGPT convert to pipeline at 14.2 percent, compared to 2.8 percent for Google organic traffic — a 5.1x gap. Despite these figures, only 22 percent of B2B marketers currently track AI-sourced visibility or traffic.

Key highlights

The 73 percent figure comes from a multi-source analysis that tracked 680 million citations across AI platforms. A separate research stream found that 94 percent of enterprise B2B buyers use LLMs specifically to research vendors — a figure higher than general B2B buyer AI adoption because enterprise procurement involves more structured research phases. The studies measure different populations and methodologies, but both indicate that AI tool usage in the B2B buying process is no longer limited to early adopters.

Platform-level conversion data shows variation across AI engines. Claude-sourced traffic converts at 16.8 percent, ChatGPT at 14.2 percent, and Perplexity at 12.4 percent. All three sit well above Google organic's 2.8 percent benchmark. The explanation offered by researchers is stage-of-intent: a buyer who arrives via an AI engine has typically already consumed a synthesised answer about the problem category and is further along the evaluation process than a buyer who clicked an organic search result.

Only 22 percent of marketers currently track AI visibility, and just 25.7 percent have plans to develop content specifically optimised for AI citations. The gap between buyer behaviour and marketer measurement creates an attribution blind spot: deals influenced or initiated through AI engine touchpoints are being recorded as direct, dark social, or unattributed traffic in most reporting stacks. The commercial significance of AI citations is therefore being systematically undercounted in marketing measurement.

The measurement gap also affects content investment decisions. Without visibility into which content earns AI citations and which does not, content teams lack the feedback loop needed to optimise for this channel. Brands that build AI tracking into their measurement stack now will have compounding data advantages over those that address it later.