A new Semrush survey of business buyers found that brand recognition is the weakest factor in getting noticed inside an AI-generated vendor list, cited by just 7 percent of respondents. The strongest factor, cited by 53 percent, is how closely a vendor’s content addresses the buyer’s stated need. For content teams built around brand-first messaging, that gap points the wrong way.
PPC Land reported the findings July 20, drawing on a Semrush study titled “How AI tools shape the B2B buying process.” Fieldwork ran from March through April 2026 across 643 US professionals; 21 were removed for failing a quality check, leaving published figures based on 519 respondents who confirmed using AI tools for work. The sample skews senior: 54 percent are final decision-makers, and 41 percent sit inside a decision-making group.
The study asked what makes one vendor stand out when an AI tool surfaces several names. Matching a buyer’s specific use case led at 53 percent. A clear, detailed description followed at 50 percent, and highlighting concrete benefits and outcomes came in at 38 percent. Brand recognition trailed the entire list at 7 percent.
One factor complicates a purely merit-based reading. Showing up early, or being named first in an AI answer, swayed 36 percent of buyers. That is a position effect, not a merit effect. Order inside an answer can matter almost as much as the substance behind it.
AI tools now touch every stage of B2B buying, according to Semrush’s data. 97 percent of respondents said AI helped them discover new vendors, 92 percent said it shaped their shortlist, and 83 percent said it influenced their final decision. 66 percent said they routinely turn to AI to research vendors and solutions for work.
That reach has a documented limit. 66 percent of respondents also said they have noticed vendors missing from AI results entirely, and 26 percent said this happens frequently. A page written for precise use-case matching cannot fix a visibility problem that starts further upstream, at whether the vendor gets surfaced by the model at all.
The study carries the caveats that come with any single-vendor survey. Semrush sells search and AI visibility tooling, giving it a commercial interest in findings that favor tactical content work over brand spend. The data is also self-reported buyer perception, gathered through a survey, not observed behavior tracked through actual AI sessions. Buyers describing what swayed them is not the same as measuring what actually did.
The timeline adds its own caution. Fieldwork closed in April 2026, Semrush shared the report with press on July 14, and PPC Land’s coverage reached trade press six days later, on July 20. Three months separate the underlying data from the write-up, in a market where model behavior and citation patterns shift quickly.
Still, the factor ranking gives content teams a specific lever, not just a mindset shift. Three moves matter this quarter. Rewrite core solution and comparison pages so the first two paragraphs answer a concrete use case, not company positioning. Add a labeled outcomes section above the brand narrative on every product page. Then check where those pages actually rank inside AI-generated comparison lists. A 36 percent position effect means a page’s rank inside an answer, not just its content, is now worth monitoring alongside its rank in classic search.
PPC Land reported the findings on July 20, 2026, based on a Semrush study, “How AI tools shape the B2B buying process,” shared with press on July 14, 2026 following fieldwork conducted from March to April 2026.
