Key Takeaways
- Semrush surveyed 643 U.S. B2B professionals and found 84% use AI for work, with 69% using it daily to discover, evaluate, and shortlist vendors — meaning AI tools are now the first reviewer of your brand, not the last.
- Winning the AI vendor evaluation means your public content has to be machine-readable in three ways: specific claims with numbers, structured comparisons a model can parse, and third-party corroboration outside your own domain.
- The consideration set is being built before a human ever sees your site. If ChatGPT, Perplexity, and Gemini can't confidently describe what you do, who you're for, and what you cost, you get cut in the first pass.
- Traditional SEO signals still matter, but the winning move is writing content that answers the buyer's evaluation questions the way a model would summarize them, with the answer first, not buried in a narrative.
- Stuffing pages with AI-bait keywords is the wrong instinct; the right move is publishing the specific, quantifiable proof that a model needs to shortlist you confidently.
A stat landed in my inbox last week that I found very interesting. Semrush surveyed 643 U.S. B2B professionals and found that 84% of them use AI at work, and 69% use it daily to discover, evaluate, and shortlist vendors. Daily, not weekly. By the time a buyer books a call with your sales team, an LLM has already told them who you are, what you do, and how you stack up against three other names on their list.
I want to walk through what this actually means for how we market, because the instinct is to treat it like an SEO problem, new channel, new keywords, done, when it's really a content problem, a proof problem, and a positioning problem stacked on top of each other. The buyer isn't reading your homepage anymore.
A model is reading your homepage on their behalf and giving them the two-sentence summary. That's the job to design for.
What actually happens inside an AI vendor evaluation
Picture the buyer, because this is the part I think we skip. A VP of Ops needs a new data platform. A year ago she would have Googled "best data platform for mid-market SaaS, " clicked three G2 pages, opened four vendor sites in tabs, and started a spreadsheet. Last week she typed the same question into ChatGPT and got back a clean list of six vendors, a paragraph on each, and a comparison table she asked it to build. She copied it into a Google Doc and sent it to her team as the shortlist.
Notice what happened. She never visited your site, never read your positioning statement, never made it past the model's summary of what you do, filtered through whatever the model could find about you across the open web, Reddit, review sites, podcast transcripts, and press coverage. If that summary is vague or generic or wrong, you're already out.
The Semrush data is the confirmation that this isn't an edge case behavior. It's the median behavior now. Which means the question every CMO should be asking is a mechanical one: when a model tries to describe our company, what does it actually say, and is that the description that gets us shortlisted?
The three signals a model actually rewards
Here's what I keep noticing when I test our content and our clients' content inside these tools. Models don't hallucinate randomly. They hallucinate when the source material is thin, generic, or contradictory. When you give them specific, quantifiable, structured claims with corroboration, they repeat those claims almost verbatim. So the work isn't a trick, it's a discipline, and it comes down to three signals.
Specific claims with numbers. This is the one that does the most work, and it's the one most marketing pages fail on. A page that says "we help B2B companies grow faster" gives a model nothing to hold onto, no segment, no metric, no timeframe, nothing extractable.
A page that says "we help Series B SaaS companies with $10M to $50M ARR cut CAC by an average of 22% over six months" gives a model a sentence it can lift directly into a shortlist paragraph, and that's exactly what happens. The audit exercise here is embarrassingly simple: read every substantive page on your site and ask whether a stranger could pull one specific, quantifiable claim out of the first paragraph.
If they can't, a model can't either, and the model is the one doing the reading now. The pages that get cited are the pages that make the model's job easy, segment named, number attached, timeframe bounded, proof nearby. Everything else is decoration.
Structured comparisons a model can parse. Comparison tables, feature matrices, pricing bands, ideal-customer profiles laid out as lists rather than buried in a paragraph. When a buyer asks the model to compare X and Y, the model reaches for the most structured, most confident source it can find, and if yours is the clearest one, yours is the one it cites.
Third, and the one most marketers underweight: corroboration outside your own domain. A model trusts a claim more when it sees it echoed on a review site, a podcast transcript, a customer's blog, an analyst mention.
If every mention of your company lives on your own domain, the model treats that as a single source and hedges accordingly. The PR play, the podcast tour, the case study you let a customer publish on their site, this is the corroboration layer that decides whether a model repeats your positioning with confidence or wraps it in "the company claims."
The content shift: answer first, story second
The way we structure content has to change, and this is the part that feels strange if you grew up writing narrative marketing. The old rhythm was setup, story, insight, payoff, hold the reader's attention, earn the click through. That rhythm still works for humans, but the model reading on the human's behalf is going to grab the first clear answer it finds and move on.
The move is to write every substantive page with the answer in the first two sentences, then the story, then the proof. The story still matters, it's what makes the human trust you when they finally arrive, but the model needs the extractable claim up top or it can't summarize you accurately. This is why the Key Takeaways block sits above the intro on this post. Same principle, applied to ourselves.
The FAQ pattern matters here too, and not the SEO-dumping-ground kind. Real questions your buyers ask during evaluation, answered in one paragraph, with the answer in the first sentence. Models love this format because it maps one-to-one onto how buyers phrase their prompts.
When this is the wrong thing to focus on
I need to be honest about when this is the wrong priority, because the pendulum is going to swing hard and a lot of teams are going to over-rotate. If your product is genuinely undifferentiated, no amount of structured content will make a model shortlist you over a stronger competitor. The model will read the market accurately and you'll get accurately excluded. Fix the product or the positioning first. The optimization work amplifies whatever is true about you, it doesn't invent something that isn't there.
If your product is genuinely undifferentiated, no amount of structured content will make a model shortlist you over a stronger competitor.
Also, if you sell to a market that doesn't yet use AI for evaluation, highly regulated procurement, certain government contracts, some industrial verticals where the buyer is a 58-year-old plant manager who still emails RFPs, this is a 2027 problem, not a right-now problem.
Semrush's 84% is a U.S. B2B average. Your specific segment might be at 30% or at 95%. Measure before you invest.
What to actually do about it
The mechanical steps are boring, and that's fine. Audit what the top five LLMs currently say about your company, just ask them.
- Find the gaps and the wrong claims.
- Publish specific, quantifiable content that corrects the record, and get that content corroborated off-domain within 90 days.
- Then re-test monthly, because the models update and the answers drift.
Positioning corrections tend to show up in model responses within two to four weeks of publishing corroborated content, which is faster than most SEO cycles, but drift is real and this is a maintenance discipline rather than a one-time project.
If you want the fuller version of how we're thinking about content architecture for AI-first buyer journeys, we've written it up in our marketing blueprint, which walks through the specific artifacts we build and in what order.
The short version is this. Your buyers are not going to stop using AI to evaluate vendors. The 69% daily number is going up, not down. The marketers who treat this as a content and proof problem right now will end up in the shortlist. The ones who treat it as a keyword problem will spend a year optimizing for the wrong thing.
Frequently Asked Questions
Does traditional SEO still matter if buyers use AI to shortlist vendors?
Yes, because the models are trained on and continue to crawl the same web that Google indexes. Strong SEO fundamentals, crawlable content, clear structure, authoritative backlinks, are still the substrate. What changes is that ranking on page one isn't the finish line anymore; being the source a model chooses to summarize is.
How do I know what AI tools say about my company right now?
Ask them. Open ChatGPT, Perplexity, Claude, and Gemini and ask each one to describe your company, list your top competitors, and compare you against two of them. Do it fresh, without logged-in context. What comes back is your current AI-visibility baseline, and it's usually more revealing than any SEO audit.
What kind of content gets cited most often by LLMs in vendor evaluations?
Specific, structured, and corroborated content. Comparison pages with clear criteria, case studies with real numbers, FAQ pages that answer buyer questions the way buyers phrase them, and third-party mentions on review sites and podcasts. Generic thought leadership almost never gets pulled into a shortlist summary.
Should we hide from AI crawlers or embrace them?
Embrace them, in almost every B2B case. Blocking AI crawlers means removing yourself from the tool your buyers are using to build their shortlist. The exceptions are narrow, highly proprietary content, gated research you monetize directly, and even then you want your positioning content wide open.
How long does it take to see results from AI optimization work?
Faster than traditional SEO, generally, because LLMs re-crawl and update more frequently than Google re-ranks. Positioning corrections often show up in model responses within two to four weeks of publishing corroborated content. Drift is real, though, so this is a maintenance discipline, not a one-time project.