B2B Buyers Use AI to Research, But Don't Trust It Alone

63% of B2B buyers use AI to research vendors, but 72% fact-check every answer. Here's how to win the trust gap AI created.

By Social Sprint Team · · 9 min read

B2B Buyers Use AI to Research, But Don't Trust It Alone

Key takeaways

  • 63% of B2B tech buyers now use AI during their purchase journey, but 72% say they always or very often fact-check what it tells them.
  • 47% of buyers trust online resources less than they did a year ago, even as AI makes research faster.
  • Third-party reviews and peer validation are winning the credibility gap AI created. Vendor-controlled content is losing ground.
  • For revenue teams, the fix isn't more content. It's more verifiable, human proof: real reps, real customers, real conversations on LinkedIn.

63% of B2B tech buyers now use AI somewhere in their purchase journey, according to TrustRadius's 2026 B2B Buying Disconnect Report (a survey of 1,862 buyers and 444 vendors). But AI hasn't made buyers more trusting. It's made them more skeptical: 72% say they always or very often fact-check what AI tools tell them, and 47% say they trust online resources less than they did a year ago (TrustRadius, 2026). That's the disconnect in the report's title, and it's the single most important thing a revenue team needs to understand about buying in 2026. Buyers are using AI to move faster, then spending that saved time verifying the answer through sources AI can't fully control: peer opinions, third-party reviews, and the people actually using the product. If your go-to-market motion is built entirely on vendor-controlled content (your website, your one-pagers, your outbound sequences), you're optimizing for the step buyers trust least.

The AI Research Habit Is Now the Default

AI-assisted research stopped being a novelty sometime in the last two years. It's now simply how a majority of B2B tech buyers start a purchase journey. TrustRadius found 63% of buyers used AI during their most recent evaluation, whether that meant asking ChatGPT to shortlist vendors, having Perplexity summarize category differences, or using an AI Overview in Google search results as a starting point.

This shift matters for two reasons:

  • It compresses the top of the funnel. Buyers arrive at a shortlist faster, often before a vendor's sales team ever knows they exist.
  • It changes what "discovery" means for marketing and content teams. Getting cited inside an AI-generated answer is now part of the job, alongside traditional SEO.

That second point is why Generative Engine Optimization (GEO), structuring content so AI answer engines can find, understand, and cite it, has become a core skill for B2B marketing teams. It's a topic worth its own deep dive: see our guide on getting LinkedIn content cited by AI search.

But getting cited by AI is only half the battle. The TrustRadius data shows the other half is what happens right after the AI gives its answer.

Why Buyers Fact-Check Everything AI Tells Them

72% of buyers say they always or very often fact-check AI-generated recommendations. That's not a small skeptical minority. That's nearly three-quarters of the market treating AI output as a first draft, not a final answer.

Why the skepticism? A few likely drivers, consistent with what the report describes as a "buying disconnect":

  • Buyers have been burned by confident-sounding AI answers that turned out to be outdated, generic, or subtly wrong.
  • AI tools often summarize vendor marketing copy back to the buyer, which buyers correctly recognize isn't independent verification.
  • The stakes of a B2B software purchase (budget, implementation time, internal reputation) are high enough that "an AI told me so" isn't a defensible reason to a boss or a buying committee.

The result is a verification layer that sits between the AI answer and the buying decision. Every claim an AI tool surfaces gets checked against something else before it's trusted. And 47% of buyers say their overall trust in online resources has actually declined over the past year, even as those resources have gotten faster and more AI-assisted. Speed and trust are moving in opposite directions.

What This Means for Vendor-Controlled Content

None of this means vendor content stops mattering. Websites, one-pagers, and case studies still do the job of explaining what a product does and who it's for. What changes is their role in the decision. The TrustRadius data suggests vendor content is increasingly used to confirm a shortlist, not to build trust in it. Trust gets built somewhere else first: in a peer's comment, a review site, or a rep's LinkedIn post that reads like a real opinion instead of a pitch.

That's a subtle but important distinction for a Head of Marketing planning next quarter's content calendar. Doubling down on more landing pages and more gated PDFs addresses a problem buyers no longer rank as their top concern. Buyers already have plenty of vendor content. What they say they lack is a reason to believe it, and that reason has to come from outside the vendor's own channels.

The Assets Winning the Trust Gap AI Created

If AI output gets fact-checked, the question becomes: fact-checked against what? TrustRadius's findings point to the same answer B2B marketers have suspected for years, now sharpened by AI. Third-party reviews and peer validation are winning the credibility gap. Vendor-controlled content is losing ground.

That reframes what "content marketing" needs to produce in 2026. It's not just about volume of vendor-authored material. It's about generating independently verifiable social proof that exists outside your own website:

  • Customer reviews on third-party platforms
  • Peer conversations and recommendations (including on LinkedIn, where buyers can see who a rep actually is)
  • Employee and founder voices that read as people, not brand accounts

That last point connects directly to a related finding worth knowing: employee advocacy on LinkedIn has been shown to lift win rates by 64% and cut acquisition cost, largely because a real employee's post reads as a peer opinion, not vendor marketing (see our breakdown of the employee advocacy data). In a world where 72% of buyers fact-check AI, a rep's genuine, consistent LinkedIn presence functions as exactly the kind of verification source buyers are looking for.

What This Means for Revenue Teams Building a Social Selling System

For a Head of Marketing or Sales Manager at a 10-200 person company, this data has a clear operational implication: your team's individual LinkedIn presence isn't a nice-to-have alongside your marketing content. It's becoming the trust layer that determines whether your marketing content gets believed at all.

Three practical shifts follow from that:

  1. Treat rep visibility as a trust asset, not a vanity metric. A sales manager or founder posting consistently, in their own voice, with real opinions, is generating exactly the kind of third-party-feeling proof that survives a fact-check. A locked-down, legal-approved-only company page does not.
  2. Build a system, not a scramble. Buyers now expect to find a real, active person behind a vendor before they trust the vendor. That means your reps need a repeatable system for showing up on LinkedIn consistently, not a one-off post when someone remembers to.
  3. Point AI research toward your people, not just your product pages. If a buyer's AI tool is going to surface information about your company, make sure your reps' expertise, case studies, and customer proof are visible and citable in places AI tools and buyers both check: LinkedIn posts, comments, and profiles included.

This is the core case for social selling as a system rather than an individual habit: it turns your whole revenue team into a distributed source of the peer-level proof that's winning the trust gap AI created.

How to Build Trust Signals That Survive the Fact-Check

Start with what's checkable. A buyer fact-checking a vendor isn't looking for more claims, they're looking for evidence they can verify themselves. That means:

  • Naming real outcomes and real customers wherever you're allowed to, instead of vague superlatives.
  • Encouraging reps to comment and engage authentically on LinkedIn, since a documented history of real engagement is itself a trust signal a skeptical buyer can check.
  • Making it easy for happy customers to leave third-party reviews, since those reviews are exactly the resource TrustRadius found buyers now trust more than vendor content.

A simple gut check for any revenue team: if a skeptical buyer pasted your latest campaign claim into an AI tool and asked it to verify the claim, would the AI find independent evidence, or only your own marketing? If the answer is "only ours," that's the gap to close first.

None of this replaces good marketing content. It sits underneath it, as the layer that makes the content believable once a buyer starts checking. And it compounds: every rep who shows up consistently on LinkedIn, every customer who leaves a review, every real conversation in the comments adds one more independently checkable data point for the next buyer's fact-check.

FAQ

Q: What percentage of B2B buyers use AI to research vendors?
A: 63% of B2B tech buyers used AI somewhere during their purchase journey, according to TrustRadius's 2026 B2B Buying Disconnect Report, which surveyed 1,862 buyers and 444 vendors.

Q: Do B2B buyers trust AI-generated recommendations?
A: Not fully. 72% of buyers say they always or very often fact-check what AI tools tell them, and 47% say they trust online resources less than they did a year ago, per the same report.

Q: What do B2B buyers trust more than AI or vendor content?
A: The report found third-party reviews and peer validation are winning the credibility gap AI created, while vendor-controlled content is losing ground.

Q: How does this affect a company's marketing and sales strategy?
A: It means investing in verifiable, human proof (customer reviews, peer recommendations, and genuine employee presence on platforms like LinkedIn) matters as much as producing vendor content, since that's what buyers check after AI gives them an answer.

Q: What is GEO and how does it relate to this trend?
A: Generative Engine Optimization (GEO) is the practice of structuring content so AI answer engines like ChatGPT, Perplexity, and Google AI Overviews can find and cite it. It affects whether buyers see you during AI research, but it doesn't replace the third-party proof buyers seek out afterward. Read more in our guide to GEO for LinkedIn content.

The Bottom Line

AI has changed how B2B buyers research. It hasn't changed what they trust once they've done that research. The revenue teams that win in this environment aren't the ones with the most content, they're the ones whose reps show up on LinkedIn as real, checkable, trusted people. That's the system Social Sprint is built to help you run: consistent, authentic rep presence across your whole team, systemized instead of left to chance. See how it works on the Social Sprint dashboard.