B2B buyers know AI makes mistakes. They know it can hallucinate, misinterpret information and confidently deliver wrong answers. And then they use it to evaluate their next six-figure purchase.
That, my friends, is the AI trust paradox in B2B buying.
Forrester’s 2025 Buyers’ Journey Survey found that nearly all business buyers (94 percent) report using AI during their buying process. In fact, the firm found that “Gen AI has become the most influential interaction across the buying cycle.” Yet, Forrester notes that AI-powered search tools “can create mistrust by delivering incomplete or unreliable information.”
The tension between AI use and trust shows up across the workplace, too. Workday’s August 2026 AI@Work Survey of nearly 6,000 workers worldwide found a 76 percent AI adoption rate, but only 43 percent of workers trust the accuracy of AI output.
While those studies surveyed different populations, they both raise a pressing question for B2B vendors: If businesspeople don’t entirely trust AI, why is it becoming so influential in purchasing?
The likely answer is surprisingly simple: AI doesn’t have to be blindly trusted to be useful, and it doesn’t have to be the final word to influence a buying decision.
That said, how buyers deal with their mistrust of AI is where things get even more interesting.
Don’t trust AI? Ask AI
Here’s a strikingly ironic finding from AIRI’s B2B AI Buying Behavior Survey of 521 B2B purchasers and purchasing influencers:
86 percent use multiple AI models at least sometimes to check another model’s work or findings.
More than half (57 percent) do so often or always.
Buyers know AI can get things wrong, yet their go-to solution is to ask another error-prone AI. It’s like consulting Dumb, then checking with Dumber for a second opinion.
While it seems nonsensical, it doesn’t mean AI checking AI is pointless. Comparing answers from separate AI models can expose disagreements and prompt buyers to dig deeper.
But it has a blind spot. Cross-checking catches places where models disagree, not mistakes they share. And as AI answers become more fluent and polished, a confident consensus is easy to mistake for the truth.
The larger point is that mistrust isn’t driving B2B buyers away from AI; it’s creating another AI-assisted task.
So why keep using a tool that creates more work for you? Good question with another simple answer: Because even with the extra checking, AI still saves buyers time.
The easy button is hard to resist
B2B buyers have work to do. Lots of it. (Just like you!) Getting it done quickly is a primary driver for using AI.
Two-thirds (66 percent) of B2B buyers AIRI surveyed said they use AI to save time aggregating information that could take hours or days manually.
Interestingly, when B2B buyers get time back, they don’t necessarily put their feet up. Among AIRI’s survey respondents who said AI saves them time:
82 percent use that extra time to focus on more strategic or important purchases.
60 percent add more options to their vetting lists.
49 percent attend to other job duties.
That tracks with a workplace in which productivity expectations are rising. Korn Ferry’s September 2026 Workforce 2026 Global Insights Report, based on more than 16,000 professionals across 11 markets, found that 62 percent report significantly heavier workloads than two years ago, while 61 percent say they’re performing the responsibilities of more than one role.
Add lean teams, hiring constraints and layoffs in some organizations, and the appeal of an always-available research assistant is easy to understand.
Here’s the practical calculus: AI may make mistakes, but it can gather, summarize and compare information fast enough to make double-checking worth the trouble. Accuracy matters. But short of bending time and space, AI may be the closest thing workers have to fitting 50+ hours of work into a 40-hour week.
Your boss may be using AI, too
There’s yet another reason buyers may ask AI questions even when they question its answers: Someone else higher up the food chain is going to ask.
In AIRI’s survey:
54 percent of B2B buyers said a boss, senior executive or buying committee member had used AI to evaluate or question a purchase recommendation they brought forward.
Another 28 percent said it may have happened without their knowledge or visibility.
Separately, when we asked what factors influenced their own use of AI in vendor evaluation, 23 percent cited concern that a boss or other purchasing influencer would use AI to vet the recommendation.
Put yourself in the buyer’s seat. You’ve spent weeks assessing vendors, and you have a recommendation ready for approval. If your boss is going to run that recommendation through AI, wouldn’t you want to know what it might say before the meeting?
In this scenario, AI becomes more than a research assistant. It’s also a fact-checker and devil’s advocate, helping B2B buyers anticipate the questions a boss or buying committee may raise.
Ultimately, once AI starts determining which questions get asked, which claims get challenged and which alternatives get considered, its influence reaches the seller whether the buyer fully trusts it or not.
AI doesn’t have to be right to change the deal
A B2B buyer can distrust AI and still act on questions it raises. For sellers, that’s when the AI trust paradox becomes a revenue issue.
If AI names a competitor, that competitor might enter the evaluation. If AI flags a security concern, the buyer may demand more evidence. If AI questions a product claim, the sales team may have another objection to overcome. Even an AI finding the buyer ultimately rejects can consume time or redirect attention.
And sometimes the consequence is considerably bigger: 56 percent of B2B buyers who responded to AIRI’s survey said they had removed a vendor, product or service after AI analysis surfaced a discrepancy between claims and actual capabilities.
Note: This doesn’t mean 56 percent of B2B buyers surveyed blindly accepted whatever AI said about a vendor. It means AI analysis was part of a process that ended with a vendor’s removal.
That’s the vital takeaway for sellers: Buyers can question AI’s conclusions and still investigate the issues it surfaces. And that investigation can change the outcome.
Ensure your sales pitch holds up under AI scrutiny
As a seller, what can you do to influence the outcome of AI scrutiny? Start with one assumption: Your buyers will use AI whether they trust it or not. Take action by shoring up proof for what AI tells them about your company, solution or category.
Focus on the four things within your control: what you claim, how consistently you say it, what AI surfaces when buyers ask and how well you enable your internal champions to defend you.
Make your claims verifiable. Pair product promises with current documentation, customer proof, technical evidence and credible third-party references buyers can independently check. Our blog, “AI Visibility Is Only the First (and Lowest) Hurdle,” shows where to start: Bring the teams responsible for your revenue story together, examine what buyers and their AI tools can find without a seller present and determine whether your most important claims are backed by clear, accessible evidence they can independently verify.
Keep the story consistent. Make sure your website, sales materials, security documentation, technical specifications and third-party information reinforce rather than contradict one another. In our blog, “What Your CRM Cannot Tell You About Buyer Use of AI,” we provide a seven-step process for assessing how AI represents your company, finding weak, outdated or inconsistent proof across on- and off-site sources, strengthening the evidence behind your claims, aligning those revenue signals and testing them continuously.
Test likely buyer questions. Look beyond whether AI recommends your company. Examine the objections, competitive alternatives and gaps it may surface across multiple models. Our blog, “What Goes in the RFP? Buyers Are Asking AI,” explains how to conduct an AI response audit by testing realistic buyer questions across multiple AI platforms and repeated sessions, analyzing which criteria, sources, competitors and gaps consistently surface, then comparing those findings with how you position and sell your offering.
Equip your internal champions. Give buyers the evidence they need when a boss or buying committee asks AI to challenge their recommendation. Our blog, “The Buying Committee Has a New Member: AI,” walks through how to identify the claims, differentiators and outcomes you need stakeholders to understand, test what AI finds when asked to investigate them, identify unsupported claims or contradictions, and close those gaps with customer evidence, technical information, case studies and independent validation.
Do these things well and you give both AI and your buyers better information to work with, fewer contradictions to trip over and stronger evidence to support the case for choosing you.
That doesn’t change the B2B buyer AI trust paradox, and it doesn’t need to. B2B buyers can remain skeptical of AI while continuing to use it. Because let’s face it: Buyers are unlikely to wake up one morning and say, “You know what I miss? Spending hours researching and comparing vendors by hand.”
The genie is out of the bottle. B2B buyers may never trust AI completely, but they’re not going back to doing this work without it.
For sellers, the practical question isn’t whether B2B buyers trust AI. It’s whether your company, claims and evidence hold up when B2B buyers use it.
Want to learn more about how buyers are using AI in the purchasing process? Download AIRI’s B2B AI Buying Behavior Survey Report, “The AI-Powered B2B Buyer Has Arrived.”