We’ve written extensively about how artificial intelligence is changing how B2B buyers research vendors. But, one of the more interesting developments is how much that research still depends on human experience.
Case in point: AI Revenue Institute (AIRI)’s 2026 B2B AI Buying Behavior Survey found 59 percent of B2B buyers surveyed use AI to analyze user review sentiment across platforms.
Think about what this means. Buyers aren’t scrolling customer review sites and reading individual opinions like they’ve done in the past. Now, they’re asking AI to not only find, but to also analyze what customers are saying about vendors, compare experiences and identify patterns across multiple sources.
And customer reviews are only the beginning. There’s evidence that AI-generated answers draw heavily on the platforms where people share their experiences and expertise.
In a June 2026 study drawing on 126 million U.S. AI search prompts, Semrush found that AI-generated answers cite customer-review sites, online communities and independent publishers.
In a May 2026 analysis of 9.5 million AI citations across 16 B2B categories, Meltwater found that 47.5 percent came from user-generated-content platforms, including LinkedIn, Reddit and YouTube, while another 15 percent came from peer-review sites.
Ultimately, AI isn’t replacing human opinions and experiences in B2B buying. It’s making them easier to access, analyze and compare at scale. For B2B vendors, that means human proof has the potential to exert even greater influence on buying decisions than it has before.
Human proof provides what AI cannot
Human proof gives buyers something AI cannot produce on its own: evidence of how vendors and their solutions perform in the real world.
AI can compare product specifications, review reported capabilities and analyze performance data. But it cannot independently experience what it’s like to implement your solution, work with your support team or use your product every day. For that, it needs evidence from people who have actually done those things.
Human proof generally falls into three categories:
Direct customer evidence: Customer reviews, ratings, case studies, testimonials, interviews and presentations document real-world experiences with products and vendors, including implementation challenges, support quality and business outcomes.
Peer and practitioner experiences: Peers in similar roles share what it’s like to buy and use products day-to-day, while practitioners such as consultants, contractors, service providers and implementation partners share their experiences recommending, implementing and managing them for clients. Both surface through LinkedIn, Reddit, industry forums and professional groups.
Independent expert evidence: Industry analysts, independent testing labs, trade and technology journalists, and industry researchers provide third-party research, analysis and evaluations. These include customer surveys, industry studies, product testing and comparative assessments that help buyers evaluate vendor claims, understand product capabilities and limitations, and compare alternatives.
The value of these human sources hasn’t changed, but AI changes how buyers can discover and leverage them.
AI is changing how buyers access and leverage human proof
Traditionally, buyers seeking customer perspectives had to gather evidence one source at a time. Now, buyers can use AI to quickly find, aggregate, analyze and compare human experiences across multiple sources.
One reason is a process called AI grounding, in which search-enabled AI systems retrieve information from external sources to support their answers. Unlike traditional search, which helps buyers find relevant web pages, AI-powered search can retrieve specific facts, experiences and findings from those pages and incorporate them into a comprehensive response. Both Google and Microsoft describe how grounding enables AI systems to use retrieved information to generate answers.
For B2B vendors, this has important implications:
Individual customer outcomes, implementation experiences and independent evaluations can become evidence in AI-generated comparisons, even when buyers never read the original sources.
AI can also bring together findings from multiple sources, making recurring strengths, weaknesses and conflicting information more visible.
Together, these new realities mean that individual customer experiences and the broader patterns they reveal can play a bigger role in how buyers perceive, compare and evaluate vendors.
This makes it easier and faster to:
Spot patterns across customer experiences.
Identify recurring praise and complaints across multiple sources.Compare real-world vendor performance.
See how competing solutions perform during implementation and ongoing use.Understand strengths, weaknesses and trade-offs.
Examine how solutions perform under different circumstances.Uncover gaps between vendor claims and actual results.
Identify where customer experiences may contradict vendor promises.
Armed with those insights, buyers can more quickly:
Identify vendor issues for further investigation.
Focus on potential risks, concerns or gaps associated with potential vendors.Ask vendors more informed questions.
Probe vendors about specific capabilities, limitations and customer experiences.Verify key vendor information.
Cross-check important vendor claims against reliable sources.Narrow their vendor options.
Assess how well vendor solutions fit their requirements and refine their shortlist.
In short, AI-assisted review of human proof gives buyers a more informed basis for evaluating vendors, challenging claims and making purchasing decisions.
AI can extend the reach and influence of human proof
For vendors, the implications go beyond how AI helps buyers more efficiently evaluate human proof to include how AI changes the reach and influence of that proof.
Before AI assistance, buyers could discover human proof through Google, Bing and other search tools, but they generally had to visit individual sources to access and evaluate it:
Customer reviews and ratings on sites like G2, TrustRadius and Gartner Peer Insights
Customer case studies, testimonials and interviews published on vendor websites, shared by sales teams or presented at industry events
Peer and practitioner perspectives shared through LinkedIn, Reddit, technical forums, professional communities and blogs
Independent research and evaluations published by industry analysts, research firms, technology publications and independent testing organizations
Now, AI introduces another route for human proof to reach buyers. When AI retrieves and synthesizes information from these sources, they can become part of answers to buyers’ questions, even when prospects don’t view the original sources.
But the potential influence goes beyond individual reviews, recommendations or evaluations. AI can bring together evidence from multiple sources, giving buyers a broader view of how vendors and their solutions perform. For example, buyers can:
Gauge overall customer sentiment.
Are reviews predominantly positive or negative? How do satisfaction levels compare across vendors?Identify recurring strengths and weaknesses.
Are customers consistently praising technical support or reporting the same implementation challenges?Compare different types of evidence.
How do customer experiences align with findings from independent testing labs, analyst research and customer surveys?Examine conflicting perspectives.
Why might customers, practitioners and independent experts reach different conclusions about the same solution?
This aggregated view can give recurring experiences and consistent findings greater influence than isolated accounts. A single negative review might be easy to dismiss, but repeated complaints about the same issue could raise concerns. Conversely, positive customer experiences reinforced by independent evaluations may strengthen a vendor’s credibility.
Of course, AI’s analysis depends on the evidence it can access, which may be incomplete or unrepresentative. Either way, for B2B vendors, the implication is significant: AI can bring human proof into more buying conversations while making recurring experiences, consistent findings and conflicting assessments more visible. That can increase the influence of human evidence on how buyers perceive vendors and evaluate their options.
5 ways to make human proof count in AI-powered buying
As AI makes it easier for buyers to compare customer experiences, practitioner perspectives and independent evaluations, B2B vendors have an opportunity to make their human proof more useful and influential. The goal is to expand the body of credible human proof available to buyers while making that evidence easier for AI to find. Here are five ways to do that.
1. Test how your human proof holds up under AI scrutiny
Start by finding out what AI is telling prospective buyers about your company, competitors and customer experiences. Conduct an AI response audit, a process we’ve outlined in a previous blog, to establish a baseline of how AI represents your business and the evidence behind its answers.
What to do:
Test realistic buying questions.
Ask multiple AI platforms to compare vendors on customer satisfaction, implementation, performance, support, business outcomes and independent evaluations.Examine the evidence.
Review cited sources where available. Flag missing evidence, inaccurate or outdated details, and recurring strengths or complaints.Prioritize gaps.
Identify the gaps and inaccuracies most likely to affect how buyers perceive your company, and determine which require attention first.Repeat the audit.
Compare responses and sources over time. An October 2026 Ahrefs study found substantial turnover in AI citations, reinforcing the value of repeated testing.
2. Make evidence accessible, accurate and current
Once you know what AI is finding, focus on improving the information available to it. AI-powered search can retrieve specific facts from customer stories, product pages, research and other sources, making the way you publish and maintain that evidence important. Both Google and Microsoft provide technical guidance relevant to making content accessible to AI-powered search.
What to do:
Correct and update published information.
Address outdated capabilities, conflicting claims and superseded results on your website. Where third-party sources contain factual errors, request corrections from the publishers.Make important evidence accessible.
Publish substantive summaries when full case studies or licensed research must remain gated. Add transcripts or text summaries to customer videos, webinars and presentations.Use structured data to help search engines understand your content.
Work with your web team to add structured data using Schema.org standards to product pages, company information, articles and other relevant content. This markup helps search engines interpret your content and potentially display richer search results.Connect and contextualize evidence.
Use descriptive headings, identify relevant products and use cases, include dates and measurement details, and link to original research and independent evaluations.Make sure AI-powered search can find your evidence.
AI search systems use search indexes to retrieve information for their answers. Use Google Search Console and Bing Webmaster Tools to identify and resolve crawling, indexing and accessibility problems that could prevent your customer stories, research and other evidence from being discovered and cited.
3. Look at reviews collectively, not just individually
AI can analyze reviews across platforms, identify recurring praise or complaints, and compare customer sentiment about competing vendors. Patterns across reviews may become more influential than an individual rating or testimonial.
What to do:
Analyze the collective picture.
Use AI to compare reviews across relevant platforms and competitors. Identify recurring strengths, weaknesses and differences by industry, company size or use case.Amplify recurring strengths.
When customers consistently praise specific capabilities or outcomes, reinforce those strengths in competitive positioning, marketing campaigns and sales materials, supported by attributed customer evidence.Address underlying problems.
Share recurring complaints with product, support and customer success teams to investigate root causes and resolve legitimate issues.Refresh outdated reviews.
When customer issues are resolved or experiences change, invite reviewers to update their feedback. Platforms such as G2 offer review-refresh programs. Ask customers to describe their current experiences, regardless of whether their ratings change.Prepare sales teams for scrutiny.
Equip sellers with review insights, supporting evidence and objection-handling guidance, so they can reinforce strengths, address concerns and explain trade-offs, such as price versus quality or service levels.
4. Make customer stories work harder
As discussed, AI grounding can retrieve individual facts and outcomes from customer stories to support generated answers. So, for example, an implementation result, performance metric or customer experience may inform a vendor comparison without the buyer reading the complete case study. That creates opportunities to get more value from existing customer stories and make newly developed evidence more useful.
What to do:
Bring customer proof into product and solution pages.
Include approved testimonials, examples and measurable outcomes alongside the products and capabilities they support. Link to complete case studies for additional context. This also places customer evidence alongside the claims and features buyers are evaluating.Preserve context when results are extracted.
Include enough context for a retrieved fact to be understood accurately. For example, a 30 percent improvement should identify what was measured, the timeframe, customer circumstances and deployment scope.Support meaningful comparisons.
Identify industries, use cases, customer requirements and other factors that help buyers assess whether reported results apply to their own circumstances.Expand the evidence available.
Identify industries, use cases and outcomes with limited customer proof, then prioritize new stories to fill those gaps. Use approved, documented anonymous results when customers cannot be named.
5. Pay closer attention to professional communities
LinkedIn posts, Reddit discussions, industry forums and other professional conversations can become sources for AI-generated vendor assessments, even when buyers don’t visit those platforms.
What to do:
Listen to relevant conversations.
Identify where practitioners discuss your category, which questions and frustrations arise repeatedly, and how those communities expect vendors to participate.Examine what AI cites.
Review community posts and discussions appearing in AI answers about your company, competitors and offerings. Identify recurring recommendations, complaints and misconceptions that could shape buyer perceptions.Put your experts’ knowledge to work.
Encourage knowledgeable employees to publish original insights, answer questions and explain specific challenges, solutions and outcomes. Provide practical guidance and disclose company affiliations.Engage constructively with criticism.
Address legitimate concerns, acknowledge limitations and offer factual clarification where discussions contain outdated or incorrect information.Respect community rules and participate authentically.
Reddit’s rules and spam policies, for example, prohibit deceptive or manipulative activity. The goal is to contribute credible expertise and authentic perspectives to the conversations that may inform AI-generated vendor assessments.
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.”