As AI becomes a primary way people research products and services, a new kind of brand risk is quietly emerging, one most companies aren’t actively monitoring.
Recent data shows that Google’s AI Overviews are 44% more likely to criticize brands than ChatGPT. At first glance, that may seem like a sentiment issue. In reality, it points to something deeper: AI systems are not just surfacing information, they’re shaping how brands are described at the exact moment decisions are being formed.
And in many cases, that narrative is built without the brand’s input.
“Each platform is a separate system that happens to share a category name with the others,” says Shane H. Tepper, cofounder of Resonate Labs, a company that helps B2B businesses be found and cited in AI search models like ChatGPT, Perplexity and Gemini.
That distinction matters more than it seems.
One Brand, Multiple Narratives
There’s a growing assumption that “AI search” is a single environment, something brands can optimize for the same way they’ve approached traditional search.
But that’s not how it works.
Different AI platforms pull from different sources, weigh credibility differently, and structure their responses in completely different ways. The result is that the same brand can appear consistently in one platform, barely show up in another, and be described differently across both.
Data backs this up. Studies from Ahrefs, Semrush, and Profound show minimal overlap between the sources cited by platforms like ChatGPT, Google AI Overviews, and Perplexity. In other words, most of the opportunities to be mentioned —or misrepresented— are platform-specific.
“The vast majority of citation opportunities are platform-specific,” Tepper explains.
That fragmentation doesn’t just affect visibility. It affects perception.
Why AI Can Get Your Brand Wrong
AI systems don’t “understand” your brand the way a human would. They assemble answers by pulling from available content, often prioritizing what is clear, specific, and easy to extract.
That means they frequently rely on:
- third-party websites
- community discussions
- outdated or incomplete information
If your own content is vague or overly polished, it may not even make it into the answer. Meanwhile, a competitor, a reviewer, or even a forum post with more specific language can shape how your brand is described.
In that environment, silence becomes a vulnerability.
If you’re not clearly stating what you do, how you compare, or where you stand, AI systems will fill in the gaps, using whatever sources are most accessible.
Fragmentation Makes the Problem Worse
The challenge isn’t just that AI can get things wrong. It’s that it can get them wrong in different ways depending on the platform.
Some systems favor short, direct answers. Others prioritize depth and multiple sources. Some lean on structured reference content, while others pull heavily from community-driven platforms like Reddit or YouTube.
“One platform is rewarding depth and breadth, the other is rewarding tight extractable claims,” Tepper says. “The same page can win on one and lose on the other for purely architectural reasons.”
This creates a situation where brands aren’t dealing with a single narrative risk, but many.
The Strategy Gap
Despite this complexity, many teams are still approaching AI visibility as if it were one channel with one strategy.
That assumption is starting to break. “The phrase itself is the first misconception,” Tepper says. “‘AI search optimization’ implies one channel with one set of rules. There is no such channel.”
In practice, this shows up in how companies allocate effort. A team might improve how they appear in ChatGPT and assume those gains will carry over to Google or Perplexity. But because each platform operates differently, those improvements often stay isolated.
The result is a fragmented presence that doesn’t reflect the brand consistently
Where This Becomes a Business Problem
This isn’t just a visibility issue, it’s a decision-making one.
AI systems are increasingly shaping shortlists, comparisons, and first impressions before a user ever visits a website. By the time someone clicks, much of the evaluation has already happened. Research shows that most buyers enter the process with a shortlist already forming, and AI is now influencing who makes that list, and how those options are framed.
If a brand is missing, misrepresented, or inconsistently described, it may never make it into consideration.
That’s what makes this shift different. It’s not just about showing up, it’s about showing up accurately, consistently, and in the right places.
“The platforms that matter are the ones where your specific buyers actually research,” Tepper says.
In practice, that means moving away from broad, one-size-fits-all content strategies and toward more precise work: answering specific questions, addressing real comparisons, and structuring information in ways AI systems can actually use. It also means paying closer attention to what’s being said beyond your own website, where third-party content, reviews, and community discussions increasingly shape how AI describes your brand.
Because AI isn’t just changing how people find information, it’s changing how brands are interpreted.
And in a fragmented landscape where each platform pulls from different sources and applies different logic, consistency becomes the real challenge.
“The brands that treat AI as a single channel will end up with content tuned for one platform at the expense of the others,” Tepper says.
In that environment, brand risk doesn’t just come from being absent. It comes from being described in ways you don’t control, differently, depending on where the question is asked.
And increasingly, that’s where decisions begin.
