TL;DR: Yext Research analyzed 17.2 million AI citations and found that AI search visibility depends on retrieval logic — not just content quality. Gemini is grounded in Google Search and often favors official websites, but ChatGPT relies on an external retrieval layer, with industry-specific variance. The takeaway? Structured first-party content, accurate listings, and reputation signals are not interchangeable — but they all matter.
If AI search still feels mysterious to you, you're not alone.
From the outside, we all see a (seemingly) simple interaction: a customer asks a question, and a polished answer appears — typically with sources cited. But the real question for marketers is: how do those sources get chosen?
Explained simply, when someone asks Gemini, Perplexity, ChatGPT, or Claude about your brand, the model doesn't pull from a stored profile. Instead, it gathers information in real time from across sources it trusts, selects which ones to cite, and then blends them into a single response.
And that retrieval logic — where the model looks and how it evaluates sources — shapes who gets seen.
To give marketers more clarity, Yext Research recently analyzed 17.2 million AI citations across industries. The data shows something critical: different AI systems rely on different types of sources to create their answers. This means visibility isn't just about content quality. It's about how each system retrieves and evaluates information.
Let's break down what we found, and what it means in practical terms.
Four AI models, four retrieval patterns
In the traditional search landscape we all know, marketers competed to get their brand website ranked on the first page of the SERP. In AI search, various models gather information from multiple sources and synthesize it to create an answer.
Here's how that process differs across Gemini, Perplexity, ChatGPT (Open AI), and Claude:
Gemini: Grounded in Google Search
Gemini is (unsurprisingly) heavily grounded in Google's search index. In practice, that means it behaves somewhat similarly to traditional search. It frequently cites official brand websites and established sources (and, um, Google itself).
If your brand has strong first-party content, structured local pages, robust Google Business Profile(s), and solid traditional SEO foundations, you're more likely to show up here.
Think of Gemini as inheriting much of Google Search's logic — including its preference for authoritative, well-structured content.
Perplexity: Search-first retrieval
Perplexity operates like a search engine that answers the question directly. Its citation patterns are consistent across industries, and it tends to pull from a mix of official websites and directories.
In our research, Perplexity demonstrated some of the most stable citation behavior across sectors. That stability suggests a tightly controlled retrieval process.
For brands, that means balanced visibility: strong websites matter, but so do accurate directory listings.
ChatGPT: Retrieval layer feeds the answer
Open AI's ChatGPT depends on an external retrieval system that can vary by industry, and that flexibility shows up in the data.
In Hospitality, for example, it cited official hotel websites 38.08% of the time — roughly double the rate of other models.
That kind of industry-specific spike suggests the retrieval layer is configured differently depending on context. For hospitality brands, that means your website may carry more weight in with Open AI than it does elsewhere. For other industries, the mix may look different.
Click here for more industry details in the full report.
Claude: Retrieval + constitutional evaluation
Of the four models, Claude is definitely the outlier. Across every sector studied, it cited user-generated content at 2–4 times the rate of other models. And in Food & Beverage, Claude cited user-generated sources nearly 10 times more often than Gemini.
Claude uses a framework often referred to as Constitutional AI, which appears to correlate with heavier reliance on reviews and user-validated content. This is a correlation, not a claim of causation — but the pattern is consistent.
For brands, the takeaway is pretty straightforward: reputation signals matter much more in Claude's ecosystem than in others.
Why these citation patterns change who gets seen
To recap, different systems favor different signals. If an AI model tends to favor:
Official websites → brands with strong first-party content benefit.
Directories → brands with accurate, claimed listings benefit.
Reviews and UGC → brands with strong reputation signals benefit.
In our dataset, we also found just how much different "longtail" publishers matter across the board. Websites generated 4.31 citations per URL… whereas listings represented 54.53% of distinct citation sources.
So, if the takeaway is that listings matter, but that first-party content also matters, but then reputation matters more sometimes… what's a brand marketer to do?
How to adjust your visibility strategy for this reality
To succeed in this landscape — and win with whatever model a customer turns to for answers — marketers must…
1. Stop assuming good website content is enough
Publishing strong website content may improve visibility in Gemini, for example. But if a model leans heavily on reviews or directories, your website alone won't carry you. So, your content strategy must be mapped to (and measured by) model behavior — not just keywords. Instead of asking: "What keywords do we rank for?" Ask: "Where does each model get evidence for answers in our category?" And instead of rank tracking, track:
How often is our brand mentioned in AI answers?
In what contexts and with which models?
Compared to which competitors?
Track changes over time, and make adjustments accordingly.
2. Treat listings as visibility infrastructure
With listings accounting for 54.53% of distinct citation sources, AI systems are constantly referencing directories and third-party platforms.
But if your listings are incomplete, inconsistent, or unclaimed, you're shrinking the number of surfaces where AI can find and verify your brand — and harming your visibility in the process. Your brand needs accurate and robust information across the highest number of third-party platforms possible.
Get started on building a great listings management strategy.
3. Understand that managing your reputation drives visibility
Claude's elevated reliance on user-generated content definitely changes the equation. Reviews aren't just about influencing customers (like they have been in the past). They influence whether your brand is cited at all.
So, reputation management needs to become part of your brand visibility strategy. If you don't have a system for monitoring, responding to, and soliciting reviews for each of your locations, it's time to change that.
4. Structure your content to improve clarity
When AI systems pull from official websites and local pages, they favor information that is clear and easy to verify.
That's because AI models don't necessarily interpret storytelling the way humans do. They want to extract (verifiable) facts. Clean structure, consistent entity signals, and accurate metadata on your pages make it easier for your brand to be cited correctly.
Click here to learn more about how to properly optimize your local pages for AI search.
Measure citations, not just rankings
Traditional SEO focused on where you rank. But optimizing for AI search is about whether you're cited.
If you're not tracking citation frequency and source mix — by model and by location — you don't have a clear view of your visibility.
That's the operational shift. AI outcomes aren't random. They're shaped by retrieval logic and source evaluation.
The marketers who understand how each system looks for information — and align their brand's websites, listings, and reputation signals accordingly — will have clarity, control, and confidence as AI search continues to evolve.
Click here to dive into the full findings in the Yext Research report.
