How to get cited in AI Overviews and ChatGPT

What if your content ranks on page one of Google but never shows up when someone asks ChatGPT or gets an AI Overview? You’re not alone. Many B2B companies are watching their competitors get cited in AI-generated answers while their own content sits untouched. At Radyant, we’ve spent the last two years testing what actually earns AI citations, and the answer isn’t what most “GEO experts” are selling. Here’s the system that took one client from 55% citation share to over 130%, without a single outreach email.

Key takeaways

• AI citation dominance starts with owned content authority. You don’t need Reddit mentions or backlink campaigns as Plan A. If your content is genuinely the best answer, every AI platform will cite it. We proved this with Planeco Building: 5x organic leads and citation share above 130%, all from owned content alone.
• Ranking #1 on Google no longer guarantees AI citations. Only 38% of AI Overview citations now come from top-10 pages (down from 76% a year ago), and roughly 90% of ChatGPT citations come from pages ranked position 21 or lower. Topical depth across sub-queries matters more than any single ranking.
• What gets cited is specific and measurable: definitive language, original data, answer capsules in the first 30% of content, and high entity density. Pages with original data tables earn 4.1x more AI citations.
• Most companies can’t see the business impact of AI Search because attribution is broken. AI-referred visitors convert 4.4x better than traditional organic, but this shows up as “Direct” in your CRM. You need a 3-layer attribution model to capture the real picture.

Looking for a shortcut to drive more organic growth from your content, SEO & AI Search efforts? Request a free growth audit from Radyant to get an honest assessment of your organic growth potential.

The ground has shifted: what the latest data says about AI citations

Before diving into the system, you need to understand three structural changes that happened in the last 6 months. They fundamentally alter what “optimizing for AI search” means.

AI Overviews are pulling from different sources than Google Search

Ahrefs’ March 2026 study found that only 38% of AI Overview citations come from pages ranking in Google’s top 10 for the same query. A year earlier, that number was 76%. This isn’t a minor fluctuation. It’s a structural shift.

The mechanism behind this is Google’s query fan-out process. When an AI Overview is triggered, Google splits your original query into multiple related sub-queries, retrieves results for each, and then selects the pages that appear most frequently across those sub-query SERPs. Pages that rank for these fan-out queries see 161% higher citation odds.

What this means practically: ranking #1 for your primary keyword is no longer sufficient. You need topical coverage across the sub-queries that AI systems expand your keyword into.

Click-through rates are collapsing, but conversion quality is rising

AI Overviews now correlate with a 58% lower average CTR for the top-ranking page (up from 34.5% in April 2025). For Google’s AI Mode, around 93% of searches end without a click.

But here’s the number that matters more: AI search visitors convert 4.4x better than traditional organic visitors. Fewer clicks, but dramatically higher quality. Across our B2B SaaS clients, we’re seeing AI-attributed trials converting at 14.3% compared to an 11% channel average. The traffic you do get from AI citations is worth significantly more.

Each AI platform cites differently, and overlap is minimal

Only 11% of domains are cited by both ChatGPT and Perplexity. AI Mode and AI Overviews reach 86% semantic similarity in their answers while sharing only 13.7% of their cited sources. These aren’t variations of the same system. They’re different ecosystems with different source preferences.

Here’s a breakdown based on Profound’s analysis of 680 million citations and Ahrefs’ research:

• AI Overviews: Blogs are the top source type (~39%). YouTube is the most-cited domain. 38% overlap with top-10 rankings.
• ChatGPT: Wikipedia leads at 7.8% of citations. ~90% of citations come from pages ranked position 21+. Averages 3.86 citations per response.
• Perplexity: Reddit leads at 6.6%. Averages 7.42 citations per response. Vendor blog citation rate around 7%.
• AI Mode: Wikipedia and Quora are prominent. Only 14% citation overlap with AI Overviews despite similar answer content.

The implication: there is no single “AI optimization” playbook. But there is a unifying principle that works across all of them. More on that below.

What actually gets cited: the evidence from 1.2 million citations

Kevin Indig’s analysis of 1.2 million verified ChatGPT citations is the most rigorous empirical study on this topic. Combined with the Princeton GEO study and Ahrefs’ ongoing research, a clear picture emerges of what AI models actually pay attention to.

The “ski ramp” pattern: your intro matters disproportionately

44.2% of all ChatGPT citations come from the first 30% of content. Indig calls this the “ski ramp” pattern: attention is highest at the top and drops off sharply. But it’s not just about placement. ChatGPT seeks the sentence with the highest “information gain” in each section, meaning the most complete use of relevant entities and additive, expansive information.

This doesn’t mean you should front-load keywords. It means your opening sections need to contain your most definitive, data-rich, entity-dense statements. The rest of the content still matters for topical depth, but the intro is where citation battles are won.

Five content characteristics that predict citation

Based on the combined research, cited pages consistently share these traits:

• Definitive language. Citation winners are almost 2x more likely (36.2% vs. 20.2%) to use phrases like “is defined as” or “refers to.” AI models prefer statements that commit to a position rather than hedging.
• Original data. Pages with original data tables earn 4.1x more AI citations. Adding statistics boosts citation performance by 5.5%. The Princeton GEO study confirmed that Statistics Addition was one of the two strongest optimization methods.
• Answer capsules. 72.4% of pages cited by ChatGPT included an identifiable answer capsule, a concise 40-60 word direct answer near the top of a section. This was the single strongest commonality among cited posts.
• High entity density. More named entities (people, companies, products, concepts) per paragraph correlates with higher citation rates. AI models use entity recognition to assess information richness.
• Balanced fact-opinion mix with simple structure. AI-cited articles cover 62% more facts than non-cited ones, while maintaining a mix of factual statements and informed opinions. Simple sentence structures outperform complex ones.

What the Princeton GEO study actually proved

The Princeton GEO study tested multiple optimization strategies and found that GEO methods can boost visibility by up to 40% in generative engine responses. Two findings stand out:

• Keyword stuffing underperforms baseline by 10%. Traditional SEO tactics don’t transfer. AI models detect and penalize forced keywords.
• The best combination (Fluency Optimization + Statistics Addition) outperforms any single strategy by 5.5%. And adding source citations to content boosted performance by an average of 31.4% when combined with other methods.

The takeaway: making content clearer, more data-rich, and better sourced works. Trying to game the system with keyword density does not.

The owned content authority system: how we build citation dominance

Most guides on this topic lead with “get mentioned on Reddit and G2” or “build backlinks to boost authority.” That’s not wrong, but it’s not Plan A. It’s a bonus.

Here’s what we’ve seen work repeatedly: if your owned content is genuinely the best, most comprehensive, most authoritative answer to your audience’s questions, every AI platform will cite it. They all want the same thing. Serve the most helpful content. Optimize for that, and you’re building something sustainable.

We proved this with Planeco Building: 5x organic leads in 10 months, citation share from 55% to over 130% (meaning the content was cited multiple times per AI response), and #1 AI search visibility in the competitor set. No outreach campaigns. No Reddit commenting. No backlink chasing. Just owned content that was undeniably the best resource on the topic.

Here’s the system behind it.

Step 1: Start with audience research, not keyword research

Most SEO workflows start with a keyword tool. We start with the audience. What do they actually need to know? What questions are they asking sales teams? What confuses them about the buying process?

In the Planeco programmatic case, most targeted keywords showed “0 search volume” in Semrush. We ignored that on purpose because we knew from customer conversations that people were researching these topics. Result: 2,000+ net new clicks and 60+ leads in under 6 months from keywords that tools said nobody searched for.

Audience intel beats keyword data because it captures demand that tools can’t measure, and AI platforms are increasingly answering questions that never show up in traditional keyword databases.

Step 2: Extract expert knowledge that AI can’t find elsewhere

This is the moat. Generic content farms can produce “what is X” articles at scale. What they can’t produce is deep regulatory knowledge from a co-founder with 15 years of industry experience, or a nuanced comparison of two approaches based on real implementation data.

For Planeco, we conducted regular interviews with the co-founders to extract regulatory knowledge that created legally accurate content no AI tool could generate on its own. This is the content that AI must cite because the information exists nowhere else on the internet.

The practical framework: schedule 30-60 minute interviews with your subject matter experts, focused on specific topics. Record, transcribe, extract the unique insights, and build content around those insights. This is what we mean by “content engineering” rather than “content creation.”

Our Masters of Search episode with Ethan Smith from Graphite goes deeper into the mechanics of how AI platforms select which brands and content to cite.

Step 3: Build depth-first content, not surface-level guides

Our content standard: if someone wouldn’t reasonably pay for the information, it’s not good enough. Every piece should include comparison tables, step-by-step processes, requirement matrices, and structured FAQs where they add value.

For Planeco, this meant building pages with:

• Detailed comparison tables of building materials with specific cost ranges, regulatory requirements, and use cases
• Flowcharts for decision-making processes (e.g., “which insulation type for which building scenario”)
• Regional regulatory breakdowns that no competitor had compiled
• FAQ sections addressing the specific questions customers asked in sales calls

This depth creates content that AI models can extract specific, factual answers from. When ChatGPT needs to answer “what’s the best insulation for a pre-1960s building in Germany,” it cites the page that actually answers that question with specifics, not the one that gives a generic overview of insulation types.

Step 4: Structure for AI extraction

Once the content depth exists, structure it so AI models can easily find and extract the right information:

• Question-based H2/H3 headings that match how people ask questions in AI tools
• Answer capsules (40-60 words) immediately under each heading, giving a direct, definitive answer before expanding
• Definitive language in key statements (“X is defined as…” rather than “X could potentially be described as…”)
• Data tables and comparison matrices that AI can parse and reference
• Schema markup (FAQ, HowTo, Article) to help AI crawlers understand content structure
• Source citations within your content when referencing external data (the Princeton study showed this boosts citation performance by 31.4% when combined with other methods)

A critical nuance: these structural elements work not because AI has special formatting preferences, but because they make content more genuinely useful. We’ve been using question-based headings and key takeaways for 5+ years, long before anyone called it “GEO.” These “tactics” work in AI search because they worked for users first. As Andy Muns from Telnyx put it on our podcast: AEO might just be SEO.

Step 5: Cover the fan-out queries, not just the head term

Given the fan-out query shift (38% overlap, down from 76%), your content strategy needs to address the sub-queries that AI systems expand your primary keyword into.

Here’s how to identify them:

• Use “People Also Ask” as a starting point. These frequently overlap with the sub-queries Google generates during fan-out.
• Check related searches and autocomplete suggestions for your primary keyword. These map to the query expansion patterns.
• Analyze the “Sources” section of existing AI Overviews for your target queries. The pages cited there are ranking for the fan-out queries.
• Build topical clusters where a pillar page covers the primary topic comprehensively, and supporting pages address specific sub-topics in depth. This creates multiple entry points for fan-out queries to find your content.

The Planeco programmatic case is a good example of this at scale: 247 pages live in 7 days, each targeting a specific sub-topic within the broader category. 140 of those pages ranked Top 3 within 72 hours. This kind of topical breadth is exactly what fan-out query expansion rewards.

Step 6: Measure, attribute, and iterate

This is where most guides stop. “Get cited” is treated as the end goal. But citation without pipeline impact is a vanity metric. The next section covers how to actually measure what matters.

How to measure AI citation impact (the part everyone skips)

Here’s the attribution problem: when someone asks ChatGPT for a recommendation, gets your brand mentioned, then types your brand name into Google, that shows up as “Direct” or “Organic” in HubSpot, GA4, or whatever you use. Never as “ChatGPT recommended us.”

Most marketing execs still make budget decisions based on this incomplete data. That’s why you need a 3-layer attribution model.

Layer 1: Click-based attribution

Your CRM and analytics data. Track referral traffic from chat.openai.com, perplexity.ai, and other AI platforms in GA4. Set up UTM parameters where possible. This captures the clicks that AI platforms do send, but it’s increasingly unreliable for discovery. Keep it, but stop treating it as the only truth.

Layer 2: Self-reported attribution

Add a “How did you hear about us?” field on your forms. Best practice: make it mandatory and free-text (not a dropdown). LLMs now make analyzing free-text responses trivial. You’ll start seeing answers like “ChatGPT recommended you” or “I saw you in an AI search result” that would otherwise be invisible.

Layer 3: Verbal attribution from sales

What prospects actually say in calls. “I asked ChatGPT for the best X and your name came up.” Most of this intel dies in the call unless you create a custom CRM field that sales fills in after every discovery call.

When we implemented this for Heyflow, we found AI-attributed trials converting at 14.3% compared to an 11% channel average. That insight was invisible in click-based analytics alone.

For citation tracking specifically, tools like Otterly, Peec AI, and Profound can monitor how often your brand and content get cited across AI platforms. But remember: 40-60% of AI citations change monthly. A single snapshot is misleading. You need ongoing monitoring.

The YouTube opportunity most companies are ignoring

YouTube is now the most-cited domain in AI Overviews. Among AI Overview citations that didn’t rank in Google’s top 100 for the same keyword, 18.2% were YouTube URLs. And Ahrefs’ research on 75,000 brands found that YouTube mentions are the strongest correlating factor with AI Overview visibility across all signals studied.

This validates something we’ve been saying for a while: YouTube will be more important for AI search than Reddit. YouTube is a controlled third-party property. You don’t own the domain, but you control what goes on it. Video titles and descriptions offer plenty of room for optimization. And unlike Reddit spam, optimizing video metadata for better discoverability aligns with what YouTube wants.

We’re currently running a YouTube AEO experiment with Heyflow to validate the impact. Early results are promising, but it’s still an experiment. What’s not experimental is the data: if you’re not creating video content optimized for AI citation, you’re leaving the single strongest correlation signal on the table.

Want to see how your content stacks up for AI citation readiness? Our growth strategy audit includes a citation share analysis against your competitors.

What doesn’t work (the anti-patterns)

The “AI optimization” space is filling up with tactics that either don’t work or actively hurt your chances. Here’s what to avoid:

• Keyword stuffing for AI. The Princeton GEO study found it underperforms baseline content by 10% on Perplexity. AI models detect and penalize forced keywords just like Google does.
• “Prompt-style writing” hacks. Writing content in a Q&A format that mimics ChatGPT prompts is just repackaged basic UX. It works because clear questions and answers have always been useful, not because AI has a special preference for prompt formatting.
• Separate “AI-optimized” content versions. If you need a separate version for AI, your original content isn’t good enough. Fix the original.
• Reddit and forum spam. Dropping brand names in unrelated threads is detectable and counterproductive. If external presence is part of your strategy, it must provide genuine value.
• Generic programmatic content with city names swapped in. Google and AI search can spot this from miles away. Programmatic content works when each page adds unique, specific value. Our Planeco programmatic case succeeded because every page contained region-specific regulatory information, not because we templated the same content 247 times.
• Treating citation frequency as the end goal. Chegg’s stock dropped 49% after ChatGPT absorbed its homework-help content. Their commoditized, text-only answers were easily replicated by AI with no need for citation. Being cited today means nothing if your content can be fully absorbed and paraphrased tomorrow.

The common thread: anything that tries to game AI platforms rather than genuinely improve content quality will fail. ChatGPT mentions brands 3.2x more often than it cites them with links. Getting mentioned is easy. Getting cited with a link requires content that AI can’t fully paraphrase because it contains unique data, frameworks, or expertise.

Putting it all together: a content audit checklist

Use this to evaluate whether your existing pages are “citation-ready.” Score each page against these criteria:

• Does the first 30% contain your most definitive, data-rich statements? (The ski ramp pattern means this is where 44% of citations come from)
• Does every major section open with a 40-60 word answer capsule? (72.4% of cited pages include these)
• Does the content use definitive language? (“X is defined as…” not “X could be considered…”)
• Does it contain original data, proprietary research, or expert insights unavailable elsewhere? (Pages with original data tables earn 4.1x more citations)
• Are headings structured as questions that match how people ask AI tools?
• Does it cover the fan-out sub-queries, not just the primary keyword?
• Does it include comparison tables, data tables, or structured frameworks that AI can parse?
• Is the content deep enough that someone would reasonably pay for the information?
• Does it cite its own sources with links? (Boosts citation performance by 31.4% when combined with other methods)
• Is there relevant schema markup (FAQ, HowTo, Article)?

If a page scores below 7 out of 10, it’s not citation-ready. Prioritize updating your highest-traffic pages first, since those have the most to gain (and lose) from AI citation shifts.

FAQ

Do I need to rank #1 on Google to get cited in AI Overviews?

No. Only 38% of AI Overview citations come from top-10 pages, and roughly 90% of ChatGPT citations come from pages ranked position 21 or lower. What matters more is covering the sub-queries that AI systems expand your primary keyword into (the fan-out effect) and having content with high information density that AI models can extract specific answers from.

Is GEO/AEO a separate discipline from SEO?

No. In our experience, GEO/AEO done right is simply good SEO with better attribution. The “tactics” that work for AI citation (clear headings, answer-first formatting, original data, comprehensive depth) are the same things that have always made content useful for humans. The difference is in measurement: you now need to track citation share and use a multi-layer attribution model to see the full impact. We covered this in depth on our podcast with Andy Muns, Director of AEO at Telnyx.

Which AI platform should I prioritize?

Start with whichever platform your audience uses most. For B2B SaaS, 73% of B2B buyers now use AI tools in their research process. ChatGPT processes 3+ billion prompts monthly with 53.5% commercial intent. But since only 11% of domains are cited by both ChatGPT and Perplexity, a depth-first content strategy (being the best answer) is the most efficient approach because it works across all platforms simultaneously.

How long before I see results from AI citation optimization?

It depends on your starting point. With Planeco Building, we saw citation share move from 55% to over 130% within 10 months, with early signals within 3-4 months. For programmatic content, rankings can appear within 72 hours. But AI citations are volatile: 40-60% of citations change monthly. This isn’t a “set and forget” effort. You need an ongoing system of content creation, measurement, and iteration.

Can owned content authority really replace external mentions?

Yes, as Plan A. The Planeco case proves it. That said, external mentions aren’t worthless. They’re a bonus that can accelerate results. But if you’re choosing where to invest first, invest in making your owned content the definitive answer. Seer Interactive’s analysis found that traditional SEO authority (backlinks, rankings) showed little correlation with brand mentions in AI answers. What correlated was content depth and information quality.

Should I create video content specifically for AI citation?

The data says yes. YouTube is now the most-cited domain in AI Overviews, and YouTube mentions are the strongest correlating factor with AI Overview visibility. This doesn’t mean producing low-effort videos. It means creating genuinely useful video content with optimized titles, descriptions, and transcripts that AI models can parse. We’re running a YouTube AEO experiment with Heyflow right now, and we’ll share results on the Masters of Search podcast as they come in.