The AI SEO advantage is engineering content for citation across ChatGPT, AI Overviews, and traditional search simultaneously – while competitors still chase rankings that don’t convert.
Your #1 ranking doesn’t mean what it used to.
While you’re celebrating top positions in Google, your potential customers are getting their answers from ChatGPT, Perplexity, and AI Overviews – and your content isn’t part of the conversation. The data is stark: 77% of US ChatGPT users now treat it as their primary search engine, and AI Overviews dominate 74% of problem-solving searches.
It’s easy to see why. AI search offers tailored answers to specific questions – like asking a friend for a recommendation or their specific experience.
This means companies need to reframe the purpose of their content. Traditional SEO focused on one question: “How do I rank #1?” AI SEO asks something different: “How do I become the source AI systems cite when answering buying questions?”
To help you understand this nuance, this article breaks down both sides of modern SEO – optimizing FOR AI platforms to capture citations, and using AI tools WITH strategy to create content that machines trust and humans value.
How AI SEO differs from traditional SEO
As cliché as it sounds, traditional SEO was a numbers game. Find keywords with decent volume and low competition. Write content targeting those keywords. Build backlinks. Wait for Google to crawl your site. Scream into the void when algorithm updates tanked your rankings. Rinse and repeat.
That playbook is obsolete.
AI SEO changes the entire question. Instead of “How do I rank for this keyword?” you’re asking “How do I become the source AI cites when answering this question?”
The mechanism is fundamentally different. Traditional search gave you 10 blue links and made users figure out which one answered their question. AI search – whether that’s in the form of Google’s AI Overviews or ChatGPT – does that work for the user. It synthesizes information from multiple sources and delivers a direct answer.
Here’s what that means for you:
| Traditional SEO focus | AI SEO focus |
| Keyword density and exact match Page-level rankings Click-through rates Backlink quantity | Semantic relevance and intent Passage-level selection Citation probability Information quality |
The AI answer engine is smarter than legacy Google ever was. It can understand nuanced questions, compare multiple sources, and evaluate which content actually answers the question with authority. You can’t game this with keyword stuffing or link schemes.
Your content either deserves to be cited or it doesn’t.
Proof that AI search is taking over fast
The numbers don’t lie. AI search has moved from an experimental feature to a dominant force faster than any previous search evolution.
Current state of AI search:
- Pew Research Center reported in March 2025 that 18% of all Google searches now display AI Overviews. In reality, our own findings show, as of October 2025, that AIOs are triggered in at least 60% of searches in most markets, and we’ve even seen this as high as 90% of searches.
- 74% of problem-solving searches and 69% of question-based queries trigger AI Overviews.
- 77% of US ChatGPT users now use it as their primary search engine.
And probably the most painful fact for legacy SEO fans: ranking #1 doesn’t guarantee AI citation.
This is because around 70% of AI Overview sources change within 2-3 months. Your top organic ranking might get completely ignored while a page ranking #7 gets cited because it better matches the AI’s reasoning chain.
Even more striking, there’s a very small overlap between what ChatGPT cites and what ranks in Google’s traditional results. The platforms are pulling from completely different sources.
This means you’re not competing on one battlefield anymore. You’re competing on several simultaneously.
And if you’re only optimizing for traditional rankings, you’re invisible where most of your buyers are actually searching.
Understanding different AI optimization targets: AIOs, LLMs, and AI Mode
Google AI Overviews
AI Overviews synthesize information from multiple sources to create ONE comprehensive answer at the top of Google’s search results. Getting cited here requires writing for machine comprehension, not just human readers.
As you can see, the first sentence in our example AI Overview (How not to kill plants) compiles the most important information in relation to the main question. Then, the AIO recommends a video with tips and provides additional detail on the factors listed above (similar to how an LLM would). Each piece of information has a source, and that source list is where you want to be.
The structure AI systems prefer
- Start every section with atomic definitions. Use the pattern “X is Y that does Z.” One sentence, one complete thought.
- Strip out marketing fluff. Google’s AI ignores words like “revolutionary,” “game-changing,” and “cutting-edge.” It keeps technical details, facts, and specific claims that it can verify against other sources.
- Format your content in predictable patterns:
- Definition (what it is)
- Features (what it does)
- Benefits (why it matters)
- How to Use (practical application)
This doesn’t mean you should dumb down your content – just make your expertise extractable. Meaning, AI systems need to pull clean passages that stand alone without surrounding context.
Research shows that 82.5% of AI Overview citations link to deeply nested pages, not homepages. Your detailed guides and technical documentation have better citation odds than surface-level category pages.
What actually gets cited
Pages that answer specific questions in FAQ format consistently outperform generic content. Use schema markup (FAQPage and HowTo) to help Google’s systems understand your content structure. Besides that, fundamental things like fast page speed and mobile optimization also have a big impact – if Google’s crawler struggles with your site, you’re invisible to AI systems.
And as we mentioned before, ranking #1 doesn’t guarantee citations. A page ranking #7 might get cited while your top position gets ignored – if the lower-ranking page better matches the AI’s reasoning chain.
ChatGPT and Similar LLM Platforms
ChatGPT operates fundamentally differently from Google. It doesn’t just scan the top 10 search results and call it done.
If I ask the same question, the LLM will generate a list of facts and advice it deems relevant to my query. Informationally, it’s similar to the AIO because it gives information about lighting, watering, and bonus tips. The difference here is that it also shares hard-to-kill plants and then asks whether I want a “personalized ‘don’t kill these’ care plan.”
The other difference you’ll notice is that it doesn’t share sources. However, that doesn’t mean that it’s not pulling that information from somewhere. So where are these answers coming from?
According to studies, it’s a very mixed bag:
- Wikipedia accounts for 43% of ChatGPT citations for general queries.
- Reddit dominates with 15% of commerce-related citations.
- Academic papers, business directories, LinkedIn articles, and forum discussions all contribute.
- YouTube appears in 5% of informational citations.
This means your on-site content is only part of the equation. You need authority signals across the entire web – not just backlinks to your site, but brand mentions in discussions, reviews on third-party platforms, and contributions to community forums where your ICP spends time.
How query fanout changes everything
When someone asks ChatGPT a complex question, it breaks that query into dozens of related sub-queries. Each sub-query pulls from different sources.
This is called “query fanout” – one main topic fractures into multiple search threads addressing different angles, user intents, and expertise levels.
Instead of just asking about general plant caring tips, now we want to know how to take care of a herb garden if we travel frequently. This makes the answer much more complicated, and now it also includes product recommendations: Smart garden systems, self-watering planters, and LED grow lights with a timer.
It transforms from simple informational queries to product recommendations in an instant.
For you, this means one blog post isn’t enough. You need comprehensive coverage of a topic from multiple angles:
- Beginner explainers.
- Technical deep-dives.
- Product listicles.
- Use case comparisons.
- Implementation guides.
- Troubleshooting content.
Each piece targets different sub-queries within the broader topic. This is why, at Ellipsis, we see clients with extensive topic clusters consistently outperform those publishing isolated articles.
Google AI Mode
AI Mode represents Google’s answer to ChatGPT – a conversational search experience that handles multi-turn dialogues. If we go back to our query about not killing plants, at the bottom of it, there’s the option to “Dive deeper in AI Mode.”
What this looks like is a mix of AI mode and an LLM chat: You still get the tailored answers and option to continue the conversation, but you also have all the sources listed on the side.
Similar to chatbots, AI Mode understands context from previous questions in the conversation. Users don’t need to repeat information – they can ask follow-up questions assuming the AI remembers what they discussed.
In all of these examples, we can reach the same conclusions. Traditional SEO is optimized for isolated queries. AI Mode requires anticipating conversation flows.
For example, if someone asks, “What is WordPress hosting?” their next question might be “How does it compare to regular hosting?” followed by “Which providers are best for small businesses?”
Your content needs to address these conversational progressions, not just answer single questions. Structure your guides to flow naturally through the questions users actually ask in sequence.
Can SEO be done by AI? The role of automation in modern SEO
Let’s get one thing out of the way. The question isn’t whether AI can do SEO because it absolutely can automate huge chunks of it.
The real question is: Which parts should you automate, and which require human judgment?
At Ellipsis, we’ve solved this through deliberate division of labor. AI handles the repetitive, data-heavy research that used to consume hours of human time. Our strategists focus on interpretation, competitive positioning, and understanding the nuanced intent behind search behavior.
This isn’t about replacing humans with machines. It’s about letting each do what they’re best at.
How we automate keyword research at Ellipsis
Our strategy team uses Goose – an open-source AI agent from Block that runs locally on our machines. It connects to DataForSEO’s Model Context Protocol (MCP) server, giving Claude direct access to real-time SEO data.
Here’s what this enables:
We prompt the AI with specific parameters: “Find 10 keywords for [product] with 1,000-5,000 monthly volume, trending upward over the last 90 days, difficulty score under 40.“
Claude generates contextually relevant keyword ideas based on the product, market positioning, and ICP. Then – without any manual intervention – it immediately queries DataForSEO’s API to validate each suggestion with real performance metrics.
Within seconds, we get a complete list with:
- Search volume trends.
- Keyword difficulty scores.
- Competitive landscape data.
- SERP feature presence.
- Intent classification.
Tasks that took 2-3 hours now take minutes. No spreadsheet juggling. No copy-pasting between tools. No manual cross-referencing.
Why Model Context Protocol matters
In simple words, MCP is the bridge that makes this workflow possible.
Traditionally, connecting AI systems to SEO APIs required weeks of custom development work. You’d need engineers to build integrations, handle authentication, manage rate limits, and structure data formats that AI models could understand.
MCP provides a standardized connection that works out of the box. It’s an open protocol (developed by Anthropic) that lets AI models communicate directly with external data sources through a common interface.
What this means in practice:
Claude gets direct access to SEO data instead of requiring manual copy-paste between platforms. The AI can formulate hypotheses, test them against real data, refine its approach based on results, and present validated insights – all in one continuous conversation.
It’s the difference between asking someone to “check if this keyword is good” and having them actually pull the data, analyze competitors, identify gaps, and recommend the best approach.
The real impact: More time for strategy
This automation fundamentally changes how we allocate expertise.
Before: Our strategists spent hours on data validation:
- Pulling keyword metrics from multiple tools.
- Cross-referencing search volume across platforms.
- Manually checking SERP features and AI Overview presence.
- Building comparison spreadsheets for competitive analysis.
Now: That research happens automatically in the background.
Our strategists reinvest that time into work that actually requires human intelligence:
- Deep competitive analysis that considers market positioning.
- Content strategy that accounts for brand voice and differentiation.
- Understanding the nuanced intent behind why people search in certain ways.
- Identifying opportunities that don’t show up in keyword tools.
“AI handles pattern recognition at scale so that we, the humans, have more time to handle strategic interpretation and creative execution. We’re not doing less work – we’re doing higher-value work that machines can’t replicate. The kind that directly impacts whether content gets cited by AI platforms or ignored.”
– James Baldacchino, Head of SEO and Strategy at Ellipsis
This is the future of SEO. Not agencies choosing between AI and humans, but agencies that know exactly which tasks belong to which.
Practical AI tips for resource-constrained content teams
Start with AI-assisted research, not AI-generated content.
Most teams are using AI backwards. They’re asking it to write entire articles from scratch, then wondering why the output reads like every other generic piece on the internet.
Here’s a better approach:
“I’ve analyzed 500 customer service tickets about tire problems. Help me identify which ones share root causes and common failure patterns.“
This takes advantage of AI’s pattern recognition – what it’s genuinely good at – while preserving your actual expertise about tire mechanics, safety implications, and customer concerns that the AI has no context for.
The “sandwich” approach to content creation
Think of your content like a sandwich: Human expertise on top and bottom, AI filling in the middle.
- Start with your core argument. Write the opening that establishes your unique perspective and the conclusion that drives your authority. These sections must be 100% human-written because they’re what separates expert analysis from commodity content (try counting how many articles start with “In today’s digital world…”).
- Use AI for supporting elements. Feed it your established principles: “Based on this framework I’ve explained, generate 3 real-world applications with specific metrics.“
- Return to human writing for any claims that build authority – case study insights, proprietary data interpretation, strategic recommendations.
This preserves E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) while using AI for its actual strength: Generating variations based on parameters you define.
Smart content brief development
Don’t ask AI to create briefs from nothing. Use it to map the competitive landscape:
“Extract the specific claims, evidence types, and questions answered in these top-ranking articles. What patterns do you see? Is there something missing that we can answer?“
AI shows you the territory. You decide where to stake your claim.
Then add your expertise layer:
- Which gaps can you uniquely fill?
- What original data do you have access to?
- How does your perspective differ from existing content?
Quality control: The expertise test
Before publishing anything AI-touched, ask one question:
“Could someone with zero domain knowledge create this using the same AI tools?“
If yes, you’ve created slop. AI should amplify your expertise, not replace it.
The content worth citing – whether by humans or AI systems – always contains insights that only come from real experience. Pattern recognition can’t manufacture that.
How FALCON tracks AI search performance
Despite how it may look, the hardest part of AI SEO isn’t optimization – it’s proving it works.
Google Search Console shows zero data about AI Overview citations. ChatGPT doesn’t report which brands it recommends. You’re flying blind unless you build your own tracking infrastructure.
FALCON monitors two key metrics across ChatGPT and Google AI Overviews:
- Citations: How often AI systems use your content as a source when generating answers. This measures whether your expertise is being referenced in AI-generated responses.
- Mentions: How often AI recommends your brand by name, even when not directly citing your content. This captures brand authority separate from specific page citations.
We track these metrics for you and your competitors across 1,000+ AI queries relevant to your ICP. You always know where you stand relative to the market.
Share of Voice: Why relative performance matters
Our newest feature shows what percentage of AI recommendations go to you versus competitors.
This matters because raw citation counts lie.
Everyone’s citations are growing as AI search expands. If you gained 100 citations last month, that sounds impressive – until you learn your main competitor gained 300.
Share of Voice reveals the truth: Are you capturing market share or losing ground?
Example: You gain 100 citations while competitors only gain 20 each. Your absolute numbers grew modestly, but your share of voice increased dramatically – you’re winning.
The opposite is equally important to catch early. If your citations grew 50 but competitors grew 200 each, you’re falling behind despite positive absolute growth.
This tells you if your optimization is actually winning, not just participating.
Unlock Your AI SEO Advantage with Ellipsis
Most SEO agencies are still living in 2020.
They’re measuring success through rankings that no longer correlate with revenue. They’re celebrating position one while your buyers get their answers from ChatGPT. They don’t track AI citations because they don’t know how – and they’re hoping you won’t notice the gap.
Your competitors probably don’t even know how often they appear in ChatGPT responses, let alone optimize for it.
This creates a massive opportunity window. While the market figures out that AI search exists, you can dominate the citations that drive buying decisions.
Ellipsis’s FALCON technology provides real data about which queries are relevant to your ICP and which queries you’re actually performing for. Other solutions are guessing based on generic keyword lists. We track the actual AI queries your buyers ask when they’re 50 messages deep into a ChatGPT conversation.
The approach optimizes for traditional search AND AI platforms simultaneously. You don’t choose between ranking or getting cited – you do both.
Some of our recent client outcomes include:
- 21% increases in ChatGPT citations within days of optimization.
- 100% success rates for getting into AI Overviews on targeted content.
- 245% AI citation growth in 90 days for enterprise hosting clients.
By working with Ellipsis, you can:
- Be cited when competitors are ignored. While they chase rankings that don’t convert, you capture the AI citations that drive your pipeline.
- Capture demand across a fragmented search landscape. One strategy that works across Google, ChatGPT, Perplexity, and whatever platform launches next month.
- Build authority that transcends individual ranking factors. When AI systems trust your brand, algorithm updates become noise instead of threats.
- Future-proof your digital presence for AI-driven discovery. You’re not adapting to a trend – you’re positioning for the next decade of search.
See exactly where you currently perform across traditional rankings, AI Overviews, and ChatGPT citations. Get your AI audit and discover which high-intent queries your competitors dominate – and how to take them back.
