After 6 months of testing, I finally understand what gets content into Google AI Overviews.
The results:
| Approach | AI Overview Appearances | What Happened |
|---|---|---|
| Traditional SEO focus | 47 appearances | Best results |
| “AI-specific” tactics | 8 appearances | Mostly failed |
| Content restructuring | 31 appearances | Helpful addition |
| Schema optimization | 28 appearances | Good boost |
What WORKED:
What DIDN’T work:
The biggest insight:
Google AI Overviews heavily favor content that already ranks well. No amount of “AI optimization” overcomes poor SEO fundamentals.
Questions:
Want to compare notes on what’s actually driving AI Overview appearances.
Your findings match what Google explicitly states: there are NO special requirements beyond standard SEO.
Google’s official guidance:
“The same foundational SEO principles that help websites rank in traditional search results also apply to AI Overviews.”
The ranking systems that matter:
These are TRADITIONAL SEO signals, not AI-specific.
Why rankings predict AI Overview appearances:
AI Overviews pull from top-ranking results. If you rank #15, you’re rarely sourced.
| Traditional Rank | AI Overview Likelihood |
|---|---|
| #1-3 | High |
| #4-7 | Moderate |
| #8-10 | Low |
| Page 2+ | Very Low |
The implication:
Improve traditional rankings first. AI Overview appearances follow.
“AI SEO” that ignores fundamentals is just bad SEO with new branding.
While rankings matter most, content structure amplifies ranking impact.
Content structure that gets extracted:
H1: [Question-based title]
[Direct answer - 2-3 sentences that fully answer]
H2: [Supporting section]
[Expanded explanation]
H2: [Additional context]
[More detail with examples]
## FAQ
Q: [Related question]
A: [Direct answer]
Why structure matters:
AI Overviews extract specific passages. Structured content = cleaner extraction.
What we tested:
| Structure | Same Ranking | AI Overview Rate |
|---|---|---|
| Dense paragraphs | #3 | 12% |
| Clear headers + lists | #3 | 34% |
| FAQ format | #3 | 41% |
Same ranking, different extraction rate.
Rankings get you in the pool. Structure determines if you’re selected.
E-E-A-T signals are crucial for AI Overview selection.
What E-E-A-T means for AI Overviews:
How to signal E-E-A-T:
| Signal | Implementation | Impact |
|---|---|---|
| Author bio | Credentials, experience, photo | High |
| Citations | Link to authoritative sources | High |
| Original data | Research, surveys, case studies | Very High |
| Reviews/testimonials | Third-party validation | Medium |
| Publication date | Freshness indicator | Medium |
Our E-E-A-T optimization results:
Before: 12 AI Overview appearances After: 34 AI Overview appearances
What we added:
E-E-A-T doesn’t just help SEO - it helps AI systems trust your content enough to cite it.
Schema markup impact on AI Overviews:
Schema types that matter:
| Schema Type | AI Overview Impact | Implementation Priority |
|---|---|---|
| FAQPage | +40-65% | Critical |
| Article | +20-30% | High |
| HowTo | +25-35% | High (for tutorials) |
| Organization | +15% | Medium |
| Author | +20% | High |
FAQ schema specifically:
Research shows pages with FAQ schema are up to 65% more likely to appear in AI Overviews.
Why it works:
FAQ schema explicitly marks question-answer pairs. AI systems can extract with high confidence.
Implementation tip:
Don’t just add FAQ schema - ensure your FAQ content actually answers questions users ask.
Bad: “Q: What makes us great? A: We’re the best.” Good: “Q: How do I implement SSO? A: SSO implementation requires…”
Schema tells AI what’s a Q&A. Content quality determines if it’s worth citing.
Readability is underrated for AI Overviews.
The research:
Content written at 8th-11th grade reading level performs best.
Why:
AI systems need to extract and present content to diverse users. Complex language = harder extraction, less universal utility.
Readability metrics to target:
| Metric | Target | Tool |
|---|---|---|
| Flesch Reading Ease | 60-70 | Hemingway |
| Grade Level | 8-11 | Yoast |
| Sentence Length | <20 words avg | Hemingway |
| Passive Voice | <10% | Grammarly |
What we changed:
Before: Academic-style writing (grade 14+)
After: Clear, accessible writing (grade 9)
Result:
AI Overview appearances up 52% on same content, same rankings.
Simplify language without dumbing down ideas.
Multimedia matters more than people realize.
AI Overview multimedia patterns:
What to optimize:
Images:
Videos:
Tables:
Our test:
| Content Type | AI Overview Rate |
|---|---|
| Text only | 23% |
| Text + images | 31% |
| Text + images + video | 38% |
For visual topics (products, how-to, comparisons):
Including relevant multimedia significantly increases AI Overview likelihood.
Local businesses: different considerations.
Local AI Overviews:
Queries like “best [service] in [city]” trigger different AI Overview patterns.
What matters for local:
Our local client results:
| Factor | AI Overview Impact |
|---|---|
| GBP optimization | High |
| Local schema | High |
| City landing pages | Medium |
| Review responses | Medium |
Local-specific tips:
Local AI Overviews pull heavily from GBP and local signals - different optimization than informational content.
Adding to local context - Knowledge Graph matters.
Knowledge Graph connection:
When Google has strong Knowledge Graph data about your business, AI Overviews are more likely to reference you.
How to strengthen Knowledge Graph:
Organization schema that helps:
{
"@type": "Organization",
"name": "Your Brand",
"sameAs": [
"https://linkedin.com/company/yourbrand",
"https://twitter.com/yourbrand",
"https://wikidata.org/wiki/Q12345"
]
}
The sameAs property connects your entity across platforms, strengthening Knowledge Graph signals.
How to track AI Overview performance:
Tools and methods:
| Method | What It Shows | Limitation |
|---|---|---|
| Google Search Console | Impressions, clicks | Doesn’t separate AI Overviews |
| Manual testing | Specific queries | Time consuming, not scalable |
| Am I Cited | AI appearances across platforms | Best for comprehensive tracking |
| Rank trackers (some) | AI Overview detection | Varies by tool |
Metrics to track:
What we learned about CTR:
AI Overview clicks are different:
Track business outcomes, not just appearances.
This discussion confirmed and expanded my findings. Summary:
What drives AI Overview appearances:
| Factor | Importance | Action |
|---|---|---|
| Traditional SEO rankings | Critical | Can’t skip fundamentals |
| Content structure | High | Q&A format, clear headers |
| E-E-A-T signals | High | Author bios, citations, data |
| Schema markup | High | FAQPage especially |
| Readability | Medium-High | 8-11 grade level |
| Multimedia | Medium | Images, video, tables |
The priority order:
What I’m changing:
Tracking setup:
Expected outcome:
Based on discussion insights, targeting 3x increase in AI Overview appearances by focusing on fundamentals + structure.
Thanks everyone for sharing real data and strategies.
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