I think we’ve cracked something important, and I want to validate it with this community.
Background:
We’re in healthcare tech - a classic YMYL (Your Money or Your Life) space. For the past year, we’ve been obsessive about E-E-A-T:
What we’re seeing:
Our content is getting cited in AI responses at a dramatically higher rate than competitors who have higher domain authority but weaker E-E-A-T signals.
The numbers:
My hypothesis:
E-E-A-T matters MORE for AI search than it does for traditional SEO. AI systems are actively evaluating expertise and trustworthiness signals when selecting sources.
Questions:
Your hypothesis aligns with what we’re seeing in research. Let me provide context:
Why E-E-A-T matters more for AI:
Traditional Google search uses E-E-A-T as one of many signals in a complex ranking algorithm. You can sometimes overcome weak E-E-A-T with strong backlinks or technical SEO.
AI systems are different. They need to:
This creates stronger E-E-A-T dependency because:
Research finding:
52% of AI Overview sources come from top 10 results. But WHICH of those top 10 get cited? The ones with strongest E-E-A-T signals.
Your healthcare results make sense:
YMYL is where this matters most. AI systems are extremely cautious about health, finance, legal content. Strong E-E-A-T isn’t just helpful - it’s nearly required for citation in these spaces.
Based on citation pattern analysis, here’s the apparent weighting:
Highest impact:
Trustworthiness (40%+ of weight)
Expertise (25-30%)
Moderate impact:
Experience (15-20%)
Authoritativeness (15-20%)
How AI evaluates these:
AI systems likely look at:
Your MD-authored content hits all these signals heavily.
Finance perspective - seeing the exact same pattern.
Our E-E-A-T approach:
Our results:
The specific signal that seems to matter most:
Author credentials that can be verified externally.
When I added LinkedIn profiles and professional certification links to author bios, our citation rate increased noticeably within weeks.
My theory:
AI systems are cross-referencing authors. If “John Smith, CFP” on our site matches a real CFP in certification databases, that’s a strong trust signal.
Unverifiable credentials = lower trust = fewer citations.
Non-YMYL perspective here - tech/SaaS space.
Interesting finding:
E-E-A-T matters even outside YMYL, but the signals are different.
What we’ve found drives citations in tech:
Experience > Credentials
Practitioner Authority > Academic Authority
Current Experience > Historical
The pattern:
In tech, E-E-A-T still matters, but “Experience” and “Expertise” are weighted more than “Authoritativeness” compared to healthcare/finance.
AI seems to understand that different fields have different credibility markers.
Let me share the implementation framework we use for E-E-A-T optimization:
The E-E-A-T Audit Checklist:
Experience Signals:
Expertise Signals:
Authoritativeness Signals:
Trustworthiness Signals:
Scoring:
Most clients start at 40-50%. Getting to 80%+ takes focused effort but dramatically improves AI visibility.
Author authority angle - this is my specialty:
Why author-level E-E-A-T is becoming critical:
AI systems are getting better at entity resolution. They can connect:
What this means:
An article by “Dr. Sarah Johnson, MD, Chief of Cardiology at [Hospital]” with verifiable credentials across platforms will ALWAYS beat “Written by Staff” or “By the Marketing Team.”
What I recommend:
The investment:
Building author authority takes time but compounds. An author with strong E-E-A-T signals carries that authority to every piece they write.
One strong author can lift an entire content program’s AI visibility.
Agency perspective on implementing E-E-A-T at scale:
The challenge:
Most clients don’t have MD authors or CFP credentialed writers. How do you build E-E-A-T for “normal” businesses?
Our approach:
Find internal experts - Every company has subject matter experts. Identify them.
Build their presence - Help them publish, speak, get featured
Leverage their expertise - Have them author or review content
Document their credentials - Professional experience counts, not just degrees
Create validation - Industry interviews, case studies, recognition
Example:
Client sells manufacturing software. No PhDs. But their implementation lead has 20 years experience installing these systems.
We:
Result: His authored content now gets cited 3x more than generic “team” content.
The insight:
E-E-A-T doesn’t require academic credentials. It requires demonstrable expertise in whatever your domain is.
Operational angle - how to scale E-E-A-T:
The bottleneck:
Expert authors are expensive and slow. You can’t have your MD write every health article.
Our hybrid model:
What this looks like:
The key:
AI seems to recognize “reviewed by expert” as a valid E-E-A-T signal, not just “written by expert.”
This scales better while maintaining credibility signals.
Measurement:
Track citation rates by content type:
This discussion has validated our approach and given us new ideas to explore.
Confirmed insights:
E-E-A-T matters MORE for AI than traditional SEO - AI systems actively evaluate expertise signals when selecting sources
Trust is the foundation - Without trust, other signals don’t matter. Verifiability is key.
Author-level matters most - Entity resolution means AI connects authors across platforms
YMYL has highest standards - But E-E-A-T helps in all verticals
Credentials don’t require degrees - Demonstrated expertise and experience count
What we’re adding to our approach:
Author entity optimization - Better LinkedIn profiles, schema markup, cross-platform presence
External validation push - Get our MDs published in more external venues
Credential verification - Make credentials easier to verify externally
Expert-reviewed model - Scale content while maintaining E-E-A-T through review process
The strategic takeaway:
E-E-A-T isn’t just a Google ranking factor anymore. It’s becoming the primary trust signal for AI systems deciding which sources to cite.
Investing in genuine expertise isn’t optional for AI visibility - it’s the price of entry.
Get personalized help from our team. We'll respond within 24 hours.
Track how your expertise-driven content performs in AI search. See which authors and topics get cited across ChatGPT, Perplexity, and Google AI Overviews.
Community discussion on optimizing YMYL content for AI search. Health, finance, and legal content creators share strategies for meeting higher AI trust standard...
Community discussion on how AI systems evaluate author expertise. Real experiences from content creators testing expertise signals and E-E-A-T for AI visibility...
Community discussion on author bios for AI visibility. Real experiences from content managers on how author credentials affect AI citation rates.
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