Our author bios were killing our AI visibility - here's what we changed

Author credibility is core to E-E-A-T, and AI systems are trained to recognize it.

What AI looks for in authors:

The verification chain:

AI cross-references:

• Your author bio → LinkedIn profile
• Your claims → External mentions
• Your expertise → Publishing history

Inconsistencies hurt you.

If bio says “10 years experience” but LinkedIn shows 3, AI notices.

The external validation priority:

What helps most:

1. LinkedIn profile match
2. Other publication bylines
3. Speaking engagements listed elsewhere
4. Academic credentials verifiable
5. Industry recognition

Verifiable expertise > claimed expertise

For YMYL content, author credentials are critical:

Health content example:

What we require for health content:

1. Medical reviewer - Actual MD reviews content
2. Credentials stated - “Reviewed by Dr. X, MD”
3. Verification links - Link to NPI registry
4. Institution affiliation - Hospital/practice name
5. Update dates - “Medically reviewed on [date]”

The AI perception:

For medical queries, AI strongly prefers institutional or credentialed sources. Generic health blogs get ignored.

For YMYL, invest in credentialed authors or medical review.

B2B author credibility signals:

What matters for B2B AI visibility:

The executive author strategy:

For B2B thought leadership:

1. CEO/executive bylines
2. Product expert authors
3. Customer success leaders
4. Technical leads

What to include:

• Current company and role
• Previous companies (brand association)
• Industry tenure
• Specific expertise areas
• Published articles/whitepapers
• Podcast/webinar appearances

Results:

Content from executive authors: 35% AI citation rate Content from marketing team: 18% AI citation rate

Expertise level matters.

Schema markup for authors - yes, it helps:

What to implement:

On articles:

{
  "@type": "Article",
  "author": {
    "@type": "Person",
    "@id": "https://site.com/author/name",
    "name": "Author Name",
    "url": "https://site.com/author/name"
  }
}

On author pages:

{
  "@type": "Person",
  "@id": "https://site.com/author/name",
  "name": "Author Name",
  "jobTitle": "Title",
  "worksFor": {"@type": "Organization", "name": "Company"},
  "sameAs": ["linkedin", "twitter"],
  "knowsAbout": ["topic1", "topic2"],
  "alumniOf": {"@type": "CollegeOrUniversity", "name": "School"}
}

Why it matters:

Schema makes author connections explicit for AI parsing.

Without schema: AI must infer author-content relationship With schema: AI clearly sees the connection

Testing showed:

With author schema: 23% citation rate Without author schema: 16% citation rate

The freelancer challenge:

The problem:

We use freelance writers. They don’t want:

• Photos on our site
• Detailed bios
• LinkedIn connections

But anonymous content performs poorly.

Our solutions:

1. Pseudonym with credentials

   • Real credentials, pen name
   • Still builds author credibility

2. Editorial team bylines

   • “The [Company] Editorial Team”
   • Team page with actual people

3. Subject matter expert review

   • Freelancer writes
   • Internal expert reviews
   • Expert byline or “reviewed by”

4. Ghostwriting for executives

   • Freelancer drafts
   • Executive byline
   • Both parties agree

Results comparison:

Anything is better than anonymous.

Fantastic insights. Here’s my author bio framework:

The Author Optimization Checklist:

Author page requirements:

On-article requirements:

Verification alignment:

Ensure consistency across:

• Your site bio ↔ LinkedIn
• Credentials claimed ↔ External verification
• Experience stated ↔ Career history

The hierarchy of author types:

Action items:

1. Audit all author pages
2. Add missing credentials
3. Implement author schema
4. Connect to external profiles
5. Track citation rates by author

Thanks everyone - this completely changed our author strategy!