We’ve been using AI to help with content creation - drafts, outlines, research assistance.
Now I’m worried: Does AI “know” when content is AI-generated? Will our content be penalized for not being “authentic”?
Our current process:
Questions:
Let me clarify the reality around AI-generated content and visibility.
The truth: No specific AI content penalty
ChatGPT, Perplexity, and Google AI don’t have a filter that says “this is AI content, penalize it.”
What they DO evaluate:
| Factor | How It’s Assessed |
|---|---|
| Quality | Is the content accurate and helpful? |
| Originality | Does it add unique value? |
| Authority | Are there expertise signals? |
| Comprehensiveness | Is the topic well-covered? |
Why some AI content fails:
Poor AI content often:
This content fails because it’s LOW QUALITY, not because it’s AI-generated.
Your process sounds fine:
AI drafts + heavy human editing + original data + expertise = likely good content.
The question isn’t “Was this made with AI?” but “Does this add value?”
This is the key question. Value-adds that AI can’t replicate:
1. Original data and research
2. First-hand experience
3. Expert perspective
4. Specific examples
The test:
Ask: “Could ChatGPT generate this exact content on its own?”
If yes: Low authenticity value If no: High authenticity value
Your content should include things AI couldn’t write without your input.
The authenticity framework I use.
Authenticity signals that matter:
| Signal | Why It Matters | How to Add |
|---|---|---|
| Original data | AI can’t invent data | Include proprietary research |
| Expert quotes | Shows real expertise sourcing | Interview actual experts |
| Specific examples | Details AI wouldn’t know | Use real cases |
| First-hand account | Personal experience | Share what you’ve done |
| Updated information | Recent developments | Include current data |
| Unique perspective | Opinion/analysis | Add your take |
The authenticity checklist:
For each piece of content:
Content that passes this checklist performs well regardless of how it was created.
Pragmatic perspective on AI content and visibility.
What I’ve observed:
We’ve tracked 500+ pieces of content across clients. Some AI-assisted, some fully human.
Results:
| Creation Method | Avg Citation Rate | Notes |
|---|---|---|
| AI + heavy editing + original data | 36% | Performs well |
| Fully human with expertise | 38% | Similar performance |
| AI with light editing | 19% | Lower performance |
| Human-written commodity content | 21% | Also lower |
The insight:
The creation method barely matters. What matters:
Low-quality content fails regardless of origin.
High-quality content succeeds regardless of origin.
Your worry is misplaced:
Focus on content quality, not creation method.
Expert content perspective.
Why expert content wins:
AI systems are trained to recognize expertise patterns:
Expert content sounds different:
Generic: “Marketing automation can improve efficiency.”
Expert: “In our work with 50+ B2B teams, we’ve seen marketing automation reduce manual campaign setup time by 40-60%, with the biggest gains in email nurture sequences. The catch: teams without documented processes often see initial efficiency drops before gains.”
The second version:
AI can’t replicate this without real expertise input.
Making AI-assisted content expert:
The AI provides structure; you add the expertise.
Tracking perspective on content performance.
What we track:
For content performance analysis:
Patterns over 6 months:
High performers (regardless of method):
Low performers (regardless of method):
The correlation:
Content quality markers correlate with performance. Creation method does not significantly correlate.
What to track:
Use Am I Cited to see which content gets cited. Then analyze what high-performers have in common.
Usually it’s authenticity markers, not creation method.
Operations perspective on content process.
Our AI-assisted workflow:
The originality check:
Before publishing, we ask:
If no to all: Back to editing to add value.
Results:
This process creates efficient, high-quality content that performs well.
The key insight:
AI is a tool. Tools can create quality or junk depending on how they’re used.
Use AI for efficiency, but ensure human expertise adds uniqueness.
This put my concerns to rest. Summary:
Key insights:
Our improved process:
Keep AI assistance, but ensure every piece includes:
The test before publishing:
“Does this add value beyond what ChatGPT could write on its own?”
If no → Add more original content If yes → Ready to publish
Tracking:
Monitor content performance via Am I Cited to see what actually works, not what we assume works.
Thanks everyone - worry eliminated.
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