Surface-level AI marketing content gets the work wrong because it is usually written by people summarizing other people’s advice instead of practitioners who have felt the constraints, tradeoffs, tech failures, revenue pressure, and customer consequences. In the AI era, summarizing information is no longer enough. Useful content has to come from doing the work.
That is the content problem AI is exposing.
The web is already full of articles written by people who have never done the thing they are explaining. AI just makes that easier to scale.
The result is polished content with no fingerprints.
It sounds fine. It ranks sometimes. It checks the boxes. But when you actually need to make a decision, it does not give you the goods.
The Information Commodity Problem
For years, content marketing rewarded companies for publishing useful information at scale.
That worked for a while.
But a lot of that content was never written by experts. It was written by good writers summarizing other sources. Sometimes they had a decent understanding of the topic. Sometimes they had almost none.
Now AI can do the summarizing faster.
That means basic information is becoming a commodity.
If your article is just a cleaner version of the same advice already on the first page of Google, AI can probably produce something similar in a few seconds.
So what is left?
Experience.
Judgment.
Nuance.
Stories from the work.
Specific examples.
Contradictions to best practice that only show up when you have actually tried the thing.
The Mailchimp Vlogging Example
I remember researching keywords around vlogging and finding an article from Mailchimp about how to get started with vlogging.
After reading it, one thing was painfully obvious:
The person who wrote the article had probably never held a camera.
Maybe they were a good writer. Maybe they did solid research. But the article felt like it had been scraped from a bunch of different sources and repackaged.
It had information, but not experience.
That is the gap.
The same thing happens in marketing content all the time. You read an article and can tell the writer has never had to make the thing work when the stakes were real.
They have never run the promotion and watched sales go up or down.
They have never had someone look them in the eyes wondering whether the numbers will be there.
They have never fought with the tech stack until the workflow finally worked.
They have never felt the win when the system clicked.
That experience changes how you explain the work.
What Practitioner Content Has That Generic Content Does Not
Practitioner content has scar tissue.
Not drama. Not fake vulnerability. Just evidence that the person has actually done the thing.
You can usually spot it in a few ways.
1. It Has Technical Examples
Surface-level content says:
“Use AI to improve your marketing workflow.”
Practitioner content says:
“Here is how I would use AI to turn a podcast transcript into a blog outline, three LinkedIn posts, a newsletter section, and a list of follow-up questions, while keeping the expert’s point of view intact.”
Specificity reveals experience.
2. It Speaks From First Person
Surface-level content hides behind corporate language:
“When implementing this strategy, brands should expect…”
Practitioner content can say:
“I tried this. It broke here. I changed this. Here is what I would do differently.”
That matters. Not because every article needs to be a personal essay, but because first-hand language creates accountability.
3. It Has Nuance That Contradicts Best Practices
Best practices are useful. They are also incomplete.
A real practitioner eventually finds the edge cases:
- When the best practice fails.
- When it only works for certain teams.
- When the advice is technically true but practically useless.
- When the common recommendation ignores organizational reality.
That is where content becomes valuable.
Anyone can repeat the rule. Practitioners can explain when the rule breaks.
AI Makes This More Important, Not Less
AI can make average content look more polished.
That is the problem.
Everyone’s posts are cleaner now. Everyone can sound more confident. Everyone can publish faster.
But if everyone can sound smart, sounding smart stops being an advantage.
In Own The Show, I call this the rise of artificial authority: people can look credible without having much substance behind the content.
The antidote is not to avoid AI.
The antidote is to give AI something real to work with.
Use it to organize your experience. Use it to sharpen your ideas. Use it to repurpose conversations. Use it to help you publish faster.
But do not ask it to replace the part where you actually know what you are talking about.
The Difference Between Helpful And Commodity
In the book, I make a distinction that matters here:
Ideas need to be both unique and helpful.
Helpful without unique becomes commodity advice.
Unique without helpful becomes novelty.
The sweet spot is an idea shaped by real experience that helps someone make a better decision.
That is what most AI marketing content is missing.
It may be helpful at the beginner level. It may define the terms. It may provide a list of tools. But it rarely has the point of view that comes from building the workflow, testing the process, and watching what happens.
How To Tell If An Article Was Written By Someone Who Has Done The Work
Ask these questions:
- Does the article include real examples?
- Does it explain tradeoffs?
- Does it mention what can go wrong?
- Does it show the before and after?
- Does it use first-hand language?
- Does it explain when the common advice fails?
- Does it include implementation details?
- Does it have a point of view beyond “it depends”?
If the answer is no, you may be reading a summary of the internet.
That might be fine for a definition. It is not enough for serious marketing work.
What Danchez.com Content Should Do Differently
My standard for this site is simple:
Write as someone who has done the work.
That means posts should include:
- What I have tried.
- What I have seen fail.
- What I would do first.
- What I would avoid.
- How I would think through the problem.
- Where AI helps.
- Where AI creates risk.
- What the reader should do next.
The goal is not to sound more polished than everyone else.
The goal is to be more useful because the advice comes from actual work.
Why This Matters For AEO And AI Search
AI answer engines are going to summarize a lot of generic content.
If your content only defines basic concepts, you are easy to replace.
But if your content contains original frameworks, first-hand examples, clear definitions, and specific practitioner judgment, it becomes harder to flatten.
That does not guarantee citations. Nothing does.
But it gives AI systems and human readers something distinct to associate with you.
The future is not more content.
The future is better source material.
Related Reading
- What Marketing Workflows Should You Automate With AI First?
- Why AI Works Best With Expert Processes
- How Marketing Directors Can Turn AI Hype Into A Practical Plan
- How Podcasts Help Experts Build Authority With AI
FAQs
What is surface-level AI marketing content?
Surface-level AI marketing content is content that summarizes common advice without first-hand examples, technical detail, practitioner nuance, or original point of view.
Why is generic AI content a problem?
Generic AI content is a problem because it increases the amount of polished but low-substance information online. It can make readers feel informed without helping them make better decisions.
How can marketers make AI-assisted content better?
Start with real source material: interviews, transcripts, customer stories, workflow notes, experiments, and lived experience. Then use AI to organize and polish the material instead of asking it to invent the substance.
Does every article need a personal story?
No. But every serious article should show evidence of experience, whether through examples, process detail, lessons learned, implementation notes, or clear judgment.
Final Take
The problem with surface-level AI marketing content is not that it uses AI.
The problem is that it has nothing real underneath it.
AI can help you publish faster. It can help you organize your thinking. It can help you turn one conversation into many assets.
But the best content still has to come from people who have done the work.
That is the standard now.
