Generic AI output ("AI Copy Slop") is writing that contains nothing only your business could have supplied.
A better prompt can’t add what you never provided. A person brings the facts, the position, and the exceptions, then owns the edit.
To stop your brand’s content from sounding like generic AI output, you need to add in what only you or your business knows — your differentiating factor, your unique offering to the market. Then put one person in charge of the edit.
That means when you create your content, you must have three things a model can’t invent:
A position you’ll defend
A pattern from your own work
An exception, meaning who your advice is not for
The reality is that rewriting AI sentences or polishing them up to sound human just doesn’t work.
Here below, I ran a test to show it. One prompt, three sets of input, same model.
The first paragraph came back clean and empty. The second sounded like me and still said nothing I’d stand behind. The third carried a fact, a number, and a limit, plus one line I had to cut because nobody could back it up.
- Generic means interchangeable. Any competitor could publish the draft unchanged, and clean grammar doesn’t fix that.
- Voice files change the register, not the facts. In my test they made the paragraph sound like me and added nothing true.
- Five changes make a draft specific: fact repair, signal over summary, a position, an exception, and an owner.
- Readers already feel the pile-up. 65% of surveyed U.S. consumers say brands produce too much AI-generated marketing content.
- One named person owns the edit. AI can flag problems. It can’t answer for a claim.
Why does my AI content sound generic even when it reads well?
Because a model compresses. It’s built on tokens and returns the most probable next word. And that’s usually a tighter, flatter version of whatever you handed it originally. It’s built for efficiency, not creativity.
Even when you tell it to be creative, it gets creative on that same fundamental concept: output fast and effectively, all based on learned patterns.
From the outside, compression looks like judgment. The two behave differently once a buyer has to choose.
gen·er·ic A·I out·putnoun
Generic AI output is writing that contains nothing only your business could have supplied: no fact, position, or limit that a competitor couldn’t also claim.
Also called: interchangeable content, category content
Researchers have seen the same split now that AI copy has been out in the wild for a couple of years.
In a 2024 short-story experiment, writers with the most AI access produced stories rated 8.1% higher for novelty and 9% higher for usefulness. The AI-assisted stories also became more similar to each other (Doshi and Hauser, Science Advances).
Each writer improved, and the group converged.
But a story experiment isn’t a marketing study, so I read it as the shape of the problem, not a forecast for your blog or content.
But you can bet that your readers already feel the pile-up. Gartner surveyed 1,006 U.S. consumers in May and June 2026. 65% of them said brands produce too much AI-generated marketing content. 57% said it has made them less trusting of brand messaging overall.
of 1,006 U.S. consumers say brands produce too much AI-generated marketing content
57% say it has made them less trusting of brand messaging overall. Gartner survey, fielded May to June 2026, released Sept. 22, 2026.
Again, those are perceptions, not purchases. But they still tell me the bar moved, and that clean copy no longer earns attention by being clean. It has to be something different altogether now.
I covered the blank-slate mechanism and the full two-document build in why a better AI model won’t fix generic content, and the diagnosis in why so much B2B content sounds like ChatGPT wrote it.
So, you already have a draft. What do you change?
What does generic AI output look like in a real draft?
It looks fine. But that’s the problem. The grammar is clean, and you can b et that the structure is tidy. Technically, nothing in it can be wrong, because nothing in it is specific.
So I picked one topic to test this out: How do you decide which old blog posts to refresh?.
I gave the model the same instruction every time: write the opening paragraph, about 110 words, for a founder or marketing lead at a B2B company.
Same model. Same instruction. Three sets of input.
Step one: no context
Just the instruction. You’ll see the result in a moment.
Step two: my two voice files
I loaded my positioning statement and my brand voice guide. The paragraph came back sounding like me.
It used a phrase I often write with (that now sounds like AI, go figure): “Here’s the thing…” It also took a confident stance on gut feel, and it promised to walk through “the four checks I run on every archive, in the order I run them.”
But it never named a check, because I hadn’t given it any. It borrowed my first-person authority and spent it on nothing.
That’s the part voice files don’t fix. They change the register, but they can’t add a fact.
Step three: voice files plus a source packet
Four items, all from my own work:
A position. Refresh by clicks, not impressions.
A pattern. On a home-services client’s site, about 45% of desktop impressions were automated rank-tracking traffic, so I filter Search Console to mobile before a query number drives a decision.
An exception. A site under about 50 published pages, or six months of Search Console data, doesn’t have enough signal. Publish something new instead.
A rule. A refresh adds substance. A date swap doesn’t count.
Here’s step one next to step three.
One prompt, two inputs
Same model. Same instruction. The opening paragraph for “How do you decide which old blog posts to refresh?”
If your company has been publishing for a few years, you probably have a library of posts that nobody has looked at since the day they went live. Some of them still bring in traffic, some quietly lost their rankings, and some say things your team no longer believes about your product, your market, or your customers. The hard part is that you can’t refresh all of it, so you have to choose. I think the best way to choose is to stop asking which posts are oldest and start asking which ones are closest to paying off. In this post, I’ll walk through the signals I check first, how I rank them, and when I’d rather retire a post than rewrite it.
Position
Mild: pick the posts closest to paying off
Evidence
None. The “signals I check first” are never named
Limit
None
Most founders I talk to pick refresh candidates by impressions. I think that’s backwards.
A page with 500 clicks deserves more of your attention than a page with 50,000 impressions and 10 clicks. So how do you decide which old posts to refresh? Start with clicks, then check your data before you trust it. On one home-services client, about 45% of desktop impressions turned out to be automated rank-tracking traffic, so I now filter Search Console to mobile before any query figure drives a decision. Two cities that client wanted to expand into showed zero mobile impressions. The demand was phantom. (One caveat: under about 50 published pages or six months of data, publish something new.)
Position
Clicks over impressions
Evidence
45% of desktop impressions were rank-tracking bots; zero mobile impressions in two target cities
Limit
Under about 50 pages or six months of data, publish something new
What the source packet added: four things only a person could supply.
Look at the struck line. The model wrote “Most founders I talk to pick refresh candidates by impressions.” I never said I talk to founders about this. It invented a sample, and it reads well enough to pass a skim. A polish pass keeps that line.
A rewrite pass catches it, and I bet you notice it once you look at them side-by-side.
Two more things changed. Step three states a number you can check against your own Search Console. And its one caveat tells a small site to skip the exercise, which is a sentence a competitor’s version won’t contain.
What does a rewrite pass change that polishing doesn’t?
A rewrite pass changes what the draft claims. Polish changes how it reads. You need both, and most teams only do the second.
| Polish | Rewrite pass | |
|---|---|---|
| What changes | How the sentences read | What the draft claims |
| What it catches | Filler words, rhythm, clichés | Wrong facts, a missing position, missing limits |
| A claim nobody can source | Passes | Cut |
| Who can do it | An editor with a style guide | A person who knows the subject |
| Result | Cleaner and just as interchangeable | Specific enough to attribute to you |
But that’s not all — you need to give it something that only you can give it.
But at the minimum, I run five changes, in this order:
Fact repair. Fluent drafts carry wrong claims. A health-niche client’s AI-and-synonym-spinner drafts called individual amino acids “personal amino acids” and metabolic rate “body rate.” No grammar checker flags either one. A person who knows the field does.
Signal over summary. Say the point out loud to someone outside your industry. If they ask a follow-up, it’s a summary. “The four checks I run” fails because a stranger has nothing to repeat.
A position. One thing you’d defend in front of a client. In step three, it’s clicks over impressions.
An exception. Who this advice is not for. It’s the sentence a template can’t fake.
An owner. A name, a date, or a first-hand detail only you could supply. The 45% figure is one. So is a client rule: an AI-generated draft for a software client mentioning the client nine times in educational articles, once inside a list of competing platforms; someone read it well, and a person who knew the rule removed all nine.
A model compresses.
A person distills.
— Brad Bartlett
That’s why I don’t treat “sound human” as a style setting. A model compresses what you give it. Distilling takes judgment. And that judgment has to come from a person.
How do I know the rewrite worked?
Run the Signal Test. Say the main point of the paragraph, out loud, to someone outside your industry, in under ten seconds. If they can repeat what it is, who it’s for, and why it matters with no follow-up question, it passes. If they nod and say “uh-huh,” it fails, no matter how many drafts it took to get there.
Then strip the logo and ask whether anyone would know it’s yours. I lay out that check, with two others, in how to scale content without losing your brand voice.
Then track it. Put your top buyer question into ChatGPT, Perplexity, and Google AI Mode, and log who gets cited. I do this for the question this post answers. As of this week, none of the brands I track get cited for it, and that includes this site. I’ll re-run it in about four weeks.
Who should own the final edit?
One named person, with the authority to stop a draft. AI can draft, summarize, and flag problems. It can’t answer for a claim.
I spent years in pastoral ministry, and one thing carried over: people know when someone is truly speaking to them. A published sentence is a promise to a real person, and a promise needs someone who can be asked about it.
The owner does four jobs:
Supplies or approves the source packet.
Checks every claim, number, and quote against a source.
Holds the exception, so the post says who it’s not for.
Signs the edit with a name.
For how bylines and authorship signals fit into search, see human authorship signals in AI search.
Will fixing this get me cited by ChatGPT and Perplexity?
No one can promise that. I’d be wary of anyone who claims they can.
The fine print
Nothing here is a ranking factor. Google’s May 2026 guidance on generative AI search puts unique, useful content at the center and doesn’t name voice as a ranking factor. What I say about voice and citation is my position, backed by one observed case.
Here’s what I can point to. A self-managed HOA software company I write for is the #1 cited brand in all 10 of its core topics in Google’s AI Mode, and about 4 in 10 of the answers we sampled cite it.
The content is specific, built around the questions buyers ask, and written to sound like a person. I can’t separate the voice from the rest of the work, so I call it one observed case.
The full numbers are in Your B2B Content Ranks on Google. So Why Won’t ChatGPT Cite It?
My working belief is that AI doesn’t cite the best content. It wants to cite the most recognizable content. A draft any competitor could publish gives an engine nothing to recognize.
Three moves for this week
01
Write the packet
Three sentences: a position you’d defend, a pattern from your own work, and who your advice is not for.
02
Cut what you can’t source
Strike every claim, number, and “I’ve seen” the model added. Keep only what you can point to.
03
Name the owner
One person signs the edit, then says the main point out loud to someone outside your industry.
Generic is a missing-input problem. Give the model what only you know, cut what you can’t source, and put a name on the edit.
Sound human. Get cited.
Your draft reads fine. Would a stranger know it’s yours?
If your content could belong to any competitor, an AI engine has nothing in it to attribute to you. A GEO Visibility Audit shows where you’re cited, where you’re skipped, and which gap is yours.
Start there. I’ll tell you what to fix first.
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FAQ
Can AI write in my brand voice?
Yes, to a point. Voice files and approved examples change how a draft sounds. In my test they added a confident first-person tone and no facts, so a person still has to supply the substance and review the result.
Is removing AI buzzwords from a draft enough?
No. Banned-word lists catch tells and miss emptiness. The step-two draft in my test had no buzzwords and still promised four checks it never named. Edit for claims first, then for words.
Should I stop using AI for content?
No. Use it to organize interviews, draft from approved material, and flag unsupported claims. Keep the position, the facts, and the final call with a person. Research suggests AI can raise individual quality while making outputs more alike, so the input you supply is the lever.
Does Google penalize AI-written content?
Not for using AI. Google’s guidance allows appropriate AI use and targets scaled, low-value content made to manipulate rankings. A few specific, reviewed posts are a different situation from mass-produced pages.
How do I test whether a draft sounds like us?
Run the Signal Test. Say the main point in under ten seconds to someone outside your industry. If they can repeat what it is, who it’s for, and why it matters without a follow-up question, it passes. Then strip the logo and ask whether anyone would know it’s yours.
Who should review AI content before it publishes?
One named person who can stop a draft. That person checks every claim against a source, confirms the position is one the brand holds, and signs off. AI can flag problems, but it can’t answer for a claim.
Where do I start if I only have an hour?
Pick one draft. Write three sentences: a position you’d defend, a pattern from your own work, and who your advice is not for. Give those to the model with your voice files, then cut every claim you can’t source.
