Last month I saw an ad from a seven-figure founder. Real business, real customers, years of positioning behind it.
The headline said, “You do not have a marketing problem. You have a messaging problem.”
I read it once and knew AI wrote it. I have seen that exact sentence shape hundreds of times this year alone.
More people use AI for everyday work now, which means more people recognize its patterns on sight. The construction “you do not have an X problem, you have a Y problem” used to sound sharp. Now it reads like every fifth ad in the feed.
This founder did nothing wrong by using AI. She used the tool everyone else is using, the same way everyone else is using it. What she published was a sentence that could have belonged to any business, in any industry, selling anything.
Her own customers, her own expertise, her own point of view: gone from that one line.
The patterns your customers can already spot
Here are twelve patterns AI defaults to. Once you notice them, you will see them everywhere, including in your own drafts.
1. “The problem isn’t X, it’s Y”
Sounds like: “The problem isn’t content. It’s consistency.”
Why it reads as AI: the construction is overused and predictable. Readers recognize the shape before they finish the sentence.
Better: state the point directly. “Inconsistent publishing is usually a systems problem.”
2. Fake-candid openers
Sounds like: “Honestly…” “To be honest…” “Here’s the thing…”
Why it reads as AI: it tries to manufacture trust instead of earning it. Real candor does not need to announce itself.
Better: start with the point.
3. Rule-of-three copy
Sounds like: “Clarity, consistency, and confidence.”
Why it reads as AI: language models default to symmetrical groups of three. Real people rarely organize a thought that neatly.
Better: use the exact number of ideas the point needs. Sometimes that is one.
4. AI filler words
Watch for: landscape, testament, showcasing, transformative, leverage, additionally, actually.
Why it reads as AI: these words add length without adding meaning.
Better: use the plain word underneath.
5. Manufactured drama
Sounds like: “No preference. No context. No memory.”
Why it reads as AI: short dramatic fragments mimic a writing style instead of making an actual point.
Better: use a real example. Let it carry the weight.
6. Chaining clauses together
Sounds like: “The team was talented, experienced, and still stuck.”
Why it reads as AI: it strings extra clauses onto one sentence. A person would more often just start a new one.
Better: use periods and commas. Let one idea finish before the next one starts.
7. Vague authority claims
Sounds like: “Experts agree…” “Industry leaders believe…”
Why it reads as AI: there is no name behind it. No source. No way to check it.
Better: name the source, or cut the line.
8. Significance inflation
Sounds like: “Game-changing.” “Revolutionary.” “Pivotal.”
Why it reads as AI: not everything can be the biggest thing that ever happened. When everything is important, nothing is.
Better: describe what actually happened.
9. Avoiding simple sentences
Sounds like: “The platform serves as…”
Why it reads as AI: it dresses up a plain sentence. It sounds more formal than it needs to be.
Better: “The platform is…”
10. Synonym cycling
Sounds like founder, entrepreneur, business leader, and visionary, all describing the same person in one paragraph.
Why it reads as AI: people repeat the same word on purpose, because repetition is clearer. AI avoids repetition even when repetition would help the reader.
Better: pick the clearest word and use it again.
11. Generic conclusions
Sounds like: “The future looks bright.”
Why it reads as AI: it sounds finished, but it says nothing a reader could act on.
Better: state the next step, or make an actual point.
12. Writing that avoids taking a position
This pattern matters more than the other eleven.
The biggest giveaway is rarely a single word. It is the absence of a real opinion. Generic AI writing tries to sound correct. Strong writing sounds like it came from someone who actually believes something, and is willing to say so.
The first eleven patterns are easy to fix. Find the phrase, delete it, move on. The twelfth one is different. You cannot search and delete it, because it is a habit, not a phrase. It is the habit of never saying anything a reader could disagree with. That habit points at the real problem.
Banning the phrases will not fix this
You could paste this list into ChatGPT right now. Tell it never to use any of these patterns again. Your writing would probably improve.
It would not solve the underlying problem.
None of this teaches AI what your business believes. Who your customers actually are. What your best client would say, in her own words, if you asked her directly. What you would never publish, even if it tested well.
That is a different problem, and it goes deeper than word choice. Once every business bans the same twelve phrases, a new set of generic patterns will take their place. The cause was never the phrases themselves.
Why the phrases keep coming back
Here is the cause. AI defaults to safe, familiar language when it does not have enough specific information about your business to draw from.
Ask it to write about your company and it can only use what you gave it. Usually that is a short prompt, a brand guide, and a few examples if you keep them organized. For most businesses, that is not enough to work with.
What is usually missing:
- Real customer language, not a guess at how customers talk.
- Actual positioning, not a category description.
- Examples of work you approved, and work you rejected, with the reason why.
- What your best customer says, in her own words.
- What competitors already say, so yours can say something different.
- Your own history: what has already worked, and what has already failed.
Give AI a thin brief and you get thin writing back. That is what happens any time a writer, human or AI, is asked to represent a business they do not actually know.
What sameness actually costs
This costs more than a handful of awkward sentences.
More people use AI every day now. That means more people have learned to recognize what it produces. Your customer sees one of these patterns in your marketing and she pauses.
That pause is the real risk. She starts wondering what else in your business AI is running without anyone checking it. Whether the expertise she is paying for is actually there. Whether what she is about to buy matches what she is about to receive.
This is not limited to words anymore. Product photos raise the same question now. Is this the real item, or an AI-generated stand-in for it.
My read of the market right now: customers are not rejecting a business simply because it used AI. The risk shows up when the work feels generic, mass-produced, interchangeable, or disconnected from the expertise they believe they are paying for. Generic AI is the actual target here, not AI as a category.
Some founders will read this and consider the opposite move: drop AI entirely and go back to doing everything by hand. That will not hold up. Competitors who train their AI well are already moving faster with it. Staying fully manual does not protect a business from that. It puts the business further behind.
Sameness makes price the easiest thing left to compare, too. If every business in your category sounds interchangeable, the cheapest one starts to look like the safest choice.
The review you never stopped doing
There is a cost inside your business too.
When AI does not know your standards, you end up saying the same things over and over. “That’s not how we talk.” “Our customer would never say that.” “That’s too generic.” “That’s not something I would approve.”
You are technically using AI. You are also still the final quality check, on every draft, every time. The tool changed. The amount of your own attention it needs did not shrink nearly as much as you were told it would.
What a brand guide cannot capture
There is a layer under the customer research and the brand guide. It is even harder to hand off, and it is your judgment.
You know almost instantly why one headline works and a similar one does not. You cannot always explain why in the moment. It is not written down anywhere. It was built from years of decisions nobody documented, including you.
AI cannot use judgment it was never given access to. Every time it guesses wrong, the guess comes back to you to fix. That is why the founder ends up finishing the work anyway, no matter how good the tool is.
The real goal is writing grounded closely enough in your business that it would not fit any other business in your category. Hiding that AI wrote it was never the point.
Give AI your judgment, not just your topic
The fix is a different starting point. Train AI on your business before you ask it to represent your business.
That means giving it real access to what a strong new hire would need on day one. Your positioning. Your standards. Your voice. Your customer research. Examples of work you approved, and work you rejected.
It also means giving it the harder layer underneath all of that: your judgment. Why you say yes to one idea and no to another that looks similar on paper. What sounds too safe. What sounds too aggressive. What your customer would never say. What your business would never claim, even if it were technically true.
Once that information lives somewhere AI can actually use it, the process changes shape. Your business knowledge and your judgment feed into the work. A draft comes back. You review it, and correct what still misses. What you correct becomes part of what the system already knows next time.
That loop is the whole idea. Every draft should need less correction than the one before it, because the work is no longer starting from a blank page. It is starting from what you already decided.
The point here is the principle, not a technical tutorial. Judgment that used to live only in your head can live somewhere your team, and the AI working alongside them, can both draw from it.
What we are testing inside Archeva
We are running this experiment inside our own company right now.
We are teaching our Brand Voice Copywriting Agent to recognize and remove the twelve patterns in this article. That part is the easy half.
The harder question is the one that actually matters. How much of my own review time does the system still need before a draft is ready to publish? A caption that avoids twelve bad phrases is easy to measure. A business that needs less of my attention on every draft is a different test. That is the one that counts.
We are documenting what works, what breaks, the cost to run it, and how much review time it actually saves, as we go. Some of that is still being measured. Where it is not finished yet, we are saying so rather than rounding up.
Done well, this tends to produce stronger first drafts, less rewriting, more consistent brand execution, and less time spent repeating the same standards out loud.
Your brand voice is one piece of a larger pattern. Most businesses keep routing decisions back through the founder. It is rarely because the team cannot execute. It is because the standards were never written down anywhere the team, or the AI working alongside them, could actually use.
Removing twelve phrases is easy. Building AI that understands enough about your business to need less correction is the harder problem. That is what we are testing next.
You have seen the obvious patterns. The bigger question is how much AI needs to know about your business before you can trust what it creates. I am documenting that build in public, including what works, what breaks, and how much founder review it actually removes.
Already know you want help doing this inside your business?
See how Archeva trains AI around your business knowledge, standards, and judgment. The demo walks through it before anything moves toward an application.
Questions business owners ask about this
Is Archeva against using AI?
No. Untrained AI is the target, not AI as a category. Trained on real business judgment, AI produces stronger first drafts than most businesses get today.
Will removing these 12 phrases fix generic AI content?
It will improve it. It will not fix the underlying problem. AI still will not know your standards or your customers’ actual words. It will not know what you would never publish, unless you give it access to that information directly.
What is different about training AI on judgment instead of just a brand guide?
A brand guide usually documents tone: word choice, formatting, a list of words to avoid. Judgment is different. It is why one headline gets approved and a similar one gets rejected. Most businesses have never written that part down, for a person or for AI.