The Tell Is Shifting
Two years ago, AI-written content mostly got flagged for one thing: factual errors. A chatbot would hallucinate a statistic, mangle a product name, or confidently describe a policy that didn't exist. That was the risk businesses worried about — and reasonably so.
That concern hasn't gone away. But something else has started happening alongside it. Readers are getting better — noticeably better — at detecting AI-generated content on feel alone, before they've spotted a single factual mistake. Not because they're running it through a detector, but because they've simply read enough of it to recognize the texture.
"Sounds like AI" is becoming its own red flag. And it's worth understanding what that flag actually signals to your customer — because it isn't the same thing as "this is wrong."
What Readers Are Actually Noticing
The tells aren't mysterious. They've been catalogued in comment sections, on LinkedIn, and in ordinary conversation. Here's what keeps coming up:
The opener that goes nowhere. AI loves to begin with a wide establishing statement — "In the world of small business, content is everything" — before finally arriving at a point. Readers have developed a fast-twitch response to this pattern. They've seen it hundreds of times.
Lists for everything. Genuine writing uses structure where structure is earned. AI defaults to bullet points and numbered lists even when the content would flow better as prose. A three-item list where each item is a single sentence has become a kind of signature.
Words that never appear in speech. "Delve," "leverage," "ensure," "seamlessly," "robust" — these aren't wrong, exactly, but they cluster in AI output in a way that reads as inhuman. No actual person says "this robust solution seamlessly delves into your needs."
Enthusiasm without specificity. AI is very good at sounding positive and very bad at knowing what to be positive about. It produces warmth without texture — "we're passionate about helping clients achieve their goals" — because it doesn't know your actual clients or your actual goals.
The symmetrical structure. Real writing has weight in unexpected places. AI tends to produce content that's mechanically balanced: three points in the intro, three in the body, three in the close. It feels assembled rather than written.
None of these is an absolute rule. Good AI-assisted content avoids all of them. The problem is that most AI-assisted content doesn't bother — because it doesn't have to, until it does.
Why 'Sounds Like AI' Is a Different Problem Than 'Contains Errors'
When AI content gets a fact wrong, the damage is specific. You correct it, apologize if necessary, and move on. The trust cost is real but bounded.
When content sounds like AI, the damage is murkier and potentially larger. What the reader actually concludes is:
"They didn't write this." Which means: whoever is behind this business doesn't think this communication is worth their time. Which means: maybe I'm not worth their time.
That's a different kind of trust erosion. It's not about accuracy. It's about whether there's a real person on the other end of the relationship — someone who chose the words, who has opinions, who knows something specific about this industry or this customer's situation.
For a small business especially, that perception of genuine human attention is often a primary competitive advantage over larger, more automated competitors. If your content reads like it was produced by the same tool your biggest competitor uses, you've handed away one of the few things that made you distinctive.
There's also a subtler issue: if customers suspect AI content, they start wondering what else is automated. Are the reviews curated by AI? Is the customer service chatbot going to handle their complaint? Is there actually a person to call? Doubt is contagious in a way that a single error isn't.
The question of whether to disclose AI use in your content is a separate discussion — and one worth having carefully, as explored in Do You Have to Tell Customers You're Using AI? — but even businesses that have decided disclosure isn't necessary should think hard about whether their content is actively triggering suspicion.
What's Actually at Risk Here (and What Isn't)
Let's be honest about where the real exposure sits, and where it doesn't.
Search rankings are not the main concern anymore. Google's current position is that it evaluates content quality and helpfulness, not how it was produced. AI content can and does rank. The article Will Publishing AI-Written Content Hurt Your Google Rankings? gets into this in more detail, but the short version is: SEO isn't where this problem bites you hardest.
Customer relationships are the main concern. The places where "sounds like AI" actually costs you money are:
- Email newsletters and follow-up sequences — where the reader has a direct expectation of human correspondence
- Social media captions and comments — where the format implies spontaneity and personality
- Proposal and quote cover letters — where a client is deciding whether to trust you with real money
- About pages and bios — where the claim is explicitly "here's who we are"
- Responses to reviews and complaints — where someone wants to know a person heard them
In none of these situations does the reader primarily want information. They want to feel like they're in a relationship with a business that sees them as a person. Generic AI prose is the opposite of that.
Commoditization is the medium-term risk. If every small business in your space is running the same AI content tools with similar prompts and similar prompting discipline, the content starts converging. Customers can't tell one business from another. On a long timeline, that's a brand problem — and brand problems are expensive to fix.
What to Actually Do About It
The goal isn't to stop using AI for content. That efficiency is real, and throwing it away isn't the answer. The goal is to use AI in a way that your voice survives the process.
Give the AI something it can't make up. Generic AI output is generic because it has no specific inputs. A blog post that starts "Write a post about our HVAC maintenance services" will sound like every other HVAC post because it has nothing to distinguish it. A prompt that includes a specific customer story, a quirk of your local climate, or a mistake you made last winter gives the model material to work with. The more specific your input, the less generic the output.
Edit for sound, not just sense. Most content review catches errors and omissions. Not enough content review catches passages that are accurate but don't sound like the business. If your emails don't sound like something you'd say to a customer's face, rewrite those parts — even if the information is fine.
Maintain a voice document. A simple two-page document that describes how your business talks — the vocabulary you use, the vocabulary you avoid, the topics you have strong opinions about, the register (formal? casual? technical?) — gives anyone editing AI content a reference point. This is one of the things we build with clients who are producing volume content: a voice guide the AI can actually use in the prompt.
Flag the high-stakes placements. Not all content carries the same relationship weight. An AI-drafted FAQ about parking at your location doesn't need the same scrutiny as a proposal cover letter or an email to a customer who had a bad experience. Know which placements actually require a human to sign off on the voice.
Read it out loud. This is the fastest and most reliable filter. If you'd be embarrassed to say it in a meeting, don't let it go out with your name on it.
The Honest Assessment
Customers catching AI content is not, for most businesses, currently a crisis. But the trajectory is what matters. Readers are getting better at this faster than content creators are getting better at disguising it — partly because the same AI tools that write content also teach people, through repetition, exactly what AI content sounds like.
The businesses that will be least exposed in two years are the ones that figured out now how to use AI for efficiency without letting it erase what made their voice worth reading. That's a solved problem, but it requires actually solving it — not just assuming the default AI output is good enough. The piece on AI for Marketing: Getting Content and Social Right Without Sounding Like Everyone Else covers the practical side of this in more depth if you want to dig into specific approaches.
The default AI output is increasingly not good enough — because the reader has seen it before.
If you want a second set of eyes on how your content is landing — or you'd like help building a voice guide before your next content push — let's talk. That kind of review is often faster than people expect, and the difference it makes to how customers read you is not small.