Imagine a landing page headline: “Empowers teams to collaborate seamlessly across the entire organization.”
An AI wrote it. A PM read it, thought it sounded fine, and was about to hand it to engineering. Nobody asked the obvious question: could a competitor run this headline unchanged?
The answer was yes. And the actual differentiator (approvals don’t block the review queue) was nowhere in the copy.
This is what AI-generated marketing copy does. It doesn’t produce errors in the traditional sense. It produces generic accuracy: statements that are true of the entire category, presented as if they describe something specific about yours.
The category-description problem
AI models have absorbed enormous amounts of SaaS marketing copy. When you ask for copy about a project management tool, a CRM, or a developer platform, the model reaches for the category template. The output reads like marketing because it was trained on marketing, but it describes the category, not the product.
“Streamline your workflow.” “Boost team productivity.” “All your tools in one place.”
These are true of every product in the space and specific to none of them. A competitor could run the same copy without changing a word. That’s the tell: if the copy could belong to a competitor without modification, it hasn’t been written yet.
The fix isn’t to ask the AI to “be more specific.” It is to supply the specifics yourself. The approval queue that doesn’t block review. The way your sync happens client-side. The thing you built that your category hasn’t. Feed that in, and the model can write copy around it. Skip that step, and the output will always drift toward the template.
Try it on a generic product page
Standard AI output for a project management tool. Nothing wrong with it, nothing specific to any product.
What to check before anything goes live
The headline test. Could this headline appear on a competitor’s site without anyone noticing? If yes, it needs to be replaced with something that names what actually differentiates the product. Not the category. The specific thing.
Claimed outcomes without numbers. “Saves time,” “reduces errors,” “improves conversion.” If the product produces a measurable result, the copy should name it. If it doesn’t, the claim should be cut or softened. AI will confidently fill in outcomes that sound plausible. They’re not necessarily yours.
Feature claims against what’s live. AI generates copy in present tense. If the integration with Salesforce is on the roadmap, not in the product, the copy about it needs to go or be reframed. This happens often because AI draws on announcements, launch posts, and roadmap descriptions that were indexed at some point.
Social proof that isn’t real. “Used by teams at Fortune 500 companies.” “Trusted by 10,000+ users.” AI generates these constructions because they appear constantly in marketing copy. If you haven’t verified the claim, it shouldn’t be in the copy. Generic trust signals that you can’t back up are worse than no trust signal at all: they invite the kind of scrutiny that finds the gap.
Tone that doesn’t match how your customers talk. This one is harder to catch. If your customers are developers, copy aimed at “cross-functional stakeholders” will feel wrong to them even if they can’t say why. The mismatch usually lives in word choice: “leverage” instead of “use,” “solutions” instead of “tools,” “empower” instead of anything a person would actually say. Read it in the voice of your best customer and notice where it stops sounding right.
The passive construction that hides uncertainty. “Can help teams,” “may improve,” “is designed to support.” These are AI hedges. They appear when the model isn’t confident enough to make a direct claim. Sometimes that’s appropriate. More often it means the specific claim needs to be found and stated, or the copy is filling space.
The version that sounds better but means less
One pattern that’s easy to miss: AI copy often gets better-sounding through revision without getting more specific. You ask it to be more conversational. It becomes more conversational. You ask it to be more confident. It removes the hedges. The copy sounds good. It still describes nothing in particular.
The right test after any revision isn’t “does this sound better?” It’s “is there now something in here that couldn’t appear on a competitor’s page?”
If the answer is still no after several rounds, the problem isn’t the writing. The problem is that the specific differentiators haven’t been supplied. No amount of prompting will produce them from nothing. Someone who knows the product has to put them in.
That’s usually one person. The founder, the PM, whoever decided what to build and why. Five minutes of their attention on a draft (reading it out loud, naming what’s missing, saying the actual differentiator) changes the output more than any prompt refinement.
The copy doesn’t need a better writer. It needs the person who knows the product to read it before it goes live.