Generic content and AI content are not the same thing, but they overlap more than they should. The reason is workflow: most AI generation workflows don't require the specific inputs that produce specific output, so they default to producing the average article on the topic. That average article is generic by definition.
Fixing generic AI content is not complicated, but you have to identify it first. These are the eight most reliable signs.
1. The Introduction Could Belong to Any Article on the Topic
The telltale generic opening: "In today's digital landscape, [topic] has become increasingly important for businesses of all sizes." Or a variation: "If you're looking to [achieve outcome], you've come to the right place." These openings establish that the topic exists. They don't say anything about it.
A specific article opens with the most interesting or contentious thing it has to say. It doesn't warm up — it starts. The introduction of a generic article could be transplanted to any of the other articles ranking for the same keyword. The introduction of a specific article couldn't.
The fix: Delete the first paragraph. Read the second paragraph — it usually contains the first sentence worth keeping. Start there, or write a new opening that leads with the article's actual argument.
2. Every Section Has Roughly the Same Word Count
Generic AI content allocates equal space to every subtopic because the model has no reason to prioritize one aspect of a topic over another. A human expert who has actually worked in the subject area knows which parts matter most and which parts can be addressed briefly. Equal section weighting is a signal that no such judgment was applied.
The fix: Identify the two sections with the most real substance — the ones where the article is saying something specific and useful. Let those run longer. Cut or compress the sections that are covering a topic just because it appears on the standard list.
3. The Examples Are Hypothetical and Generic
"For example, imagine a small business owner who wants to grow their email list." This is an example-shaped sentence that contains no actual example. It introduces a fictional scenario so generic it adds nothing to the reader's understanding.
Real examples have names, numbers, or specific outcomes. A real example is specific enough to be verifiable, or specific enough that someone who has done the same thing would recognize the scenario.
The fix: Replace each generic example with one real specific. Either from your own experience, from a case study you can link to, or from a publicly documented outcome. One real specific example does more work than three generic illustrative scenarios.
4. You Can't State the Article's Argument in One Sentence
Ask yourself: what does this article claim? Not what it covers — what it claims. If the honest answer is "it covers the main aspects of [topic]," that's not an argument. That's a description of coverage.
An article with an argument makes a claim that could be disagreed with. "Segmenting by purchase behavior matters more than segmenting by demographics for e-commerce email." "The conventional advice to 'write for your audience' fails because most writers don't know their audience specifically enough for the advice to be actionable." These claims are arguable. Coverage is not.
The fix: If you can't state the argument in one sentence, the article needs a different brief before it gets revised. Write the one sentence the article should be arguing, then check whether the existing content supports it. If it does with edits, edit. If it doesn't, the structure needs to change.
5. The Conclusion Is a Summary
The generic AI conclusion: "Now that you understand [topic], you can apply these strategies to achieve [outcome]. Remember to [first key point], [second key point], and [third key point] for best results."
This conclusion tells the reader what they just read. A reader who just read the article knows what they just read. The conclusion has one job: to add the one thing the body built toward but didn't quite say. An implication the argument leads to. A specific next action that follows from the article's central claim. The honest tension the article hasn't resolved.
The fix: Delete the summary conclusion. Write one or two sentences that extend the article's argument beyond where the body ended. If the article argued that brief quality determines AI output quality, the conclusion might be: "Which means the bottleneck in your content operation isn't generation speed — it's how quickly you can develop a specific, arguable position on a new topic. That's a different kind of throughput problem."
6. Every Claim Is Hedged to the Point of Saying Nothing
"AI content can be effective in some situations, though results may vary depending on your specific circumstances." This sentence is technically accurate about almost anything. It conveys nothing.
Generic AI content hedges systematically because the model is trained to avoid false claims, which makes it reluctant to commit. The result is content that qualifies every statement without ever landing on a position. A reader finishes the article with the same uncertainty they arrived with.
The fix: In the editing pass, mark every instance of "may," "might," "in some cases," "depending on your situation," and "many experts believe." For each one, ask whether the qualification is genuinely necessary or whether the article is just avoiding commitment. Replace the unnecessary hedges with the specific claim the section is actually making.
7. The Subheadings Read Like a Table of Contents
"What Is [Topic]," "Why [Topic] Matters," "How to Get Started With [Topic]," "Common Mistakes to Avoid," "Final Thoughts." This is not a structure — it's a template applied to a topic. Every section heading could appear in any article on any subject in the niche.
Specific subheadings advance an argument. They tell the reader not just what the section covers but what it demonstrates. "Why Your Segmentation Strategy Is Producing the Wrong Results" is a subheading with a claim. "Email Segmentation Strategies" is a subheading with a topic.
The fix: Rewrite the subheadings first. Make each one a specific claim or a specific answer to a specific question. The prose within the sections often gets better just from having a more specific heading to write toward — the heading focuses the generation and editing in a way that topic labels don't.
8. Nothing in the Article Required Knowing Anything Specific
The test: could this article have been written by someone who had never worked in or studied this subject, using only what's generally available on the internet?
If yes, the article has nothing in it that required judgment, experience, or expertise. It's a synthesis of what other articles have already said, organized into a structure. Readers who finish it leave with no information they couldn't have found elsewhere, and Google's systems, trained on behavioral signals at scale, know this.
The fix: Identify one thing you actually know about this topic that isn't in the top-ranking articles. One edge case. One finding from your own testing. One place where the standard advice breaks down based on real experience. Add it. One piece of specific knowledge anchors the rest of the article and changes the category it belongs to — from coverage to contribution.
The consistent theme across all eight signs: generic content is the output of a process that didn't require anything specific as input. The fixes are all upstream — a more specific brief, a more specific argument, a more specific editorial pass. An AI tool asked for something specific will produce something specific. An AI tool asked for "an article about [topic]" will produce the article about the topic that already exists. The content is only as specific as the question it was asked to answer.