Just a few years ago, AI texts were exotic. Today, they are a working tool used by everyone from freelance copywriters to large media editorial offices and marketing teams. AI writes quickly, evenly, without emotion and without fatigue. And this is where the main pitfall lies.
The problem is not that the text was created by a neural network. The problem is that it is increasingly being published without verification, like a draft that accidentally became the final version. Without an editor’s eye, without fact-checking, without asking, “Is it really okay to release this to the public?”
In practice, the consequences are almost always the same. The text contains inaccuracies or half-truths that sound convincing but do not stand up to scrutiny. The wording becomes too smooth and predictable, as if correct, but without any lively thought. And with this come problems with audience trust, SEO, and the reputation of the brand or media outlet.
That is why Western editorial offices and content teams today are not talking about “banning AI,” but about a separate AI review stage before publication. This is not censorship or a fight against technology. It is a return to the basic journalistic principle: any text, regardless of who wrote it, a person or an algorithm, must be checked before it appears on the website.
Rule number one: AI detectors are not a verdict, but a filter
When it comes to checking AI texts, the first reaction is usually the same: “Let’s run it through the detector and everything will become clear.” This is a logical desire, but this is where the main mistake begins.
All authoritative materials on AI content, from QuillBot to Screpy and Ukrainian service reviews, agree on one thing: AI detectors cannot say “yes” or “no.” They work with probabilities, patterns, and statistics. In other words, they do not determine the truth, but only signal a potential risk.
That is why a detector should not be perceived as a judge. Its role is more like a filter or a red flag. It does not answer the question “can this text be published or not.” It suggests something else: where exactly the text needs a more careful editorial eye.
Sources directly advise not to rely on a single tool. One service may show a low probability of AI, while another may show the opposite. This does not mean that one of them is “lying.” They simply analyze the text using different models. Therefore, the best practice is to check the material in two or three detectors and look not at the percentage, but at the patterns.
The most valuable thing in such a check is not the final indicator, but the paragraphs that are consistently marked as problematic. This is where the typical weaknesses of AI texts are usually hidden: overly uniform wording, template transitions, abstract generalizations without specifics. For an editor, this is not a reason to delete the text, but a signal: this needs to be reviewed manually.
Therefore, detectors occupy an important but not primary place in the AI review checklist. They do not replace the editor and do not make decisions. They only help to quickly find areas where “just okay” text can be made truly high-quality — human, accurate, and something you won’t be ashamed of after publication.
Structure and predictability: where AI gives itself away most often
If you don’t look at the percentages in the detectors, AI most often gives itself away not with words, but with structure. This is clearly seen in the observations of QuillBot and Screpy: neural networks write texts too neatly. So much so that this neatness begins to stand out.
A typical AI text looks like it has been edited before anyone has even read it. The paragraphs are almost the same length. Each thought follows logically from the previous one, without stops, without doubts, without pauses. Everything is correct, consistent, and… a little dead.
Another characteristic feature is universal wording that seems to be suitable for any topic and any audience. Phrases such as “It is important to note that…” or “In conclusion, we can say…” are not wrong in themselves. But when they are repeated from text to text and have no specific meaning, it is almost always a sign of machine origin.
For the reader, such predictability works against the text. They quickly recognize the pattern, stop thinking, and move their eyes on. For the editor, this is a clear marker: the text looks too correct, too smooth, too safe.
Therefore, the next item on the checklist is to check the text for this “perfection.” Are the paragraphs too symmetrical? Can phrases that sound like an introduction or conclusion “for the sake of it” be removed or rephrased? Are there lively transitions, clarifications, and local context in the text that do not look universal?
At this stage, editing often boils down to simple things: shortening template constructions, mixing the rhythm of paragraphs, allowing the text to be uneven. Because in real journalistic material, thoughts rarely follow a perfect line. And it is this unevenness that makes the text lively and convincing.
Facts, dates, statements: the area of greatest risk
While the structure and style of AI text can still be corrected through editing, the situation with facts is much more complicated. According to observations by Libril and Munro Agency, this is where neural networks make the most frequent and dangerous mistakes.
AI knows how to speak with confidence. So confidently that even a dubious or inaccurate statement sounds like a proven fact. This is especially true for numbers, dates, and cause-and-effect relationships. The model can round off data, mix different studies, or logically “think up” a connection where it has not actually been proven.
As a result, the text looks convincing, but it is not always true. And this is the key point. The reader has no sense of error until they begin to check the information themselves. But for the media, a brand, or an author, such an error can cost them credibility.
That is why all sources emphasize a simple rule: any fact in an AI text is considered unverified by default. If a number can be verified, it must be verified. If a date can be confirmed, it must be confirmed. If a statement sounds too general or categorical, it is worth finding out what it is based on.
In the checklist, this means manually checking everything that has specific content. Search for primary sources, official reports, or authoritative publications. And in cases where sources cannot be found, the editor must do something simple but honest: either soften the wording or remove it from the text altogether.
AI can help formulate an opinion, but the responsibility for the facts always remains with the person. And it is at this stage of verification that it is decided whether the text will become a full-fledged journalistic material or remain a beautifully written but dangerous draft.
SEO verification: when AI is harmful
AI is often presented as the ideal assistant for SEO. It knows keywords, can build structure, and quickly creates texts based on queries. But materials from Libril and LinkedIn Content Checklist show the other side of the story. Without editorial review, AI can easily turn from an assistant into a source of SEO problems.
The most common mistake is oversaturation. AI strives to be useful and therefore generously scatters keywords throughout the text. As a result, the material looks optimized, but is difficult to read and quickly tires the reader. The second typical problem is headlines. They are formally correct, but do not carry real value for the reader. And the third mistake is ignoring search intent. The text seems to respond to the query, but does not solve the person’s real problem.
It is important to understand one principle that sources emphasize. Google does not evaluate the fact of using AI. It evaluates quality, usefulness, and relevance to user expectations. If the text does not answer the query or looks artificial, no amount of keywords will save it.
Therefore, SEO verification in AI review is not about technical details, but about meaning. In the checklist, it looks like this:
- check titles and subtitles for semantic value, not just for the presence of keywords;
- analyze whether the text really corresponds to what the user is looking for;
- cut down on SEO fluff, repetitions, and phrases that exist solely for optimization.
At this stage, the editor actually puts themselves in the reader’s shoes. Would this text answer my query? Would I want to read it to the end? If the answer is no, then AI has done its part, and there is still work for humans to do.
All the materials on which this article is based repeat the same idea. AI is not a problem in itself. The problem is the absence of a human being between generation and publication.
Neural networks have already become part of the workflow. They help to write faster, structure thoughts, and find the right wording. But they do not take responsibility for the accuracy, meaning, and consequences of the published text. This responsibility always remains with the human being. With the copywriter, editor, media, or brand.
That is why high-quality AI content is not just a matter of one click. It always involves three stages:
- generation as a starting point;
- checking as a risk filter;
- editing as the final responsibility.
The checklist in this process is not bureaucracy or unnecessary control. It is a tool that brings editorial logic back to the text. It helps to see weaknesses, ask the right questions, and not release material into the world as a raw draft.
Ultimately, it’s not about fighting AI. It’s about maintaining standards. It’s about ensuring that any text, regardless of who or what wrote it, remains high-quality, accurate, and something you won’t have to apologize for after publication.