“ChatGPT, write me a LinkedIn post about AI.” Just three years ago, we were impressed by the near-instant output that generative AI could produce from such a simple prompt. The AI revolution was just beginning to gain momentum, opening the door to an effortless world of seemingly endless possibilities for content creation.
Today, we have reached an important turning point.
There is a very high chance that the same prompt will still generate a grammatically flawless text. There is, however, an equally high chance that no one will read it to the end. We can either continue using AI for writing the same way we did three years ago and receive a polished yet interchangeable piece of content, complete with long em dashes, a neat list of three to five bullet points, and a generic call to action at the end. Or we can use AI as a sparring partner while maintaining our credibility. Because great content is not determined by speed. It is determined by the quality of the collaboration between humans and AI.
The share of AI-generated content on the web continues to grow steadily. An analysis by Ahrefs shows that 74.2 percent of all webpages published in 2025 contain at least some AI-generated content. An updated study by Graphite concludes that around half of all newly published online articles are now created primarily by AI.
This creates a cycle that is becoming increasingly relevant for content strategies. AI generates content, search engines and platforms evaluate it, language models use it as the foundation for new responses and content, and the cycle repeats itself. Researchers refer to this phenomenon as the AI feedback loop, in which AI systems increasingly rely on content that was itself generated by AI. This encourages the spread of repetitive and redundant material commonly referred to as AI slop.
Anyone publishing content today is no longer competing only with other companies, but with millions of algorithmically generated pieces of content that appear every single day.
Why AI-Generated Content Often Falls Short
Poor AI-generated content rarely fails because of grammar. It fails because it lacks relevance.
Typical patterns include generic introductions, predictable wording, overly polished language, unjustified confidence when facts are uncertain, hallucinations, mechanical translations, poor prioritization, and a tone that could fit anywhere, which ultimately means it resonates nowhere.
The problem becomes even more significant in specialized subject areas. AI can invent convincing-looking studies, statistics, or sources. It may rely on outdated assumptions, confuse markets, or translate English technical terminology so literally that the result appears correct while failing to sound like a professional article written for a German-speaking business audience.
This is exactly where the difference between content generation and editorial work becomes apparent.
According to LinkedIn, AI-generated posts are not penalized across the board. However, the platform recommends reviewing, editing, and approving AI-generated content before publication.
In practice, generic AI content can still lose visibility. LinkedIn evaluates posts based on quality, relevance, and engagement, indirectly reducing the reach of AI slop. New posts are initially shown to a smaller audience, and strong early engagement signals can significantly increase further distribution.
Anyone publishing content that sounds like every third AI-generated LinkedIn post risks shorter reading times, fewer meaningful comments, and weaker relevance signals.
The algorithm primarily evaluates factors such as:
Lack of relevance: Content must provide genuine value and meaningful context for its intended audience.
Generic language: Phrases that are obviously generated by tools such as ChatGPT can be recognized.
Automated comments: Fully automated interactions are also penalized by the platform.
Other platforms are taking a stronger approach by labeling AI-generated content. Since 2024, Meta has been labeling AI-generated content on Facebook, Instagram, and Threads. TikTok requires labels for realistic AI-generated images, audio, and video. The rules for text-only posts are less clearly defined. What is becoming increasingly evident, however, is that platforms aim to make synthetic content more transparent while limiting the reach of low-quality mass-produced material.
AI in Everyday Work: Problematic or Full of Opportunity?
Recently, the German news magazine Der Spiegel stated that AI is used for research purposes in its editorial process, while every published article continues to be written exclusively by human journalists. For a news organization, this approach is both appropriate and necessary. The principle of “content written by people for people” should absolutely be preserved.
But what about companies in other industries, or employees facing long to-do lists and constant pressure to produce content?
In many cases, AI is not used out of convenience but because of limited time and heavy workloads. It helps teams manage tasks that would otherwise be impossible to complete.
Realistically speaking, the moral debate over whether AI should be used for writing has already been settled in most organizations. AI has become an integral part of today’s content workflow.
The discussion should therefore no longer focus on whether AI may be used for writing. The crucial question is how it is used.
Those who rely on AI merely to generate text will usually end up with generic content. Those who use it as a research assistant, structuring partner, and editorial sparring partner can develop content that may even outperform articles written entirely without AI. Achieving that requires a clearly defined workflow.
Better AI Content Starts Before the Prompt: A Six-Step Workflow
Anyone who wants high-quality results from AI should not begin with writing the article itself. The greatest improvement in quality happens much earlier.
Professional AI-assisted content creation is therefore not about a single prompt. It is about following a structured process.
Create the Right Context AI knows nothing about your company, your target audience, or your writing style. You need to provide that information. Start by defining the role the AI should take. Should it act as a content marketing expert, a specialist editor, a business journalist, or an SEO copywriter? Then provide the necessary context. Who is the target audience? What is the goal of the article? Which tone of voice should it use? Which terms or expressions should be avoided? The more precisely you describe the starting point, the less generic the result will be. OpenAI and Anthropic explicitly recommend defining the role, objective, audience, format, and quality criteria before introducing the actual topic.
Structure First, Write Second One of the most common mistakes is asking AI to write an entire article immediately. Better results are achieved when content is developed step by step. Start by generating an outline. Then define the key message of each section and add your own notes, data, or sources. Only once the direction and substance are right should the AI produce the first draft. This approach, known as prompt chaining, is now considered one of the most effective best practices for working with language models. Breaking complex tasks into smaller steps significantly improves both quality and consistency.
Work with Your Own Information The best content does not come from the AI’s knowledge alone. It comes from yours. Provide the AI with briefings, research papers, customer interviews, internal documents, or your own notes. Doing so greatly reduces the risk of hallucinations while ensuring that your content contains insights competitors cannot simply reproduce. A strong AI-generated article is therefore built on proprietary knowledge rather than relying exclusively on publicly available information.
Train Your Own Writing Style If you work with AI regularly, there is no need to start from scratch every time. Upload several articles you have already published and ask the AI to analyze your writing style. Your vocabulary, sentence structure, recurring phrases, organization, and way of arguing can then become permanent style guidelines for future content. It is also worthwhile creating a small editorial playbook that documents preferred terminology, words to avoid, and examples of successful introductions and headlines. According to both OpenAI and Anthropic, examples are among the most effective ways to achieve consistent results.
Turn AI into a Sparring Partner Don’t use AI only for writing. Ask it to formulate counterarguments, raise critical questions, or identify blind spots in your draft. Have it review your content like an editor-in-chief or subject matter expert, pointing out unsupported claims or sections that fail to provide meaningful value. Another useful technique is asking the AI what information it still needs in order to produce a better article. This change in perspective often leads to significantly better results than endlessly refining prompts.
The Final Polish Still Belongs to Humans The first draft is rarely the final version. Verify facts and sources, remove repetition, shorten lengthy passages, and replace generic wording with concrete examples or personal experience. Afterwards, you can ask AI to review the article for readability, SEO, clarity, or established copywriting frameworks such as AIDA. The most important task, however, cannot be delegated to a language model: editorial judgment. Only humans can ultimately decide which information is truly relevant, which claims are reliable, and whether an article genuinely provides value.
What Companies Should Take Away
AI does not replace editors or content creators. It changes the way they work.
Research, structuring, alternative versions, counterarguments, first drafts, and editorial sparring can all be supported effectively by AI. The original idea, the strategic perspective, and final responsibility, however, must remain with people.
For companies, this means that using AI simply to produce more content primarily increases the volume of interchangeable material.
Those who guide AI effectively, enrich it with proprietary knowledge, and apply rigorous editorial review can develop better content in less time.
At CURE, we combine artificial intelligence with human excellence.
We develop content strategies in which AI supports research, analysis, and structure, while professional expertise, editorial judgment, and quality remain firmly in human hands.
