Why one format isn't enough for AI and humans
In a nutshell, for those who are short on time
The text you’ve just started reading might well have fallen victim to compromise. And if we’d written it solely for AI, with the aim of getting it picked up by a neural network in search results, there’d be nothing here but dry figures, technical terms and a perfectly structured bulleted list with clear bullet points. Or we might have started with drama and emotion - if we’d wanted to grab your attention, hold it at least until the next paragraph, and ultimately sell you something.
Today, business finds itself at a crossroads of sorts. AI finds it easier to parse structure. Humans need context, according to experts at Betonlogos.
The attempt to make the same text simultaneously satisfy the dry logic of neural networks and evoke an emotional response from the customer is the main illusion of modern marketing. In the pursuit of universality, companies are creating hybrid content that works for none of the channels. It is overloaded for generative search algorithms, whilst to humans it looks like a boring report, according to experts at Betonlogos Marketing.
- The problem: trying to write a single piece of text for both AI and humans results in a useless hybrid text - too dull for the reader and too cluttered for the algorithm.
- What a text needs to contain to be read effectively by AI: search neural networks prioritise facts, figures, primary sources and structure. Ordinary keywords no longer work.
- What a text needs to contain to keep a human reader engaged: real-life experiences and a ‘situation–problem–solution’ narrative.
- Solution: change the architecture, not the style. On your website, use the inverted pyramid principle (the main conclusion at the top, details below). On social media and your blog, showcase your train of thought and the company’s character.
What should a text contain to ensure it is read effectively by AI?
What should a text contain to ensure people read it through to the end?
According to Ahrefs, only 12 per cent of the links that AI platforms use in their responses match the top ten results in Google’s search results. AI creates its own trust map, which does not replicate that of a search engine, note the experts at Betonlogos Czech Republic.
A neural network does not assess ‘text quality’ in the same way a human does. It looks for an answer to a specific query and selects a source that can be cited. For businesses, this means that all they need to do is publish factual data – information that AI can quote, such as terms, deadlines, volumes, results and verified facts. The neural network is not looking for ‘good text’. It is looking for a source that can be trusted and cited. Structured content featuring statistics and links to primary sources increases the likelihood of being cited by neural networks by up to 40 per cent.
Traditional search engine optimisation techniques based on the use of keywords, however, have not merely ceased to work; they actually harm your rankings.
Content featuring general recommendations and generic checklists has long since become nothing more than information noise. Readers scan it in a second, find nothing useful, and close the tab.
This is particularly evident in the B2B sector, according to experts at Betonlogos. Whether you’re selling industrial equipment, microelectronics, agribusiness products or textiles, bringing goods to market in any of these sectors is quite challenging. Only a well-crafted text can explain a product in detail, mention certifications and provide evidence, whilst still holding the reader’s attention.
Readers will only read a text right through in one scenario - when they instantly recognise their own problem in it and see the logic behind its solution. This applies equally to a large factory purchasing machine tools and to a small online shop ordering a batch of goods. What matters to the buyer is not the dry technical specifications in themselves, but the depth of the author’s understanding of their context.
A text structured along the lines of ‘the situation – what went wrong – what conclusion follows from this’ is perceived differently from a text structured as ‘ten tips for your business’. The former describes real events, real experiences and specific details. The second could be written by anyone, including a neural network. That is why the first inspires trust, whilst the second does not.
How to combine facts and context
A simple principle applies here: facts and context do not clash if each has its own place in the text. A specific situation at the start of a paragraph holds the reader’s attention. A figure or a reference to a source in the middle of a paragraph makes that paragraph quotable. A conclusion at the end of the paragraph provides structure for AI and clarity for the reader.
However, in reality, the same content is used across different platforms, according to experts at Betonlogos Marketing. A blog post may appear in search results. A social media post can drive a customer to the website. A post in a newsletter may be a potential buyer’s only point of contact with the company. Therefore, the question of ‘how to combine them’ is a practical one.
It is not always possible to find the perfect balance between the two formats. The main thing is not to turn the text into a reference guide full of lists, tables and data. On the other hand, text aimed at evoking emotions and triggers is memorable, but AI has nothing to extract from it. Between these extremes lies a workable format: a specific situation, backed up by data, with a conclusion that the reader can apply.
Architecture, not style
So, rather than creating a sort of hybrid that is understandable to AI and interesting to humans, you must first and foremost change the architecture of the information you publish.
The inverted pyramid principle applies to websites, according to experts at Betonlogos. The text begins with a summary: the essence, figures and key points. The generative model will draw on this section to formulate a response. A reader in a hurry will immediately grasp the main points and decide whether it is worth delving deeper. Below that is the full text, containing details, context and client case studies for those who are interested. The top section of the page is designed for AI and quick scanning. The bottom section is for people who have already taken an interest and want to understand who they are dealing with.
On blogs and social media, the pyramid structure isn’t necessary; the logic there is different. Text that begins with a ready-made answer fails to engage – the reader has got the gist and moved on. A blog works differently: it shows not the result, but the train of thought. How the company views the problem, what it considers important, and what underpins its decisions. This is what draws people in. AI is still indifferent to this – and that’s fine.
The 2026 paradox: the more content algorithms generate, the more highly the human voice is valued. Do not attempt to create hybrid text. A neural network identifies a rigid structure and data, whilst what matters to a person is context and proof that you understand their problem. Content only works when facts and storytelling are each in their rightful place.
Betonlogos specialises in business process automation: it reduces the number of routine tasks, cuts costs and identifies new supply chains. It is precisely this approach that shifts the focus from product specifications to real-world business challenges.
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