AI and content.

What AI can really do

A neural network is not an author and not a replacement for a specialist. It is a tool that quickly does the rough part of the work: builds a structure, suggests wording options, rewrites text for a different format, prepares a dozen drafts instead of one.

Models are trained on large bodies of data, so they reproduce the generic well: a product description, a post from a template, a headline option. For exactly that reason they do the unique badly — what was not in their data: your experience, your figures, your specific case.

Hence a working division of labour. The machine takes on volume and drafts; the human takes on facts, meaning and responsibility for the result.

Text

This is the most mature area. AI generates articles, product descriptions, posts for social media and blogs, rewrites long text into short and vice versa, and comes up with headlines.

The best results come where you give the model material rather than asking it to invent from scratch. A draft with your key points, a transcript of a conversation with an expert, a list of facts — from raw material like that you get text that only needs editing. The request “write an article about our services” produces polished nothing.

A practical rule: the more detailed the task, the fewer the edits. The prompt should include the audience, the goal, the tone, the length and what must not appear in the text.

Images

Image generation covers the need for illustrations when a photo shoot is unnecessary or impossible: article covers, abstract illustrations for sections, backgrounds, composition options for a designer.

The limitations are clear too. The model copes poorly with text inside an image, with precise diagrams and with depicting specific real objects — your office, your product, your staff. Where authenticity is needed, you shoot.

For consistency it makes sense to keep a set of proven style descriptions and reuse them: otherwise the illustrations in the blog look as if they were gathered from different publications.

Video and voice-over

Video generation tools make it possible to produce animation, short clips and training materials, as well as to voice text with a synthesised voice and assemble subtitles. The main benefit is reduced time and cost for producing simple videos.

Realism here is still inferior to filming, and complex storylines need manual refinement. So the sensible use is a supporting one: explanatory inserts, animated diagrams, versions of a clip for different platforms, subtitles for material already shot.

Where the machine goes wrong

The main danger is confidently worded untruth. The model can invent a figure, a source, the name of a law or someone else’s case study, and it will look convincing. Anything that resembles a fact is checked by hand, without exception.

The second problem is averageness. The texts come out smooth and identical: the same turns of phrase, the same structure, the stock openers about modern times and an experienced team. Readers recognise this instantly, and it adds no trust.

The third is losing your voice. The material stops being different from a hundred similar ones, and that difference is precisely the reason people come to you.

How to build it into the workflow

The working scheme is simple: the human sets the task and provides material, the AI prepares a draft, the human edits and adds what the machine cannot have.

• Facts and figures — only your own, verified. Not a single figure from the model’s answer goes into a publication as is.
• Experience — cases from practice, mistakes, details of the process. This is what cannot be generated.
• Voice — editing to match how you usually talk to a client.
• Checking — read it aloud. Anything that sounds like advertising about nothing gets cut.

It is useful to build up your own assets: successful task wordings, style descriptions, a list of banned words. Over time this saves more than the generation itself.

AI and search

Search engines do not ban text created with AI — they assess the usefulness of the material. What ranks poorly is not machine text as such but empty text: with no answer to the question, no experience, no specifics.

There is a flip side too. More and more answers users get directly in search and in chat models without visiting websites. To be mentioned in such answers, your material must be structured and substantive — with clear wording, answers to specific questions and data that can be quoted.

Frequently asked questions

Can AI text be published without editing?

Better not. The minimum is fact-checking and editing to match your voice. Without that the material is indistinguishable from thousands of similar ones.

Will AI replace the copywriter?

It takes over the rough work, but not the expertise. The value of a text is created by the person who knows the subject and answers for what is said.

How to quickly tell generated text?

By smoothness without specifics: general advice, no examples or figures, repetitive opening phrases. Living text always has something to hold on to.

Is AI suitable for product descriptions?

Yes, it is one of the most successful tasks: you supply the specifications yourself, the model expands them into text. It still needs checking.

What to do next

Take one recurring task — product descriptions, announcements, answers to typical questions — and try putting together a detailed brief for the model. The savings are visible immediately, and the risk is limited to one format.

We prepare content and promote it on social media — that is SMM. Next, you may find useful our pieces on how to win clients from search that answers with AI and how to write engaging content.