Using artificial intelligence to draft an article, product page or corporate web page can save time at the start of the writing process. The problem begins when that first draft is treated as finished content.
A text can read well and still contain incorrect data, non-existent sources, imprecise claims or messaging that does not fit the brand. That is why any company using AI-generated content needs an editorial review process before publishing it.
This review is not just about correcting spelling and grammar. It should assess accuracy, usefulness, brand consistency, search intent and any potential risks associated with the content.
Why can convincing content still be wrong?
Generative models produce answers based on language patterns. They can write fluent, apparently authoritative copy, but that does not mean they have verified every statement or understand the company’s actual context.
Common errors include incorrect dates, features attributed to products that do not offer them, fabricated references, outdated regulations and conclusions that go beyond the available information. Contradictions can also appear within the same text, especially when different sections have been generated separately.
The level of risk depends on the type of publication. An inaccuracy in an educational blog post may damage the brand’s credibility. On a technical product page, service page or in content related to health, finance or legislation, the consequences can be more serious.
Creating content with artificial intelligence does not remove editorial responsibility. The company remains accountable for the information published on its website, even if a tool produced the first draft.
How should you verify AI-generated content before publishing it?
Verification should begin by separating checkable facts from interpretations. Names, dates, figures, features, quotations, regulations and results should be checked against reliable sources, preferably original or official ones.
A practical review can follow these steps:
- Highlight verifiable claims. Identify every sentence that presents a fact, requirement, comparison or cause-and-effect relationship.
- Consult the original source. If the content refers to legislation, review the official document. If it describes a product or technology, check the latest documentation.
- Check whether the information is current. Something that was correct a year ago may now be outdated, particularly in technology, taxation or digital platforms.
- Remove anything that cannot be confirmed. A questionable claim does not become reliable simply because it is well written. If it cannot be supported, omit it or rewrite it with appropriate caution.
- Review the content as a whole. After validating each fact, check that the complete text does not contain contradictions or unsupported conclusions.
Asking the same AI tool to check its output may help identify points that need attention, but it is not a substitute for consulting the sources. A model can repeat or reinforce the same mistake it introduced in the first place.
Does the content genuinely sound like your brand?
Accurate content is not always suitable content. AI tends to reproduce predictable structures, generic expressions and claims that could apply to almost any company. The result may be technically acceptable but difficult to recognise as your own and lacking a clear point of view.
The review should assess whether the content reflects the brand’s vocabulary, level of technical detail, tone and priorities. It should also identify exaggerated commercial promises, services the company does not provide or expressions that contradict the way it works.
This consistency cannot be achieved through a good prompt alone. It requires a clearly defined position, audience and set of editorial guidelines. A well-defined brand and content strategy establishes what should be communicated, how it should be approached and which boundaries should not be crossed.
It is also important to review how the text fits into the company’s wider identity. Content, tone and visual presentation should form part of one coherent branding and communication system, rather than operating as separate elements.
How should you review SEO for AI-generated content?
Repeating a keyword does not make a page a good search result. SEO for AI-generated content should start by understanding what the user is trying to resolve and whether the page provides a clear, complete and reliable answer.
Does the page genuinely match search intent?
The answer should be identifiable within the opening paragraphs. If someone is searching for a way to verify AI-generated content, they need a process they can apply, not a lengthy introduction to the general benefits of the technology.
Headings should accurately describe each section, repeated passages should be removed, and the most important information should not be buried beneath generic paragraphs. Keywords should appear naturally alongside relevant synonyms and related concepts.
Can AI define the entire SEO strategy?
No. It can help explore search terms, structure a draft or suggest questions, but it does not necessarily understand the company’s objectives, the actual competition behind each query or the role of the page within the wider website.
Before publishing, it is also worth reviewing the title tag, meta description, internal links, URL, image alt text and any potential overlap with existing pages. This last issue, known as keyword cannibalisation, occurs when several pages compete for the same search query without a clear distinction between them.
AI can speed up certain tasks, but greater speed does not guarantee better quality. The difference between producing more and working more effectively is also central to our analysis of the efficiency of AI in marketing.
What editorial process does a company need?
Not every piece of content requires the same level of scrutiny. A short social media caption does not carry the same risk as a technical specification page or an article about legislation. The process should reflect the potential impact of each publication.
A clear workflow can be divided into five stages:
- Brief: define the objective, audience, channel, available sources and person responsible for the content.
- Generation: use AI to prepare an outline or draft without treating it as the final version.
- Verification: check data, sources, dates, names and sensitive claims.
- Editing: adapt the text to the brand’s editorial standards, remove repetition and improve its clarity and usefulness.
- Approval: carry out a final editorial and SEO review by someone authorised to approve publication.
This workflow should be documented with a checklist and clearly assigned responsibilities. It is also advisable to keep a record of the sources used, particularly when the content includes technical information or details that may change over time.
When content is produced at scale, some checks can be automated, such as detecting empty fields, broken links, duplicate passages or incorrect formatting. Assessing accuracy, nuance, usefulness and brand fit, however, still requires human judgement. In our technology and artificial intelligence articles, we explore this distinction between what can be automated and what still needs human oversight.
The useful question is not whether a person or an AI system wrote the content. It is whether someone has checked its quality and is willing to take responsibility for it. Artificial intelligence can make the first draft easier; publishing with care remains an editorial decision.
We like to discuss these questions before they become a fully defined project. Tell us about your case.