Google does not prohibit publishing AI-generated content. The risk arises when a business publishes inaccurate, repetitive material with no value of its own. Before uploading a page, check its claims, add real business knowledge, review titles and structured data, and decide who owns the final version. Human review matters when reliability is the aim.
What has changed in Google's recommendations?
Google's guidance on AI content, updated on 1 October 2026, stresses manually checking reliability before publication. That includes metadata and images. Its documentation updates help establish when guidance changed; they do not demonstrate an effect on a particular website.
For a small business commissioning an agency, using a writing assistant or generating service descriptions, the practical question is who checks material published in its name. A model does not know your current trading conditions, operational limits or changes you have not announced. Fluent writing does not fill that gap in knowledge.
Which errors can AI-written content conceal?
As a hypothetical example, imagine a maintenance business providing support from Monday to Friday. It asks a tool to write a page targeting urgent repairs. The draft sounds convincing but promises permanent availability, lists brands the business does not handle and describes guaranteed repairs. It looks professional while creating promises the company cannot fulfil.
Errors are not always obvious nonsense. False precision is harder to spot: a neat figure with no source, a poorly summarised legal requirement, compatibility described as universal, or a commencement date taken from an announcement rather than the current rule. The more confident the paragraph sounds, the easier it is to overlook the missing evidence behind it.
Another problem is overlapping search intent. Five articles with different titles may answer essentially the same question. Their keywords vary slightly, yet readers find repeated examples and no new decision they can make. The website gains URLs without necessarily becoming more useful. Producing more pages does not compensate for repeating an answer already available elsewhere on your site.
Less visible errors matter too: an image shows a different tool from the one discussed, a testimonial is invented, FAQs exist in markup but not on screen, links lead to missing pages, or hypothetical examples appear as real clients. Consistency between what you promise and what you deliver matters more than the word count of an individual page.
How should you audit an article before publication?
A useful review involves more than asking another AI tool whether the draft is correct. It can help locate claims, but checking them requires sources and judgement. Work through the material in an order that makes errors easier to trace:
- Separate verifiable claims from opinions. Highlight dates, figures, features, obligations, compatibility statements and promises. Find primary documentation for each and record when you consulted it.
- Check statements about your business. Services, hours, availability, territories, team and conditions must match authorised information. Label a demonstration as a demonstration; do not sell a feature still in development as an existing implementation.
- Check the scope of each source. A beta feature does not establish general availability. An announcement about a regulatory change does not replace the current legal text. A screenshot of an old interface can also mislead.
- Look for internal duplication. Compare the main question with published URLs. If another article already resolves it, improve that resource or choose a genuinely different need for the new piece.
- Review the less visible layer. The SEO title should reflect the content, the description should not exaggerate, ALT text should describe the actual scene and JSON-LD should agree with the visible page.
- Test the route to an enquiry. Check links, mobile reading and the call to action. An accurate article leading to a broken form is unfinished work, even if the writing itself has passed review.
Keep a small verification record with columns for claim, source, date, reviewer and decision. It need not become bureaucracy. Its purpose is to help you correct material when a rule or provider changes. Traceability also reduces arguments about who approved a sentence: the decision is recorded rather than reconstructed from memory or an old conversation.
| Element | Review question | If it fails |
|---|---|---|
| Fact or rule | Is there a current official source? | Correct, qualify or remove it |
| Commercial promise | Can the business deliver it? | Rewrite without exaggeration |
| Example | Is it real or clearly hypothetical? | Label it clearly |
| Image | Does it illustrate the article's mechanism? | Choose a relevant scene |
| FAQ and schema | Do they say exactly the same thing? | Synchronise visible content and markup |
What original content does a B2B website need?
Useful service pages answer questions a general tool cannot resolve without knowing the business. What information do you need before starting? Which cases do you decline? How do you choose between adapting a tool and building a system? What happens when an integration fails? That operational knowledge is original content, even without impressive statistics attached to it.
Think of an advisory firm receiving documents from companies. A generic page explains that automation saves time. A useful one describes which documents tend to arrive incomplete, how missing fields are identified, when a correction is requested and why someone validates the result before submission. Explaining those criteria does not require disclosing client data or confidential procedures.
In May 2026, Google published a resource on optimising presence in generative search experiences. It highlights original material and the continuing importance of SEO fundamentals. GEO is no justification for manufacturing answers. It is another reason to define terms clearly, explain limits and make published information easy to check.
How do you organise an editorial workflow you can maintain?
Begin before the first paragraph with an inventory of published topics, a main query and the reader's problem. Research, outlining, drafting, factual checking, image selection, technical SEO and approval then follow. Reversing that order can produce attractive photographs for articles that still lack a central idea. The visual and the text should serve the same explanation.
Give each decision an owner, even in a small team. One person can write and another review. If both jobs fall to the same person, separate the sessions and use a checklist. Do not label text verified merely because an automated tool processed it. A review should leave evidence of what was actually checked and what remains unresolved.
Define an update procedure as well. Articles about platforms, privacy and features need a review date and a way to notice changes to their principal sources. They do not all require monthly rewrites. Someone should nevertheless be able to identify which paragraphs would become outdated if the provider changed its conditions, and which claims would then need another check.
How do we approach this at meiika?
At meiika, we approach websites as business tools, rather than collections of interchangeable phrases. Before writing or structuring a page, it makes sense to identify the service offered, the problem it addresses and the information a person needs to decide whether to get in touch. That also gives a reviewer something concrete to assess.
We connect that approach with Growth & MarTech: content, measurement and the route to an enquiry should tell a consistent story. We do not present client results or figures we cannot verify. A useful editorial audit starts by identifying doubtful claims, duplication and promises that the business cannot sustain, rather than applying a supposedly universal formula.