AI-written content: review, source, sign

An AI model drafts an article, a newsletter, an offer page or a series of posts in seconds. The time saved is real, and it is tempting to publish as is. But a text published in the company’s name commits it: its numbers, its promises, its proper names. What separates useful content from content that costs dearly is not the tool that wrote it, it is what happens between the first draft and publication.

This guide describes those three steps (review, source, sign) for marketing teams of 20 to 500 people. It extends our guide to marketing automation for SMEs, which places AI among the other marketing tools.

Why a generated text does not go out as is

A language model, whichever one, produces the most likely sequence of words from what it has learned and what it is given. It writes with confidence, including when it is wrong: an approximate date, a plausible number, a client that does not exist. Google, whose documentation acknowledges that generative AI can help research a topic and structure original content, says it plainly.

The same page warns against using generative tools to produce many pages without adding value for users, which Google treats as abuse. A reviewed, enriched text is not targeted; a site full of texts nobody has read is.

Review: what the review checks

Reviewing a generated text is not proofreading: the model makes few spelling mistakes. It is checking what the model cannot know.

• Facts. Proper names, dates, job titles, software versions, legal texts. A dated fact is checked again at publication time, not only at writing time.
• Numbers. Each one must exist somewhere other than in the text (see below).
• Promises. A deadline, a result, a price, a guarantee: the model writes them readily, and the company will have to keep them.
• The brand’s tone and position. The model drifts toward the average sentence; the review puts back your point of view, your words, your refusals.
• What is missing. The lived example, the in-house number, the honest limit: what no model can invent without lying.
• Metadata. Google points out that the review extends to the title, the description, structured data and image alt text, which can appear in its results.

The writer or subject expert who reviews is not an end-of-line proofreader: they are the one who knows. That is why AI does not replace writers; it moves their time from the blank page to the angle, the checking and the experience.

Source: every number has an origin

The rule fits on one line: a number without a source is not published. The source can be internal (your CRM, your accounts, your analytics tool, with the date of the reading) or external (a study, official documentation, with the link). Either way, readers must be able to tell where the number comes from, and you must be able to find it again in six months.

Sourcing also protects against numbers that look too good. A fact-check audit of Palmora Property’s articles brought rental yields advertised at 8 to 12% down to about 5.8%, a figure sourced from Colliers, a real estate advisory firm, across six articles published in five languages. A wrong number, once translated, has to be corrected as many times as there are languages.

The safest approach is to reverse the order: give the model its sources before it writes, rather than looking for them afterwards.

Automated checks do not replace the review, they focus it on what requires judgement. On this blog, a script rejects a block of numbers without a source, and a generated article is born as a draft: it is the human review that puts it online.

Sign: a person answers for the text

Signing means saying who answers for what is written. For readers, a byline makes the text verifiable: they know who they are dealing with. Google recommends it in its guide to helpful content: it strongly encourages adding accurate authorship information where readers expect it, and suggests explaining how automation or AI was used to produce content.

European law points the same way for one category of texts. Article 50 of the AI Act requires disclosure of a generated text published to inform the public on matters of public interest, unless it has undergone human review and a person holds editorial responsibility for it. The detail, and what must be disclosed in every case, is in our article on whether to disclose AI-written text and in the one on marking AI-generated content.

What AI does well in the chain

Once these three steps are in place, AI saves a great deal of time, whichever model you choose:

• Preparing: suggesting an outline, grouping interview notes, summarising a long document.
• Adapting: turning an article into a newsletter issue or into posts for LinkedIn.
• Translating: on Palmora Property, switching to an AI model cut translation costs by a factor of 60, and 95% of segments then come from a cache. The review can then focus on the pages that sell, as our guide to international SEO explains.
• Repurposing: drawing an article, extracts and content offered for an email address from a webinar.

What it does not do: interview a client, live through a project, decide what the company thinks. A customer case study is written faster with AI, but its numbers come from the client. The split of roles between tool and writer is detailed in AI and digital marketing: what changes for agencies.

Organising the review within the team

Not everything needs the same level of review. A simple rule, written into the marketing plan, avoids both bottlenecks and texts nobody has read:

1. What commits the company (prices, deadlines, contracts, numbers, customer data): reviewed by the person with business responsibility for it.
2. What speaks for the brand (articles, pages, newsletters, emails to a list): reviewed and signed by its author.
3. What stays internal (notes, summaries, working drafts): light review, but no distribution without the two previous steps.

An email sent to a whole list deserves the same attention as an article: our method for running an email campaign lists the checks before sending, and segmentation avoids sending a text to people it does not concern.

Measuring what content brings in

Producing faster only helps if what is produced pays off. Track, for each piece of content, the contacts it brings and what becomes of them: our guide to automated marketing reporting connects those numbers, marketing attribution says which content weighed in a sale, and the marketing-to-sales handoff rule says when a reader becomes a contact to call back.

Where to start

Write the rule in three lines: who reviews what, what source for a number, who signs. Give the model its sources before it writes. Add a simple automated check (no number without a link or internal reference) and stick to it on the next piece of content.

For a content chain where AI drafts, checks verify and a person publishes, our digital marketing offer starts from your sources and your CRM.

Frequently asked questions

Sources

1. Google Search Central, creating helpful, reliable content (new tab)developers.google.com
2. Regulation (EU) 2024/1689 on artificial intelligence (new tab)eur-lex.europa.eu