A draft that costs a few cents can cost an hour to approve. If the editor has to rebuild the argument, check every example and strip invented promises, the price of the draft tells you nothing about the price of publishing it.
The volume is already here. Ahrefs found that 74% of 900,000 pages published in April 2025 contained AI-generated content. And Google’s spam policies name scaled content abuse: many pages generated to manipulate rankings without helping users, however they were created.
So the question for a marketing lead is how to use AI for content without publishing slop. This guide describes the practice we run: expertise in, drafts out.
The expert supplies facts, sources and positions. The machine supplies prose. Checks and a critic reject what fails. A person approves what’s left, and AI content quality is measured as cost per approved article.
Why generated slop fails readers and search
Slop is text that reads plausibly and gives the reader nothing they can use. It is easy to produce and easy to recognise, and the tells are consistent enough to list.
| Symptom | What the reader does | What search systems do |
|---|---|---|
| Generic claims with no numbers, names or sources | Stops trusting the page and the brand behind it | Treat the page as adding little beyond existing results |
| Invented statistics or sources | Finds out, sometimes publicly | Trust signals on the whole site weaken |
| Confident errors about your own product | Enquires, then finds the promise was false | The enquiry becomes a complaint |
| Uniform sentence length and stock phrases | Skims, then leaves | Engagement signals fall |
| The same page with a name swapped in | Notices the copy on a competitor’s site | Google’s scaled content abuse policy applies |
Google’s guidance on helpful content turns this into questions a publisher can ask before publishing. The useful ones for a business site are these:
- Does the page give original information, reporting or analysis? Or does it rephrase what already ranks?
- Does it show first-hand expertise? Someone who has done the work, with the exception or the trade-off a buyer can’t see from a feature list.
- Would you expect to see it referenced by a printed magazine or a book?
- Who wrote it, how was it made, and why? Google puts trust first among experience, expertise, authority and trust, and says the “why” should be to help people.
None of those questions asks whether a machine typed the words. They ask whether an expert stood behind them. That is the design constraint for the rest of this guide.
Expertise in, drafts out
The practice is a gate, and the gate has an order. The expert’s work happens before the draft exists. The machine’s work happens in the middle. The checks run before any person reads. And the person decides at the end.
In practice it runs as six steps:
- Write the brief. The reader, the decision the article supports, the facts the business can prove, the sources with dates, and the positions the business holds.
- Draft from the brief only. The model may structure and phrase. It may not add facts, numbers or sources the brief doesn’t contain.
- Run the writing checks. Deterministic rules: banned phrases, sentence shapes, paragraph length, heading case, rhythm. A fail sends the draft back with the exact lines.
- Run the critic. A second model, on a different model family, reads the draft against the brief and lists unsupported claims, drift and gaps.
- Revise within a bound. Two rounds against specific findings. A third round means the brief is missing something, so a person fixes the brief.
- Approve or reject. A named person reads the draft with the findings and the sources and records the decision.
Each step has a cost and an owner, which is what makes the economics later in this guide possible to calculate.
What goes into the brief
A brief written as “write about choosing a support platform” gets a generic overview back. A brief that says “help an operations lead decide which enquiries need a person before buying an automated support tool” gets a decision guide. The difference is everything the expert knows and the machine doesn’t.
Before drafting, the brief should contain:
- The reader and what they already know. A COO comparing options, or a customer completing a task.
- The decision the article supports. One sentence. If you can’t write it, the article isn’t ready.
- Facts the business can prove. Product capabilities confirmed by their owner, results with permission to publish, prices with dates.
- Sources with dates and the passage that matters. A link on its own invites the model to paraphrase the whole page.
- Positions the business holds. What you would refuse to recommend, even when it would make a better sales paragraph.
- The limits of the advice. Who the recommendation doesn’t suit.
| Proposed material | Evidence to collect | Editorial decision |
|---|---|---|
| Product capability | Current documentation and confirmation from its owner | State the conditions and limitations |
| Customer result | Approved measurement, time frame and naming permission | Publish only the result the evidence supports |
| Market statistic | Original research, population and method | Keep its scope, label it illustrative, or leave it out |
| Expert recommendation | Reasoning, relevant experience and counterexamples | Explain when a different choice makes sense |
Keep confidential notes and customer data out of the drafting tool unless the agreement covers them. Removing a name is often not enough when the remaining details identify the customer. Our AI governance service covers the access rules for this kind of workflow, and the AI compliance policy generator produces a starting policy for what may enter it.
Editorial rules that catch machine tells
Machine prose has habits. They are consistent enough to write as rules and check by program, before anyone spends review time. The rules below are the ones that catch the most in our own editing, and each one is deterministic: a script either finds the pattern or it doesn’t.
| Rule | What it catches | Why it matters |
|---|---|---|
| Banned phrases | Stock openers, buzzwords and throat-clearing, from a list the team maintains | Readers recognise them and stop reading |
| Sentence shapes | “It’s not X, it’s Y”, “Not X. Not Y. The Z.”, “X beats Y”, three-item rhythm at the end of every paragraph | The shapes perform rather than explain |
| Paragraph length | Anything over 60 words | Long paragraphs hide the point from a reader scanning on a phone |
| Sentence rhythm | Twenty sentences of the same length in a row | Uniform rhythm reads as machine output even when the words are fine |
| Contractions and spoken openers | Fewer than a handful of contractions per thousand words | Instruction-tuned models write “do not” where a person says “don’t” |
| Heading case and punctuation | Title case headings, em dashes, emoji | House style, and the em dash is the most recognised machine tell |
| Structure shape | Missing takeaways, an FAQ with one-line answers, bold scattered mid-sentence | The reader’s skim path breaks |
The phrase list starts with the ones readers recognise on sight: “in today’s fast-paced world”, “delve”, “seamless”, “game-changer” and “it’s worth noting”. It grows every time an editor catches a new one.
The rules are deterministic, so a fail names the line. A draft goes back to revision with this list, and no reviewer reads it until the list is empty.
These rules cost nothing per run. So they run first, on every draft, and a fail returns the line numbers. Nobody reads a draft that still trips them.
They also have a limit. A paragraph can pass every rule and still use an accurate statistic to support the wrong conclusion. The checks find tells. They do not find truth, which is what the next two steps are for.
The critic pass and bounded revisions
A critic is a second model, on a different model family from the one that wrote the draft, given the brief, the sources and the draft. Its job is a list of findings with locations. It never rewrites.
Ask it for specific failures:
- Claims with no support in the brief or the sources.
- Numbers, names or sources that appear in the draft and nowhere in the brief.
- Sections that don’t answer the reader’s decision from the brief.
- Positions that drift from what the business said it holds.
- Places where a hedge in the source became a certainty in the draft.
The revision loop is bounded on purpose. Two rounds against specific findings fix most drafts. A third round means the brief lacks a source or a position, and regenerating won’t supply either. So the third failure goes to a person, who fixes the brief or stops the article. Repeated generation is a poor substitute for a missing fact.
Fact and claim review
The critic reduces the reviewer’s work to a list. The reviewer still owns the decision on every claim class, because each class has a different way to be wrong.
| Claim class | Who confirms it | Evidence | Rule |
|---|---|---|---|
| Product capability | The product owner | Current documentation | State the conditions. “Available on request” is not “available” |
| Customer result | The account owner and the customer | Measurement and written permission | Publish the measured figure and the time frame, nothing rounder |
| Market statistic | The editor | The original study, its population and date | Label it as a typical range or an example, and link it on first use |
| Recommendation | The subject expert | Reasoning and counterexamples | Say when the other choice is right |
| Legal or compliance statement | Whoever owns compliance | The regulation or the adviser’s note | Describe the obligation. Never state that a reader complies |
| Price | Finance or sales | The current price list | Include the date, or leave the number out |
Google also suggests giving readers context on how content was produced. A short line on who briefed the article, what was checked and who approved it says more than a disclaimer.
Human approval
The approver reads the final draft with the critic’s findings, the sources and the brief beside it. Their job is the decision, and the recorded decision is the evidence that the process ran.
What the approver does:
- Reads the argument as the intended reader and decides whether it helps them decide.
- Confirms each claim in the table above has its evidence attached.
- Checks the positioning against what the business holds.
- Records approval or rejection against that exact version, with a reason on rejection.
What the approver no longer does: hunt for banned phrases, count paragraph lengths or reformat headings. The checks did that. A reviewer who spends twenty minutes on an article with a finding list usually spent an hour without one, and that difference is where the economics come from.
The economics of cost per approved article
Divide the total cost of a production batch by the number of articles approved. Rejected drafts stay in the numerator. If nothing meets the standard, report zero approvals and the cost incurred rather than a misleading unit cost.
The example below is a worked scenario with assumed figures. It is neither a customer result nor a quote for any tool. Assume ten articles start, eight are approved and labour costs EUR 60 an hour.
| Work across the batch | Assumed effort or charge | Cost |
|---|---|---|
| Research and briefs, ten articles | 5 hours | EUR 300 |
| Model and tooling charges | Illustrative allocation | EUR 20 |
| Critic findings read and revisions checked | 3 hours | EUR 180 |
| Expert and editorial approval | 4 hours | EUR 240 |
| Total for eight approved articles | 12 hours plus tools | EUR 740 |
That is EUR 92.50 per approved article, of which EUR 52.50 is review and approval labour. The two rejected articles are inside that number. Design, distribution and later maintenance are outside it, so add them when you evaluate the wider programme.
The same batch under three different processes shows where the money moves:
| Process | Review minutes per article | Rejection rate | Cost per approved article | What the number hides |
|---|---|---|---|---|
| Manual writing and editing | 60 to 90 | Low, because the writer self-edits | Highest, driven by writing hours | Slow throughput |
| AI drafts, no checks or critic | 45 to 60 | High or unknown | Looks cheap per draft, expensive per approval | Errors that reach readers |
| AI drafts with checks, critic and approval | 20 to 30 | 15% to 25% | Lowest sustainable | Requires a real brief for every article |
The ranges are assumptions to show the shape, and your own batch will give the real ones. Two signals from the batch matter more than the average. A rejection rate near zero usually means the checks are too loose. A rejection rate above a third usually means the briefs are thin, and the fix is upstream.
The two rejected drafts sit in the numerator and the eight approved articles in the denominator. Review and approval labour is EUR 52.50 of the EUR 92.50, which is why the checks earn their place by shortening review.
Cutting the model bill from EUR 20 to EUR 10 saves EUR 1.25 per approved article. Cutting review by an hour across the batch saves EUR 7.50, if the same eight articles still meet the standard. So the checks and the critic earn their place by taking minutes out of review, and never by removing the reviewer.
Measure usefulness after publication
Approval is a production milestone. It does not prove an article attracts the right readers or helps them decide. Give each article a small set of measures tied to its job, and keep production efficiency separate from audience outcomes.
| Measure | What it tells you | Caution |
|---|---|---|
| Review minutes per approved article | Editorial workload | A shorter review may miss more |
| Factual corrections after publication | Failures that escaped review | Record severity and reach, not only the count |
| Useful next-step rate | Whether readers use the resource or action the article offers | Define the event and eligible visits the same way every month |
| Qualified enquiries influenced | Connection to suitable demand | Attribution shows a path, never a cause |
For a comparison guide, track use of the comparison and the enquiries that follow. For a tutorial, ask whether readers completed the task. A page view answers neither question. Our guide to technical SEO and AI search covers how those enquiries get measured against the search that produced them.
Start with one article type
Pick a format with available evidence and an available expert, such as a buyer comparison or an implementation guide. Run a batch of ten through the whole gate before increasing volume, and keep the rejected drafts with their findings so the team can see where the effort went.
At the end of the batch, read the expensive failures. Repeated factual corrections point to weak briefs or an unsuitable topic. Heavy restructuring points to a missing reader decision. Persistent voice problems need better examples in the brief rather than more instructions.
Flotta runs this practice for the focused sites it builds. Pages are researched before they are written, every page passes automated checks and a second-model review, and you approve blog posts before they publish.
Ask for a trial on a representative brief and your own approval criteria. Then bring the batch’s briefs, findings and costs to the decision about the next one.
Frequently asked questions
Can we publish AI generated content without human review?
Not for business content that carries factual claims or brand promises. Automated checks catch formatting, banned phrases and some errors, and a critic model catches unsupported claims. Neither can confirm that the argument is sound or that the recommendation fits your customers, so a named person approves each article.
How do we measure whether AI content saves money?
Divide the total cost of a production batch by the number of approved articles, and compare it with the same measure for your manual process. Include research, drafting, review, revisions and the drafts you rejected. Track reviewer minutes separately so a lower model bill can't hide extra editorial effort.
Will publishing more AI articles improve search performance?
Volume on its own earns nothing. Google's scaled content abuse policy covers many pages generated without adding value, however they were produced. Choose topics you can support with expertise, answer them well, and measure the reader actions each article produces.
What counts as AI content slop?
Prose that reads plausibly and gives the reader nothing they can use: generic claims without numbers, invented sources, confident errors, and the sentence shapes machines default to. Readers stop trusting the site, and search systems treat the pages as low value.
Should we tell readers that AI helped write an article?
Google suggests giving readers context on how content was produced, in a way that makes sense for your audience. A short note on the process, who checked the facts and who approved the article does more for trust than a generic disclaimer.
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