How I Review, Edit and Refine AI Generated Content

Most marketing teams I work with now use AI for first drafts. The drafts look finished, with clean headings and confident claims. Read them closely, though, and you’ll find stats with no source, citations that don’t exist and examples that could belong to any brand.

Publishing those drafts as they are rarely pays off. Graphite studied the top Google results for more than 31,000 keywords in 2025. Only 14% of the articles were AI-generated, and only 7% of the articles ranking #1. That’s at a time when about half of all new articles online are mostly written by AI.

I edit AI-assisted content for B2B SaaS brands, and I follow the same seven steps for every draft. Here’s what each step involves and why it matters.

Why a raw AI draft isn’t ready to publish

Google doesn’t penalise content just because AI helped write it. It acts against content that doesn’t help the reader. Since March 2024, its spam policies also cover scaled content abuse, which means publishing lots of pages mainly to rank, however they were made.

The bigger risk is accuracy. A language model writes by predicting the most likely next words, which is why its sentences read so smoothly. It’s also why the model can’t tell whether a stat is current or whether a source exists.

A quick edit doesn’t fix this unless someone checks the facts. In 2023, CNET published 77 articles drafted with AI and reviewed by its staff. A later audit found errors in 41 of them, and the site had to issue corrections.

1. Check every claim, stat and source

I start with the facts, because one wrong number does more damage than any awkward sentence. It can end up in a sales deck or a customer email before anyone notices.

Trace each number to the original source

AI drafts are full of stats, and many come with no source. Others name a source that says something different. So every number stays unconfirmed until I find the study or report behind it.

That usually means going past the first blog that quotes it. Secondary sources round numbers, drop the date and lose the context. Once I have the original, I check who was surveyed, when and what was measured. If it doesn’t support the sentence, the claim gets rewritten or cut.

Open every link

AI tools produce citations that look real, with believable authors and working links. In a 2025 study in JMIR Mental Health, researchers asked GPT-4o to write six literature reviews. About one in five of its 176 citations were made up. Of the fake citations that came with a DOI, 64% pointed to a real paper on an unrelated topic, so a quick look at the link would suggest everything was fine.

That’s why I open every link and read enough of the page to confirm it supports the sentence. For a 5,200-word guide to e-commerce in France that I wrote for Anchanto, that meant checking all 23 citations.

Replace old data

Every model has a training cutoff, so the “latest” figures it gives you can be a year or two old. In search and AI, that’s a long time. For each report a draft cites, I look for a newer edition. When there isn’t one, the year goes into the sentence so readers can judge the data themselves.

2. Replace generic examples with real ones

Ask an AI tool for an example, and you get the most predictable one, like a growing SaaS startup or a marketing manager chasing a quarterly target. Every brand using the same tool gets the same examples. A buyer comparing vendors then sees the same scenarios on every blog and finds no reason to pick yours.

Specific examples have the opposite problem. When AI names a company or quotes a customer result, it often makes up the details.

Google’s guidance on helpful content puts weight on first-hand experience, which AI can’t supply. Your company usually has plenty of it, and these are the places I look first.

• Published case studies and customer stories, since they’re already approved for public use
• Help centre articles and product docs, which show how the product handles real tasks
• Sales and support call notes, which capture buyers’ questions in their own words
• A short call with a product manager or solutions engineer who knows the topic

If I can’t find a real example for a section, I remove the example rather than keep a made-up one. A generic scenario adds words, but it doesn’t prove anything.

3. Make each section earn its place

Lead with the answer

AI drafts warm up before they get to the point. A section called “How to choose a 3PL partner” often opens by explaining why choosing a 3PL partner matters. The reader already knows that, which is why they clicked.

So the answer moves to the first sentence or two, and the rest of the section backs it up. That makes the page easier to skim and easier for AI search tools to quote.

Give readers a reason to click

This matters more now that Google shows AI summaries above the results. Pew Research tracked the browsing of 900 US adults in March 2025. When an AI summary appeared, people clicked a regular result in 8% of visits, compared with 15% when there was no summary.

A definition won’t earn that click, because the summary already gives it. For each section, I look for something a summary can’t offer, like a checklist the brand uses with clients, a comparison table or a common mistake and how to avoid it. If a section has none of these, it gets built up or cut.

4. Catch the drift back into AI writing patterns

Nearly every team I work with gives its AI tool a style guide, and the drafts still come back sounding like AI. The first few paragraphs usually follow the guide well. By the middle, the old habits creep back, and by the conclusion they’ve taken over.

Research suggests one reason. A 2025 study by researchers at Microsoft and Salesforce compared how models handle a task given all at once with the same task spread over several turns. Across more than 200,000 simulated conversations, performance dropped by 39% on average when the instructions came in turns. A long style guide followed by rounds of revisions looks a lot like that setup.

So I read every draft against the style guide from start to finish. Wikipedia keeps a public guide called Signs of AI writing for its editors, and these are the patterns I find most often in client drafts.

• Overused words like “delve,” “underscore” and “showcase”
• Ideas grouped in threes, even when there are really two or five
• Fake contrast, like “This isn’t just a tool, it’s a partner”
• Long dashes in almost every paragraph
• Intros that praise the topic and conclusions that repeat the whole article

One of these alone doesn’t prove AI wrote a piece. When several show up together, readers notice and trust the content less.

5. Remove repetition

This is the most common problem in long AI drafts. The same point appears in the intro, again in the middle and once more in the conclusion, each time in slightly different words. It happens because AI writes each section as if the reader is new to the topic.

Before I edit any sentences, I write one line describing each section’s job. One section might explain a concept, and another might compare options. When two sections have the same job, they get merged. A section with no clear job usually goes.

Then I read the draft once more, looking only for repeated ideas, and keep each point where it does the most work. Drafts usually get noticeably shorter at this stage, and they read better for it.

6. Place CTAs and product mentions with care

AI drafts either barely mention the product or pitch it every few paragraphs. Neither matches how a buyer reads a blog.

Someone searching for “what is inventory management” wants to understand a concept and isn’t ready for a demo. Someone searching for “best inventory software for manufacturers” is close to a decision, so a direct call to action makes sense. On Kladana’s blog, we matched each article’s call to action to the reader’s stage. Explainers mentioned the product lightly near the end, and comparison articles invited readers to try it.

The best product mentions come right after the article describes a problem the product solves. At that point, the mention reads like a helpful next step. AI can’t judge this well, because it doesn’t know which features your buyers care about or which objections come up on sales calls. Your team does.

7. Make the final calls on audience, voice and compliance

The last pass covers decisions that need human judgment.

• Depth. A CFO and an operations manager might search for the same topic, but they need different levels of detail.
• Voice. Reading the piece aloud shows whether it sounds like the brand or like a generic AI voice.
• Risk. Claims about results, pricing, competitors or regulations get flagged for legal or compliance review.

That last check matters more than it used to. In 2024, a Canadian tribunal ruled that Air Canada was responsible for wrong fare advice its website chatbot gave a customer. A company owns what its AI tools say, even when nobody there wrote the words.

Where I use AI and where I don’t

I use Claude and ChatGPT every day. They’re good at building outlines, summarising long reports and rewording a paragraph that isn’t working. Those tasks used to take hours.

What I don’t hand over are the decisions that make readers trust the content. A person needs to confirm the facts, choose the examples, decide what to cut and agree on what the brand can say. AI can suggest options for all of these, but it shouldn’t make the final call.

What a human editor adds

AI has made first drafts fast and cheap. A fast draft still needs someone to check the facts, add real examples, cut the repetition and make it sound like your brand. Without that work, it blends in with everything else online, and as Graphite’s data shows, that content rarely reaches the top of Google.

If your team uses AI for content and wants it to read like it came from people who know the subject, book a call or send me an email.