Automated, but Not Intelligent: Where AI Tools Fall Short in SEO
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“Here’s how AI replaced”
I get it, everyone is excited about using AI to automate their tasks, I am too. But we also need to understand what AI cannot fully replace. One thing we can agree on, is it cannot replace SEOs!
I saw this post recently on LinkedIn and I just cannot stop laughing 😂
I also didn’t believe AI should be heavily involved in quality content writing. We’ve seen one study after another showing the impact of scaled content abuse, or as Google puts it in its spam policies, “Using generative AI tools or other similar tools to generate many pages without adding value for users.”…..
But I also came across a very interesting example!
I was super surprised to learn that Ahrefs are using Claude Code to write and update some of their content. They’ve been doing it for 8 months now, and the process makes read-to-publish drafts starting from keyword in just 6–12 minutes !!!
I had to pause and learn how are they doing it and not getting hit with some kind of spam penalty.
Which brings me to today’s topic.
AI is a great tool. But like any tool, its value is defined by how you use it, and more importantly, where you use it.
Not all automations are created equal.
Nor everything that can be automated should be handed to an LLM.
So let’s break this down properly.
Using AI to generate quality content
Any good use of AI will not involve a “quick” setup, and the Ahrefs team seem to have invested a lot of time into building and automating their editorial process.
Here’s how they did it:
Turned every editorial SOP into a claude skill file.
If you don’t know what a skill file is, it’s a a structured, markdown-based text file that teaches an AI agent how to perform a specific, specialized task (often named SKILL.md)
SOP for how to write a brief became it’s own skill file
SOP for how to write an outline became it’s own skill file, and so on…
Add your inputs, like focus keyword, working title, your POV and points you want to include, other things you want to reference (e.g specific products, etc…)
Build one master skill that runs all the other skills files in order.
When the steps are executed sequentially, this gives you a chance to provide feedback at every stage.
Use Ahrefs MCP to pull SEO data like SERPs results, and competitor coverage.
Refine the output by providing 5-10 great articles of your own to reference, and the system surfaces moments in the draft that you can insert your own real life experience.
Refine the system, using a skill that tests every other skill in isolation and suggest improvements. This way your system gets better over time.
Read every article from start to finish before you publish it
Note: they’re not using this system to pump out hunderds of blogs every month, they actually only created 15 so far [source to post]
I see what they did here, they removed all the noise and stripped the process down to its core: the value isn’t the content itself anymore, it’s the meaning/knowledge they are trying to communicate.
I think this is a very good approach, and it works for them.
But I don’t think this can work for me.
As an indie newsletter writer, what I put out isn’t just SEO insights. It’s the tone, the random comments, the little jokes (don’t worry, I’m holding back… mostly 😂).
That’s part of the newsletter… me.
And I don’t think that’s something you can fully systematize.
Also by the by, if you have not heard, Starbucks are hiring more baristas to win back their customers.
“Over the last couple of years, we’ve actually been removing labour from the stores. I think with the hope that equipment could offset the removal of the labour,” Mr Niccol said during a call with investors.
“What we’re finding is... that wasn’t an accurate assumption with what played out.”
Customer experienced seemed to suffer, so they’re rehiring people.
Don’t use AI for these
There are things that AI simply cannot do very well. Here’s what I don’t recommend you fully automate:
Data tasks: Despite the hype, AI tools like ChatGPT and Claude are unreliable for data work.
They can hallucinate, miss critical data points, and quietly omit information without warning.
Did you know that LLMs struggle data in tables (tabular data). According to recent research, even the smartest AI models see a big drop in their scores when they have to read data from a grid.
So if you’re uploading your data csv to chatgpt and asking it questions about the data, chances are the output is inaccurate, which is risky if you’re doing client reporting for example.
Summarizing important documents: this is probably one of those no-brainers we see circulating around. I do it too, but only when I want a rough idea on something. If I want a summary that’s comprehensive and does not miss out any important points, AI is not gonna do that because it can simply skip important information…. I wouldn’t trust the output.
Write long code in one go: I teach vibe coding, and one thing I always say break tasks down into smaller tasks and test each one. The bigger the task, the more likely the mess in the code and therefore the output, and the more challenging it is to fix, if at all 😂😂😂 so if you’re vibe coding, you need to have a mental plan of the steps involved. Start small to grow your code.
And That’s a Wrap (Almost 😄)
I really believe that the future is human, more human, and deeply human. All things machines cannot create.
Think of Artesian bread vs. store bought bread with tons of preservatives and additives.
That’s how I see the difference between AI output to human work.
And because this difference exits, not everything can be fully automate.
That’s that for today folks, and see you next newsletter 🙏
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Disclaimer: LLMs were used to assist in wording and phrasing this blog.