I Spent 4 Hours Trying to Automate Marketing Content With AI (It Failed-and That’s the Point)

Yesterday afternoon I had a profound learning experience. Like many profound learning experiences, it was painful.

As the owner of an AI implementation business, I’ve got a bias toward practicing what I preach. I don’t want to be the builder whose own kitchen is never finished. So I decided it was time to get more AI operating inside my own company-not as an idea, but as working systems.

I chose a process that feels like an obvious target: generating marketing content.

Content needs to be created regularly. It’s time-consuming. It’s easy to procrastinate. And it sits close to revenue. On paper, it’s the perfect “AI automation” win.

So I did what most people do: I jumped straight into tools.

I started binding together different AI tools inside Make.com. I had a picture in my head of a clean workflow: inputs go in, content comes out, and my marketing engine runs with less effort.

Four hours later, I had a mess that didn’t work.

The more it didn’t work, the deeper I went.

The deeper I went, the more it didn’t work.

By the end, I was hot, tired, frustrated, and I couldn’t think straight anymore.

Then came the insight: this wasn’t a waste of four hours.

It was a live demonstration of how businesses accidentally sabotage their own AI projects.

What I did wrong (and what to do instead)

Mistake #1: Automating before confirming it’s worth automating

I didn’t properly consider whether the thing I was automating was actually worth automating.

With hindsight, that isn’t clear.

Yes, content creation can be repetitive. But not all content work is the same:

• Some tasks are production (turning a clear brief into output)

• Some tasks are strategy (choosing the message, angle, proof, and CTA)

• Some tasks are judgment (brand voice, nuance, what not to say)

AI can help with all three, but you shouldn’t automate all three the same way.

If you automate the wrong layer, you don’t get leverage-you get a higher-speed version of a flawed process.

What to do instead: Before you automate, score the process:

• How often does it happen?

• How long does it take now?

• What’s the cost of delay?

• What’s the downside of errors?

• What parts are repeatable vs. judgment-based?

If the answer is “this is strategic and requires high judgment,” maybe you don’t automate it-you build a draft + review system.

Mistake #2: Building without a map

Secondly, I didn’t properly map out the process I was trying to automate.

So when things started to break, I had no “source of truth” to refer back to.

That’s the hidden problem with tool-first builds: once the workflow becomes complex, you can’t tell whether you’re:

• fixing the system, or

• just moving the problem around

What to do instead: Write the workflow on one page before you open any tool:

• Inputs: What must be true before this starts?

• Steps: What happens in what order?

• Decisions: Where does the workflow branch?

• Outputs: What does “done” look like?

• Quality checks: What fails the output?

• Ownership: Who approves what?

That map becomes your anchor when you’re tired, stuck, or tempted to chase edge cases.

Mistake #3: Choosing tools without auditing them

Thirdly, I didn’t properly select the best tools for each stage.

I took suggestions but didn’t properly evaluate, audit, or push back.

When you do this, you end up with a “Frankenstack”-tools that technically connect, but don’t produce consistent outcomes.

What to do instead: Choose tools after you define requirements. For each stage, ask:

• What’s the minimum viable output?

• What format does it need to be in (JSON, plain text, doc)?

• What failure modes are unacceptable?

• Where do you need human approval?

• What needs to be logged for debugging?

Tools are a means. The workflow is the product.

Mistake #4: Losing the big picture as complexity grows

Because I didn’t have the map, as I got deeper into the process, I lost my place.

And the deeper I went, the harder it was to come back to the big picture.

I also used AI to help me fix the automation. But the deeper it went, the more it lost track of the overall objective and the overall system we were trying to build.

This is a common trap: AI is excellent at solving the problem right in front of it. It is not automatically excellent at protecting the original intent of a multi-step system.

What to do instead: Treat AI like a junior operator:

• give it the map

• give it the objective

• give it constraints

• and make it earn autonomy over time

Why this wasn’t a waste

I didn’t get a functioning automation after four hours.

But I got two things that matter:

• A sharp lesson about how not to implement AI inside a business

• Material for this post (which will prevent other people from repeating the mistake)

Also, Captain Amplifyy (my internal name for the content engine that is supposed to be firing) was not impressed.

Fair.

The simple pre-automation checklist (steal this)

Before you build your next AI automation, answer these on one page:

• Is it worth automating? What’s the time cost, revenue impact, and cost of mistakes?

• What’s the workflow, in plain English? Inputs → steps → decisions → outputs → quality checks.

• What guardrails exist? Human approvals, escalation rules, monitoring, and logging.

If you do just that, you’ll avoid 80% of “AI automation” failures.

Closing question

What process have you tried to automate that turned into a mess?

And if you could do it again, what would you map first-before touching any tools?