Don't 'Build' AI Automation Workflows, 'Code' Them.

Follow me on LinkedIn - AI, GA4, BigQuery

Have you ever wondered how these AI gurus consistently publish complex automation workflows every week?

Like… are they actually that smart, or are they just using AI to build AI? 😏

If you’ve ever tried building one of these workflows manually, you know it’s no walk in the park.

We’re talking weeks of building, breaking, debugging, and rebuilding.

Yet somehow, these “AI experts” drop a brand-new, ultra-sophisticated automation every few days, effortlessly.

They rack up likes, praise, and comments for their “brilliance,” but here’s the uncomfortable truth: they’re not doing most of the work themselves.

The illusion of genius.

These AI Gurus love to claim that everything is handcrafted, a testament to pure skill and creativity. But in reality, they’re letting AI do all the heavy lifting.

The system writes the code, builds the workflows, and they just tweak the output a little before presenting it as their masterpiece.

The audience sees polished results and assumes genius, not realising that behind the scenes, it’s mostly prompt orchestration, not deep technical mastery.

My breakthrough moment.

After spending some time experimenting with Claude, I had a realisation that completely shifted my perspective:

You don’t “build” AI workflows, you code them.

Recognising that workflow automation equals coding means you start debugging like a developer, architecting like a coder, and optimising like a system designer.

It’s a realisation commonly reached by professionals who move from out-of-the-box recipes to complex, bespoke automations, the step from “user” to “automation engineer.”

Nobody tells you this. It’s the quiet secret of real automation builders.

I hesitated to share it because, honestly, it’s that valuable. Even after paying experienced AI developers thousands of dollars, not one of them mentioned this insight.

That’s how valuable and powerful it is.

To take automation beyond basics, you end up “coding”, whether in the language of logic, scripts, or advanced expressions.

Here’s the key insight.

What is an n8n workflow, really? It’s just a JSON file, a piece of code.

Once you realise that, everything changes.

If you can generate, manipulate, or modify n8n JSON directly, you can build any workflow you want, without ever manually dragging or dropping a single node in the n8n UI.

That’s the real trick behind the “AI influencer magic.”

They’re not building complex workflows manually; they’re generating the code and taking credit for it.

In AI development, building a workflow is the easy part.

That’s why you’ll find tens of thousands of n8n workflows floating around online, all free to download.

However, most of these workflows are merely demonstrations. They have no commercial value.

Because in the real world, without real integration with CRMs, data systems, APIs, and infrastructure, these workflows have no commercial or operational value.

Debugging and integration are where skill actually matters.

In real-world AI development, the hard part isn’t generating a workflow. It’s debugging it.

It’s integrating it into a living, breathing system where data flows, clients interact, and uptime actually matters.

That’s where true developer skill and domain expertise show up. Not in how fast you can generate a JSON file, but in how seamlessly you can make it work.