I’ve been building automation workflows with n8n lately, and there’s one lesson that keeps coming up: code and AI are good at very different things.
It sounds obvious. But once you start wiring things together, you realize that knowing where to use each one is the whole game.
Code Is Predictable. AI Is Flexible.
Here’s a concrete example. I have an n8n workflow that collects and summarizes news articles.
The first part — fetching articles via RSS feeds — is pure code. Hit a URL, get structured data back. Do it a hundred times, you get the same result every time. No surprises, no randomness. That’s exactly what you want.
But summarizing those articles? That’s a different story. Every article is different in length, tone, and subject matter. Figuring out “what’s the key takeaway here?” isn’t something you can hardcode with a few if statements. That’s where AI shines.
And yes, AI might summarize the same article slightly differently each time. That’s fine. A summary doesn’t need to be identical every time — it needs to capture the point.
A Simple Question to Guide Your Decisions
When I’m designing an automation, I ask myself one thing:
“Does this step need to return the exact same result every time?”
If yes — use code. Fetching data, moving files, converting formats. These are tasks where consistency is everything.
If no — consider AI. Summarization, tone analysis, content classification. These are tasks where a bit of variation doesn’t hurt, and rigid rules would actually hold you back.
Don’t Hand Everything to AI
Every time AI makes headlines, someone says, “Just let AI handle everything.” But when you actually build automations, you learn quickly that’s not how it works.
You don’t need AI to pull an RSS feed. And you can’t reasonably summarize a news article with a chain of if-else blocks.
The real skill is seeing the boundary — knowing where code ends and AI begins. Once you see that line clearly, your automations get simpler and more reliable.
Why n8n Makes This Visible
What I love about n8n is that this code-AI relationship becomes visual. As you lay out nodes in a workflow, you can literally see: “Everything up to here is deterministic. From this node on, AI takes over.”
If you’re just getting started with AI or automation, my suggestion is this: don’t start by asking “How do I automate everything with AI?” Instead, ask “Where exactly does AI add value?”
That shift in perspective makes all the difference.