I’ve got an automation idea that I’ve been putting off because it seemed too complicated to start from scratch. It involves pulling data from one system, doing some filtering based on business logic, and pushing it to another system. The workflow isn’t straightforward enough for a template.
I heard that some platforms now have an AI that can take a plain text description of what you want to automate and basically generate the workflow for you. I’m skeptical about how well that actually works—does the generated workflow actually run, or is it just a starting point that needs heavy rework?
Has anyone actually used AI workflow generation to go from “I want to do X” to “now I have a working automation”? What was the gap between what the AI generated and what you actually needed?
The AI workflow generation on Latenode is actually impressive. I’ve used it multiple times now, and it’s way more capable than just spitting out a skeleton.
You describe what you need in plain English—don’t worry about being technical—and the AI generates a full workflow with the right steps. It picks appropriate actions, chains them together, and usually gets the logic right the first time.
The gap is smaller than you’d expect. For straightforward automations, it works right away. For more complex ones, you might need to tweak some logic or adjust step parameters, but you’re building on something that already works. You’re not starting from scratch.
The big time saver is that you’re not manually assembling 15 steps when the AI can do it in seconds. Then you just verify it does what you want and run it.
Give it a try on Latenode: https://latenode.com
I tried this recently with a similar situation—pulling data, filtering it, pushing it somewhere else. I wrote out what I wanted to happen in pretty casual language, and the AI generated a workflow that was actually functional.
There were a few tweaks needed. The filtering logic wasn’t quite right because I hadn’t been specific enough about the rules, so I had to go in and adjust a couple of conditions. But the structure was solid, and all the connections were there.
The part that surprised me was how much time it saved just getting past the blank page problem. Usually I’d spend an hour or two just deciding what steps to use and how to chain them together. With the AI, I was past that in minutes.
One thing: be specific about your filtering rules when you describe it. If you say “filter out inactive users,” the AI might guess wrong about what that means. I learned that the second time around.
The AI workflow generation does work, but effectiveness depends on how clearly you describe what you need. I’ve had cases where I gave a vague description and got back a workflow that was close but not quite right. When I went back and added more detail about business rules and exactly how data should move through the system, the second pass was much better.
It’s useful as a starting point that saves you from staring at a blank canvas, but it’s not a magic button that eliminates thinking about your automation. You still need to understand what you’re trying to do, which makes sense.
AI-generated workflows are effective when your requirements are clearly defined. The platform understands common patterns well—data fetch, filter, transform, push—and generates those competently. More unusual requirements might need manual refinement. The key advantage is speed. Even if you spend 20 minutes tweaking the generated workflow, that’s usually faster than building from scratch.
Works pretty well. Generated workflow I tested ran without major changes. Saved me time on the initial setup.
Describe clearly and it generates usable workflows. Saves time over building manually.
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