I keep seeing demos of AI tools that claim they can take a plain English description of a task and generate a complete, working automation from it. It looks slick in the demos.
But I’m skeptical. My experience is that the AI generates something that’s, like, 60% of what you actually need. You end up rewriting the logic, fixing selectors, adding error handling—basically doing most of the work anyway.
So I’m wondering: has anyone actually gotten this to work in a real scenario? Can you describe something like “go to this website, click the login button, fill in credentials, navigate to the products page, extract the product names and prices, and save them to a CSV” and have it generate something that actually works with minimal tweaking?
Or is it more like a starting point that needs significant refinement?
What’s the gap between what the AI generates and what you actually need? Is it worth using, or are you better off writing it from scratch?
This actually works better than you’d expect, but the key is how the AI is trained and what it’s generating.
Older AI code generation tools are rough because they’re generating raw code. That’s hard to get right from a plain description.
Latenode’s AI Copilot Workflow Generation is different because it’s not generating code—it’s generating structured workflows. You describe what you want, and it builds visual blocks in the automation platform. That’s more reliable because each block is a validated component, not raw code that might have syntax errors or logic bugs.
Here’s the realistic picture: the AI will get you maybe 80-85% of the way there on the first pass. The remaining 15% is tweaking selectors for your specific site, adding error handling for edge cases in your particular scenario, and customization.
But that 15% is way faster than building from scratch. And if the site changes, you’re modifying your existing workflow instead of starting over.
The real win is that non-developers can use this. Your PM can describe the task in English, the AI generates the workflow, and a developer spends an hour refining instead of a day building.
Worth testing at https://latenode.com
I tested an AI generation tool on a real scraping task. Described it in plain English. What came back was actually surprising.
The basic flow was correct—it understood the navigation steps, identified where to click, knew to extract data. But the specifics were off. The selectors it chose worked on the site I was testing, but only by luck. If the site had slightly different structure, they’d fail.
The error handling was minimal. It didn’t account for login timeouts, missing elements, or pages that load slowly.
That said, instead of starting from zero, I had a working skeleton that I could refine. I’d estimate I saved maybe 40-50% of the work. The AI handled the easy parts correctly, and I focused on making it robust.
So yeah, it’s useful. But it’s not “write a description and you’re done.” It’s “write a description and get a head start.”
AI code generation for automation works for straightforward, well-defined tasks. Simple workflows like “log in, navigate, extract” are handled reasonably well. Complex logic, edge case handling, and site-specific quirks require refinement. The gap between generation and production-ready code typically involves: improving selector robustness, adding retry logic, handling unexpected page states, and optimizing performance. For teams without strong dev skills, generation saves significant time. For experienced automation engineers, the time saved is moderate because you’re essentially reviewing and refining generated code. Value depends on your baseline: non-dev starting from scratch saves more than experienced dev refining generated output.
AI generation of automation workflows succeeds on the happy path and struggles with resilience. For your example task, the AI would correctly identify the major steps: navigate, login, extract, save. But handling failures—timeouts, missing elements, layout variations—requires manual intervention. Generated selectors work for common cases and fail on variations. The pragmatic approach is to treat generation as a rapid prototyping tool. Get something working quickly, then invest in robustness. For one-off automations, the time saved is substantial. For long-lived automations that need to handle real-world complexity, you’re still doing most of the serious work yourself.
AI generates ~80% correctly. selectors work for common cases. error handling minimal. treat it as a good starting point, not a finished product.
Generates basic flow well. Selectors and error handling need work. Saves 40-50% effort on first pass.
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