I’ve been hearing about AI copilots that can take a plain English description of an automation and turn it into a working workflow. It sounds almost too convenient, but I’m genuinely curious how well it works in practice.
Like, if I said “log into this website using username and password, wait for the dashboard to load, then extract all the transaction IDs and amounts into a structured format,” would the AI actually generate something that works? Or is it mostly the AI generating 80% of the code and you spending hours debugging and rewriting the other 20%?
I’m also wondering about edge cases. What happens if the site’s login page is slightly different than expected, or if the transaction table has empty cells, or if there’s a CAPTCHA step? Does the generated workflow handle those gracefully, or does it fall apart?
Has anyone actually used this approach and had it work well, or is it more of a proof-of-concept thing?
The plain English to workflow conversion actually works surprisingly well. You describe what you want, the AI generates a ready-to-run workflow, and most of the time it just works. The generated workflow understands the steps semantically, not just surface level.
Where it really wins is with edge cases. If you said “extract all transaction rows,” and one row is missing an amount, a hand-coded script would probably break. But an AI-generated workflow handles missing fields more gracefully because it understands the intent—you want the transaction data, and it figures out how to get what’s available.
I’ve used this for login flows with CAPTCHAs, sites with dynamic content, pages that load data asynchronously. The AI asks questions during generation if something’s ambiguous, so you’re not just blindly hoping it worked.
Yeah, sometimes you need tweaks, but I’ve never had to rewrite from scratch. And when you do need to adjust something, you can usually edit the workflow visually without touching code.