I keep seeing claims about AI-powered workflow generation where you describe what you want in plain English and the system builds it for you, ready to deploy. It sounds amazing if it’s real, but every time I’ve seen something like this in practice, there’s always a ton of customization and tweaking after the initial generation.
We have a fairly straightforward workflow we want to automate: take customer support emails, extract key information, route them to the right department, and send automated acknowledgments. It’s not rocket science, but it’s also not trivial—there are conditional branches, API calls, and error handling.
The question is: if I describe that to an AI system using natural language, would it actually produce something I can put into production, or would I end up rebuilding half of it anyway? And if I do end up rebuilding it, what’s the actual time savings compared to just building it in a visual editor to begin with?
I’m also wondering if there’s a quality difference in what gets generated. Like, does an AI-generated workflow tend to miss edge cases or create inefficiencies that become problems once it’s live? And how much testing is involved after generation to make sure it actually does what you described?
Has anyone actually used AI-powered workflow generation for a real production workflow? What was the experience like, and did it actually save you time, or did the customization work end up being just as tedious as building it from scratch?