I keep hearing this claim about AI Copilot Workflow Generation, and it sounds great in theory. You describe your automation goal in plain English, and the system generates a ready-to-run workflow. For a migration project where we’re trying to move critical processes to an open-source BPM, that would be a game-changer.
But I’m skeptical because I’ve seen too many “AI-generated” solutions that need massive rework before they’re actually production-ready. We’re evaluating whether to use this for our migration planning, and I need to understand what “ready-to-run” actually means.
Has anyone actually used plain text descriptions to generate migration workflows and had them work without significant modification? I’m specifically curious about what fails—do the generated workflows miss edge cases? Do they skip error handling? Do they not account for your specific data structures? Or does it actually produce something your team can deploy with minimal tweaking?
Our migration team is asking this because we need to know if this approach would actually save timeline or if we’d just be rebuilding everything from the AI’s starting point anyway. What’s the honest answer from people who’ve actually tried this?