We’re evaluating a migration from Zapier, and one of the things I keep seeing mentioned is the AI Copilot that generates workflows from plain-language descriptions. It sounds great in theory—describe what you want, get a working automation.
But every tool makes this claim, and I’m skeptical. In my experience, you need to rebuild things anyway once they hit production.
I’m trying to understand the realistic time-to-value here. If I describe a workflow like “take customer emails from Gmail, extract data, write summaries in Claude, post to Slack,” how much of that actually works out of the box? Do you end up spending hours rebuilding conditions, error handling, and edge cases? Or does it genuinely accelerate things?
I’m also curious about the enterprise angle—when you’re deploying across departments or teams, does the head start from AI generation actually matter, or are you customizing so much that the initial time savings get lost?
What’s been your experience with plain-English workflow generation at scale?