I keep seeing claims that you can describe what your current workflows do and the system will generate a ready-to-run migration for you. This sounds too good to be true, which is usually a red flag.
We have some fairly complex processes—document approvals with multiple stakeholders, conditional routing based on document type, escalations if approvals take too long. These aren’t simple linear flows.
I’m wondering if anyone has actually tried the AI copilot approach to generating workflows from plain language descriptions. Did it work? How much of what it generated was actually usable, or did you end up rewriting most of it? And more importantly, would you trust generated workflows for critical processes without a full audit?
I’m not asking theoretically—I need to know if this actually saves time or if it just shifts the work around.
I tested this and honestly it was about 60 percent useful. I wrote out one of our approval workflows—basically described the steps, who gets notified, what happens at each decision point. The system generated something that was structurally sound but missed nuances.
Specifically, it didn’t capture that sometimes we need different approval chains based on document value. The routing logic was there but oversimplified. I had to go in and add conditions it didn’t infer from my description.
Here’s the thing though—it crushed the boring part. All the boilerplate connection stuff, the notification setup, the basic flow structure. I just had to refine the decision logic and edge cases. That probably saved me a day of work even with the rework.
The quality depends heavily on how well you describe the process. If you’re vague, you get vague output. If you describe step by step what happens in each scenario, it actually does pretty well.
We tried it with a simpler process first—employee onboarding checklist. That one came out almost production ready. Then we got ambitious with our claims processing workflow and yeah, it missed a bunch of the conditional logic.
I think the sweet spot is using it for 70 percent of the work and planning for cleanup. Don’t plan on it being perfect, but don’t dismiss it either.
We invested time in writing really detailed process descriptions upfront. We documented not just the steps but the edge cases, what happens when things fail, who makes decisions when there’s ambiguity. That descriptive work took a few hours but it paid off.
When the AI generated workflows from those descriptions, we got something we could actually review logically instead of starting from scratch. It was like having a first draft that was 80 percent there. We still did full testing and made changes, but we weren’t reimagining the entire flow.
Generated workflows save maybe 50 percent of dev time but need full audit before production. Use it for the boring part, keep humans in charge of the critical logic.
We actually use the AI Copilot Workflow Generation feature for exactly this, and it changes the game when you approach it strategically. The key is being specific about your decision points and conditions, not just narrating what happens.
You describe something like “when invoice arrives, route to manager if under $5000, otherwise to CFO. Manager approves or rejects within 2 days, CFO is auto-escalated if pending 3 days.” The system generates a workflow that captures that logic correctly because the conditions are explicit.
Where it falls short is organizational context—things like “we always check vendor history first” that everyone knows but nobody documents. So you generate the workflow, the process owners review it, you add 10 percent refinement, and you’ve saved hours of building from zero.
For migration especially, this cuts your evaluation timeline way down. You’re not spending weeks rebuilding every single workflow. You’re generating candidates and validating them.