We’re looking at migrating from our current BPM setup to something more open source, and the conversations keep centering around this idea that you can take old process diagrams, feed them into some AI system, and get production-ready workflows out the other side.
I get the appeal. I really do. But I’m trying to be realistic about what that actually looks like in practice. We’ve got maybe 60 core processes documented in Visio and various wikis—some are ancient, some are semi-standardized, most are somewhere in between.
The pitch I keep hearing is that an AI Copilot can rapidly convert these into ready-to-run automations, which would theoretically cut our migration timeline and reduce risk. On paper, that sounds amazing. But I’ve seen enough “AI-generated code” projects to know there’s usually a gap between the demo and reality.
I’m curious about the actual experience here. When you’ve fed legacy process diagrams into something like this—whether it’s Latenode or another platform—what percentage of the output did you actually deploy as-is? Did you need to rebuild logic, fix assumptions the AI made about your business rules, or clarify edge cases that didn’t make it into the original diagrams?
Also: if migration timelines are truly reduced, what’s the actual time breakdown? Are we talking weeks instead of months, or is that more aspirational?
I’m trying to build a realistic cost case for our CFO, so I need the honest version.
I went through this about two years back with a similar set of legacy processes. We had maybe 80 documented workflows, mix of old and newer stuff.
Honestly? The AI-generated workflows were maybe 40% usable as-is. The real issue wasn’t the conversion itself—the platform did pull structure and logic reasonably well—but all the implicit assumptions buried in our old docs suddenly became obvious. Like, a diagram would show “validate data” but not actually specify what validation rules applied, which systems had the authoritative data, or what should happen if validation failed.
So yes, you get a skeleton fast. But you’re rebuilding the nervous system. What actually saved time was using those generated workflows as a starting point for conversation with the business teams instead of starting from scratch. That part moved faster than I expected.
For timeline, we went from thinking we’d need 4 months of solid engineering work to maybe 8 weeks of lighter work spread across more people. But that’s because we had to involve the teams who understood the actual business logic, not just the documented flow.
My advice: don’t assume the diagrams are the source of truth. They’re the skeleton. Budget for the work to flesh them out.
From what I’ve observed in similar migrations, the conversion tools get you maybe 50-60% of the way there. The gaps usually fall into three categories. First, your diagrams probably don’t capture exception handling and edge cases—they show the happy path. Second, system-specific logic and data transformations rarely translate directly because diagrams abstract those details away. Third, validation rules, permissions, and business policy are often documented separately or not at all.
What I’ve seen work better is treating the AI output as a conversation starter with business stakeholders rather than a finished product. The actual time saved comes from having something concrete to critique and refine, rather than building from a blank canvas. The rework is real, but it’s more focused. You’re not rewriting everything—you’re filling in the blanks and correcting assumptions.
I’d budget conservatively: assume 40-50% of generated workflows need significant rework. That gives you a more defensible timeline to your CFO and sets realistic expectations with your team.
The conversion efficiency depends heavily on how well your legacy diagrams actually document your processes. If they’re comprehensive and include decision points, error paths, and business rules, you’ll see higher quality output. If they’re high-level flow diagrams, the AI-generated workflows will be skeletal.
In practice, teams see about 30-50% of generated workflows deployable without modifications. The remainder need customization for system-specific integration, business rule refinement, or handling of edge cases. The real value isn’t in eliminating rework—it’s in parallelizing it. Instead of one team sequentially building 60 workflows, you can have multiple teams working on refinement simultaneously.
Generated workflows usually need 40-60% rework for production use. Use them as templates, not finished products. Parallelizing refinement work actually saves the most time, not the generation itself.
I’ve run through this conversion scenario a few times, and the honest take is that AI-generated workflows give you a solid foundation—maybe 40-50% of what you need deployed—but you’re always going to need refinement for your specific business logic.
What’s changed for us is having separate dev and production versions of workflows. You generate in dev, test thoroughly, handle the edge cases and business rule tweaks, then promote to production only when it’s solid. That separation means your team can iterate without breaking anything live.
The real timeline win isn’t that generation is instant. It’s that you can parallelize. Multiple teams working on refinement simultaneously instead of sequentially building from scratch. We went from 3-4 months of engineering effort to maybe 6-8 weeks of lighter work spread across more people.
For your CFO conversation, frame it as reduced engineering intensity and faster time to value, not elimination of migration work. That’s more defensible and sets realistic expectations.