We’ve been evaluating how to pitch an open-source BPM migration to our finance team, and I’ve been looking at different ways to speed up the business case. One thing that keeps coming up is this idea of describing what we need in plain English and having AI generate the workflow.
On paper, it sounds perfect. Instead of spending weeks documenting requirements and having developers translate them into workflows, we just describe the process and get something production-ready back.
But I’m skeptical. Every tool I’ve used that promises this kind of shortcuts ends up needing significant rework. The generated version gets you 60-70% of the way there, then you’re customizing for another month anyway.
I’m curious whether anyone’s actually pulled this off without major rebuilds. When you describe a migration workflow in plain text—like “move customer data from Camunda to open-source setup while maintaining audit logs”—does the AI actually understand the constraints, or does it give you something that looks right until you test it in staging?
More importantly, if you do end up rebuilding half of it, how much time do you actually save versus just having a developer build it from scratch?
I ran into this exact issue last year when we were migrating off a legacy system. We tried using AI to generate some of the workflows and honestly, the initial output was fine for maybe 40% of what we needed. The AI nailed the straightforward stuff—data mapping, basic conditionals, that kind of thing.
But the moment we needed anything with real logic—like handling edge cases or integrating with our specific error handling patterns—we had to rewrite it. The problem wasn’t the AI being dumb. It was that “plain language” is too vague. When I said “maintain audit logs,” I didn’t realize I should’ve specified whether we wanted immutable logs, where they’d be stored, retention policies, all that.
What saved us wasn’t the AI generation itself. It was using it as a starting point for conversations with the team. We’d generate something, break it down together, figure out what was missing, and then build properly. Took about the same time as building from scratch, but we caught assumptions earlier.
If you’re going to try this, be specific in your descriptions. Write out the edge cases, the constraints, everything. The AI is better at translating detailed specs than at guessing what you mean.
The real issue I’ve seen is that AI-generated workflows work great for happy path scenarios but fall apart with the variation that exists in real processes. When you’re migrating from Camunda, you’re not just moving a simple flow—you’re handling years of accumulated business logic, exception cases, and workarounds that nobody documented properly.
I’d estimate you save maybe 15-20% on total timeline if you use AI generation as a scaffold. You get the structure faster, which is valuable. But you still need domain experts validating that it actually reflects how your business works. The rebuild often happens because the first version is technically sound but doesn’t match your actual process.
What works better is using it iteratively. Generate a draft, run it through a QA cycle with business stakeholders, let them point out what’s wrong, regenerate with that feedback. It’s slower than it sounds, but you avoid the big rewrite in month two.
Plain language workflow generation works best when your requirements are well-defined and you have clear acceptance criteria upfront. In migration scenarios, those conditions rarely exist because you’re learning the existing system as you go.
The rebuilt workflows I’ve seen typically fail because AI misses implementation details around data validation, timeout handling, and cross-system consistency. These aren’t language problems—they’re domain problems. The AI can’t know your Camunda instance has quirks around how it handles large transaction batches, or that your data cleanup script runs at 3 AM specifically to avoid production queries.
I’d recommend treating AI generation as prototyping, not development. It’s useful for quickly sketching out process flow and validating the general structure. But plan for a full development cycle afterward where you add your actual constraints and test against real data volumes.
yeah we tried it. got 50% of what we needed without rework. rest needed tweaks anyway. good for demos n prototypes, not for production-ready code right out of the box
I’ve dealt with this exact problem, and the key insight is that plain language generation works best when you pair it with a platform that lets you iterate quickly and safely.
What I found is that Latenode’s AI Copilot actually handles this better than other tools because you can describe your workflow, get something generated, test it in a dev environment separate from production, and then refine it based on what breaks. The dev/prod environment separation means you’re not constantly rebuilding from scratch—you’re just patching and iterating.
The workflows I’ve generated this way needed maybe 20-30% customization, which is way better than starting from zero. The big difference is having a platform that makes iteration cheap enough that you don’t feel like you have to get it perfect the first time.
Real migrations I’ve seen work well using this approach: describe the process, generate the initial workflow, run it on sample data in dev, identify gaps, regenerate with more specific prompts, then roll to production. Takes about the same calendar time as manual development but the thinking work is distributed across sessions instead of front-loaded.