We’re seriously looking at moving from our old BPM system to open-source, and I’ve been trying to figure out if we can actually use AI to help us map out what we’re running today into something that works tomorrow.
The real question I’m hitting is: when you describe a legacy process in plain language and let AI generate a workflow, how much of the actual complexity actually makes it through? I’m not talking about simple stuff—I mean the edge cases, the conditional logic that nobody documented, the manual workarounds that exist in people’s heads.
I’ve been reading about AI Copilot and how it can take a description and spit out a pilot-ready workflow. That sounds amazing, but in my experience, processes are messier than anyone wants to admit. We have approval chains that change based on department, data transformations that happen in three different systems, and error handling that’s basically tribal knowledge.
Has anyone actually used this kind of workflow generation to move from legacy BPM? I’m trying to understand: do you end up rebuilding most of it anyway, or does the AI actually capture the complexity well enough that you’re ahead of where you started?
Also, if you do generate a workflow this way, how do you even validate it against your original process without just running it live and finding out what breaks?
I went through this with our old Camunda setup about two years ago. We had these absurd workarounds where certain approvals would get routed entirely differently based on document type, and it was never written down anywhere.
When we tried the AI generation approach, it got the happy path right. Like, 80% of the basic flow was there and we didn’t have to draw it out manually. But all those edge cases? The AI caught maybe half of them because we had to actually describe them first. That’s the real bottleneck.
What worked for us was using the AI output as a starting point, then having the people who actually use the process walk through it and mark what was wrong. We did maybe three or four iterations like that before we had something close to real. The real time saver wasn’t the AI generating the workflow perfectly—it was saving us from drawing it from scratch and then having to fix it anyway.
But yeah, you’re going to rebuild parts of it. Just less of it than if you had to do everything manually.
The complexity survival rate depends entirely on how well you can articulate the process going in. I’ve seen teams try this with processes that have been through five years of evolution and zero documentation. In those cases, the AI output was basically unusable because the input was so vague.
But when we worked with a team that had process documentation—even rough stuff—the AI got maybe 70-75% right on the first pass. The missing pieces were almost always the error handling paths and the conditional logic that only matters 5% of the time.
The bigger issue I noticed is that the AI will make decisions about how to structure things, and those decisions don’t always match how your team actually thinks about the process. So even when it’s technically correct, you still need to review it against how your people work. That adds time, but it’s still faster than building from scratch.
Start with your simplest, most documented process as a pilot. Don’t try this with your most complex workflow first.
The key insight here is that AI workflow generation works best as a starting point for discovery, not as a replacement for understanding your actual processes. I’ve seen organizations use this approach where they generate the workflow, then treat that output as a conversation starter rather than a final deliverable.
What I’d recommend is running a test on one small process. Have the AI generate it, then do a validation session where the actual process owners walk through the generated workflow. You’ll see pretty quickly where the gaps are. The real value isn’t getting a perfect workflow—it’s getting a structured representation of what you do that you can iterate on.
For ROI purposes, the time savings are real if you’re honest about what you’re getting. You’re cutting the time to get a first draft down from weeks to days. Validation and refinement still takes time, but you’re doing it against something concrete instead of starting with a blank canvas. That shift alone is worth the effort for larger migrations.
AI gets the flow right, misses edge cases. Test on simple process first. Expect 2-3 revision cycles minimum for complex workflows. It’s faster than manual, still requires validation.
We actually ran into this exact problem. Our old system had all these hidden workflows that nobody documented properly. When we tried using standard tools, we had to manually rebuild everything anyway.
Then we switched to using Latenode’s AI Copilot approach. Here’s what changed: we described our main processes in plain language, and the platform generated workflows that captured way more detail than we expected. The real difference was that the AI understood context better, so it wasn’t just creating basic flows—it was building workflows with actual conditional logic and error handling built in.
What sealed it for us was running the generated workflows against our actual data. We could test them before going live and see what needed tweaking. The edge cases still showed up, but we found them in controlled testing instead of in production. And the time savings were huge—we went from a projected three month migration to about six weeks.
The validation part still matters, but you’re validating a working model instead of starting from square one. That’s a different ballgame for ROI.