I’ve been reading about AI workflow generation tools that claim you can describe what you want in plain English and they’ll spit out a ready-to-run workflow. That sounds incredible for our migration timeline, but I’m skeptical.
We’re moving from a proprietary BPM system to an open-source stack, and I want to understand if there’s a real way to accelerate this without rebuilding everything halfway through. Right now our team is manually recreating workflows in the new system, which is slow and error-prone.
The question I keep circling back to is: if I write out a migration brief describing how our current workflows behave, can a tool actually generate something that runs without significant engineers reworking it? Or is this one of those “it generates something” situations where you spend three weeks debugging edge cases anyway?
Our finance team wants to know if this affects the ROI calculation for the migration. If workflow generation can genuinely cut manual recreation time, that changes the cost picture. But if it’s just generating scaffolding we have to rebuild, it’s not worth the evaluation overhead.
Has anyone actually used this kind of tool during a migration where it saved real time, or is this still pretty theoretical?
I tested this during a process reengineering project, not a full migration, but similar challenge. I described our current invoice reconciliation workflow in plain text and fed it to one of these generators. It produced something that was genuinely 60-70% there, which was better than I expected but not production-ready.
The generated workflow got the basic logic right—conditions, data flows, logic gates. What it missed were the edge cases and specific field mappings to our actual system. I’d estimate we spent about 30% of the time rebuilding versus if we’d started from scratch, but that 30% savings added up when we were doing this for 20+ workflows.
The real value wasn’t the initial generation—it was that having a structured draft meant our team could review and iterate faster than starting from blank JSON or a blank canvas in the builder. It changed the work from “write the whole thing” to “fix the parts the generator got wrong.” That’s worth its weight in evaluation budget.
For a migration, I think it depends on how standardized your workflows are. If you have 30 similar order processing flows, the generator learns patterns and gets better. If you have 30 completely unique workflows, manual might actually be faster.
We explored this for a workflow migration between BPM systems last year. The honest answer is that plain language generation works, but not the way vendor marketing makes it sound.
What we found: if your processes are reasonably standard and well-documented, the generation is genuinely useful. It handles 50-60% of the workflow logic correctly. The parts it struggles with are the domain-specific business rules and the integration points where your workflow talks to other systems.
We spent time writing clear migration briefs rather than vague descriptions, and that made a huge difference. A vague brief like “handle customer requests” will generate garbage. A specific brief like “check inventory, if available create order and notify customer, if unavailable trigger reorder process” generates something your team can actually build on.
For ROI calculation: we saved roughly three weeks of engineering time across 15 workflows, but we also spent a week writing detailed briefs and reviewing generated outputs. So net savings was about two weeks. That was meaningful, but not transformational. The bigger win was using generation as a starting point so discussions with business stakeholders could happen faster.
I’m going to be honest—the gap between what sounds possible and what I’ve actually seen work is pretty significant. We tried using plain language generation for a smaller workflow during evaluation. It generated something, sure, but it became obvious pretty quickly that a senior engineer could have written it from scratch faster than they spent debugging the generated version.
Now, that doesn’t mean it’s useless. For simpler workflows or as a communication tool, it’s great. Your business team can describe a process, see it visualized, say “yes, that’s what we meant” or “no, this is wrong,” and iterate faster than if they were looking at technical specs.
But production-ready? I haven’t seen that yet. More like 50-70% done on average. What changes the ROI is whether you’re doing this to accelerate business validation or to replace engineering work entirely. If it’s the former, it’s genuinely worthwhile. If it’s the latter, you’re probably going to be disappointed.