I’ve been skeptical about this whole “describe your workflow in plain English and the platform builds it for you” thing. It sounds great on the marketing page, but I’ve been through enough migrations to know that’s usually where you end up spending three months reworking something that was supposed to be production-ready in a day.
We’re at the point where we need to move from our current BPM system to something open-source, and the idea of using AI to translate a process description into a runnable workflow is interesting. But I need to understand what actually happens in that gap between the initial description and deployment.
The migration brief we have is pretty detailed. It maps out our data flows, event triggers, and integration points. If I fed that into a platform that claims it can generate a workflow from plain language, what would I actually get back? Is it a rough scaffold that still needs serious engineering work? Or can it actually understand the nuances of our specific business logic?
Has anyone actually used this kind of AI workflow generation for something real, or does it always come down to “the AI built 40% of it and we rebuilt the other 60% ourselves”?
I was in your shoes about six months ago. We tried this with a payment reconciliation workflow, which seemed like a good test case. The AI generation was actually useful, but not in the way I expected.
What happened was that the generated workflow captured the happy path pretty accurately. The system understood our data mappings and the main event flows. But the edge cases and error handling required manual work. We probably saved about 60% of the build time, but not in the way the marketing promised.
The real value came from iteration. Instead of describing the whole workflow once and expecting it to work, we described it, reviewed what the AI generated, fixed the obvious gaps, fed it back, and iterated. After three or four rounds, we had something production-ready. The total time was still less than building from scratch, but it wasn’t instant.
For migration work specifically, I think the AI generation works best when you’re translating from an existing system where the logic is already documented. It struggles less with reimagining processes and more with translating what you already have.
The key is to have a detailed starting point. If your migration brief already documents the process flows, data transformations, and integration points, the AI has a lot to work with. We took our Camunda process definitions and fed them as input to the generation process, and the platform was able to interpret them pretty effectively.
What took work was customization. The AI nailed the main workflows but missed some of our custom validation logic and specific business rules. That said, having a mostly complete workflow that needed refinement was infinitely better than starting from scratch. The review and adjustment phase took maybe 30% of what a full build would have taken.
Plain language to production is possible, but the devil is in the definition quality. We found that describing workflows at too high a level gets you vague scaffolding. But if you provide concrete examples with actual data flows and error scenarios, the AI generation produces something much closer to deployable.
Our team spent time upfront structuring how we described the migration scenarios, including examples of edge cases and specific integration requirements. The generated workflows required less rework because the input was precise. The time saved depended entirely on how well we set up the initial description.
We ran into this problem when we were evaluating how to handle our migration planning phase. We used Latenode’s AI Copilot to generate workflows from our process descriptions, and the output was way better than I expected. The platform actually understood the data transformations and event flows from our plain language descriptions.
What we got wasn’t completely production-ready, but it was substantial. The scaffolding was there, the integrations were mapped correctly, and the main logic paths were right. We spent maybe a day refining the error handling and custom business rule validation.
The real difference was that we had something tangible to iterate on instead of starting from a blank canvas. We could test the generated workflow, see where it fell short, and make targeted fixes. For our migration case, we went from bare workflow to deployable version in about a third of the time a manual build would have taken.
The AI wasn’t perfect, but it understood our domain well enough that we spent our time on thoughtful refinement rather than basic implementation.