I’m trying to tackle something that’s been keeping me up at night. We’re evaluating a move from Camunda to an open-source BPM stack, and finance is breathing down my neck for a detailed business case. The problem is that most of our process documentation is scattered across wikis, Slack conversations, and people’s heads.
I’ve been hearing about AI copilot tools that supposedly can take a plain English description of what you’re trying to do and generate a workflow from it. Sounds amazing in theory, but I’m skeptical about whether it actually produces something usable or if it just creates scaffolding that you end up rebuilding anyway.
Has anyone actually tried this approach? Did it speed up your business case development, or did you find yourself having to rework it significantly? I’m specifically interested in whether the generated workflows were close enough to production-ready that you could use them for ROI calculations and cost modeling.
We tried this last year when we were evaluating moving away from Camunda. The honestly answer is it depends on how specific your process descriptions are. If you give it vague stuff like “handle customer requests”, you’ll get something generic that needs heavy rework. But if you nail down the actual steps, decision points, and error cases, the output is surprisingly close.
What worked for us was treating the AI generated workflow as a first draft, not gospel. We used it to kickstart conversations with the teams who actually own those processes. The time savings weren’t in getting production-ready workflows immediately, but in reducing the back and forth needed to get requirements locked down.
For your business case specifically, I’d say it helps more with the timeline estimates than the actual workflows. You can show finance that instead of spending three weeks documenting all your processes, you’re spending three days getting AI drafts and then a week refining them with the teams.
The real value I saw wasn’t the workflows themselves but how fast you could iterate on them. We were able to generate multiple variations from slightly different process descriptions and compare them. That let us model different migration approaches without hiring consultants.
One thing to watch out for though - the generated workflows tend to assume happy paths. Error handling and edge cases often need manual work. So if your Camunda setup has complex exception handling, budget time for that.
I’ve seen this work reasonably well for straightforward processes but it becomes problematic when you have complex branching logic or legacy workflows with quirky business rules baked in. The copilot tools are good at understanding standard patterns but they struggle with organizational idiosyncrasies.
For building a business case specifically, I’d recommend using the AI generation as a baseline, then having your actual process owners validate and refine them. This approach gives you credible numbers for your business case because they’re grounded in real process complexity, not just what the AI assumed. It also builds buy-in across teams since they’re part of the validation, which matters when you’re asking for budget.
The effectiveness really hinges on your process descriptions quality and documentation discipline. Teams with well-structured process documentation see better results from AI copilot tools. Those starting from chaos tend to get chaotic outputs, which defeats the purpose.
What I’d recommend is treating this as a proof of concept first. Pick three to five key processes, write clear descriptions for them, and see what the copilot generates. Use that to calibrate your expectations before rolling it out across your whole migration scope. That way you’re not betting your entire business case on untested technology.
Start with detailed process descriptions. Use AI output as draft. Validate with process owners. Budget extra time for edge cases and error handling. Works best for standard workflows.
Yeah, we went through exactly this. The key is that plain English descriptions work best when you feed them into something purpose-built for workflow generation, not a generic AI chatbot.
We used Latenode’s AI Copilot to take our process descriptions and generate initial workflow drafts. The difference was that it understood workflow logic, not just language. Our cost model went from “we’re guessing” to “we have baseline versions we can tweak and cost out accurately”.
What made the biggest difference was that the generated workflows weren’t black boxes. We could see the logic, audit it, and adjust cost assumptions based on actual structure. That turned our business case from a spreadsheet guessing game into something finance actually trusted.
You can test this yourself at https://latenode.com