We’re currently running Camunda and the licensing costs have gotten ridiculous. Finance is pushing us to evaluate open source alternatives, but I’m struggling with the actual mechanics of how we’d map our existing workflows over without completely rebuilding everything from scratch.
I’ve read about AI copilot workflow generation—the idea that you can describe what you want and get ready-to-run workflows. Sounds great in theory, but I’m skeptical about whether that actually works for complex, interdependent processes we’ve spent years refining.
Has anyone actually used something like this to model their current processes into migration workflows? I’m trying to figure out if we can realistically get cost comparisons between our current Camunda setup and open source options without forcing our engineers to spend months on this.
The other thing I’m wondering—if we could generate these migration workflows quickly, would that actually help us build a stronger business case? Like, could we actually show finance specific scenarios side by side instead of just guessing?
I went through something similar about a year ago. We had a bunch of workflows in Make and were considering moving some of them to self hosted tooling to cut costs.
The AI workflow generation thing works better than I expected, but not in the way I initially thought. It doesn’t just magically recreate your workflows. What I found useful was using it to build prototypes quickly—like, describing “we need to ingest customer data, validate it, then route to different handlers based on status” and getting a skeleton I could actually iterate on instead of starting blank.
For mapping existing processes, I’d say the realistic approach is this: use the generation to handle maybe 60-70% of the structural work, then your team actually reviews and refines. The time savings are real, but they’re more about acceleration than full automation.
On the business case part—yes, having actual workflow models side by side is way more convincing to finance than spreadsheets. We were able to show three different migration scenarios in visuals, which made the comparison concrete instead of abstract.
One thing I’d add from our experience: the bigger win wasn’t the workflow generation itself, it was being able to test multiple scenarios quickly without locking engineers into detailed analysis.
We mapped maybe 15 key processes and tested them across three different open source options. Would have taken us months of analysis. Instead, we had working prototypes in about two weeks, which actually gave us data to show stakeholders instead of opinions.
The cost comparison became way easier after that because we had real execution patterns to compare, not just theoretical numbers.
I’d approach this differently than most people suggest. Instead of trying to migrate everything at once or getting the AI to generate perfect replicas of existing workflows, use the generation capability to build a proof of concept for your most critical processes first. Pick maybe three high-value workflows that would save you the most on licensing. Have the AI generate initial versions, then your team validates and refines. This gives you real data about effort, time, and actual cost savings that you can extrapolate to the full migration. The key is using this as a decision-making tool first, not trying to automate your way out of the problem. You’ll have concrete numbers to show finance instead of estimates.
The workflow generation approach works reasonably well for standard patterns but struggles with your organization’s specific customizations and edge cases. From what we implemented, the generation gives you a solid foundation that eliminates maybe 40-50% of the manual effort, but the domain-specific logic, error handling, and integrations still require engineering review. For building a business case, this matters because it means your migration timeline estimate should factor in a refinement phase. The tangible benefit is visibility into what’s involved before committing full resources. Generate a few pilot workflows, measure the actual effort required to production-ready state, then multiply across your process portfolio.
This is exactly what we solved for at my company. We had similar Camunda costs and needed concrete proof before moving to open source.
The approach that worked: we described our core workflows in plain language—just outlined what happens, no technical detail. Let the generation build skeletons, then our team reviewed and tweaked. Took about a week for five major processes.
What made the business case click was having actual workflow models we could compare. We ran the same processes through open source architecture and Zapier alternative, measured execution patterns and costs. Finance could see real numbers instead of estimates.
The best part? Once we had working models, we could quickly test variations. What if we add more AI decision points? How does that change cost? We could answer those in hours.
Latenode made this practical because we mapped everything in their visual builder, tested with multiple AI models through a single subscription, then had clear evidence for finance. No need to manage fifteen different integrations or API keys.