I’m evaluating workflow platforms partly because I want to reduce our Camunda licensing costs, but also because I’m genuinely curious whether AI-powered workflow generation actually works or if it’s just marketing language that falls apart in practice.
The pitch I keep hearing is: describe what you want the automation to do in plain language, and the platform’s AI generates the workflow for you. That sounds amazing in theory, but I’ve been in enough automation implementations to be skeptical. Usually the gap between “what we described” and “what actually works” is massive.
I’m specifically wondering: when you describe a workflow in English, how much rework happens before it’s actually production-ready? Do you end up rewriting 80% of the generated workflow, or is the AI doing something genuinely useful? And how much of the rework is because the AI misunderstood the requirement versus just needed normal refinement?
I ask because if the rework is substantial, then we’re not really saving the developer time that would justify moving away from Camunda. But if the AI output is actually a solid starting point, that changes the math.
Has anyone actually used AI Copilot or similar tools to generate automations? What percentage of the generated workflow survived to production unchanged, and where did you have to step in and rebuild?
I’ve done this with AI-generated workflows, and honestly, it depends heavily on how specific your English description is. If you just say “send an email when we get a new lead,” the AI will give you maybe 40% of what you need. But if you document the exact steps—which fields matter, what conditionals apply, what the error cases are—then the generated workflow is maybe 70-80% there.
What surprised me is that the AI doesn’t usually get the logic wrong. It understands conditionals and branching fine. What it misses are edge cases and the specific data transformations your company needs. So we’d get a workflow that was structurally correct but passing the wrong fields, or not handling null values, or not matching your actual data schema.
The real time savings came from not having to think through the entire structure from scratch. Instead of starting with a blank canvas, we started with something that was architecturally sound but needed refinement. That’s definitely faster than Camunda custom development, where you’re writing BPMN from zero.
The gap between generated and production-ready is real, but it shifted where we do the work. Instead of developers writing code, we had semi-technical people describing workflows, the AI generated something, and then devs spent time fixing data mappings and edge cases. That’s faster than starting from scratch, but it’s not hands-free.
Where we saved actual time was on straightforward workflows—the ones that are probably 60% of your automations. For those, the generated version went to production with maybe 10-15% tweaks. For complex workflows with intricate conditional logic, the generated output was more of a prototype we had to rebuild. But even then, having a working prototype meant less design time upfront.
I’ve seen this work well when the workflow is well-understood and documented. We described one expense approval flow in detail—who can approve at each tier, what conditions trigger escalation—and the AI nailed about 85% of it. Obviously we had to tune the rules and add some custom validation, but the skeleton was right. Less clear workflows, like ones with fuzzy business logic, generated something that was structurally okay but missed intent.
I’ve tested this extensively with AI Copilot workflow generation, and the reality is closer to the optimistic side if your description is detailed enough. The key is that the AI isn’t trying to be perfect—it’s trying to give you a working foundation that you can iterate on.
For straightforward automations (data entry, notifications, simple approvals), I’ve had workflows go from plain text description to 90% production-ready in one go. The AI understands common patterns and maps them correctly. For more nuanced workflows with business-specific logic, you’re looking at 60-70% there, which still beats writing BPMN from scratch.
What changes the math is iteration speed. With traditional custom development, you’d describe a workflow, wait for dev, review it, request changes, and wait again. With AI-generated workflows, you describe it, get something immediately, spot the gaps in minutes, refine the description, and regenerate. That loop is fast enough that you actually save real time even if the first version isn’t perfect.
I’ve had better luck when I describe workflows in structured way: step by step what happens, what data gets used, what conditions matter, what the success and failure paths are. That level of detail helps the AI understand the full context. Vague descriptions generate more rework.
The consolidated subscription pricing helps here too. You can experiment with different descriptions and regenerations without worrying about licensing costs per generation, so you’re not being conservative with your workflow experimentation.