I’ve been seeing a lot of claims about AI that can take a description of a process and generate a fully working workflow. Sounds amazing in theory, but I’m skeptical about the execution.
Our team has been asked to prototype a potential migration from our current Camunda setup to an open-source BPM solution. One of the supposed benefits I keep reading about is something called “AI Copilot Workflow Generation”—where you describe what you want and it builds the workflow for you.
Here’s what I’m trying to understand: how much of the workflow actually comes out ready to use? Like, if I describe a procurement process with approvals at different stages, does it actually create something I can deploy, or does it generate a skeleton that needs heavy customization?
And more importantly, what happens when the workflow hits reality? A description of a process is never as detailed as an actual process. How does the AI handle ambiguity around business rules, edge cases, or system integrations that aren’t explicitly mentioned?
Has anyone actually used workflow generation tools and gotten something deployable on the first shot? Or is this one of those tools that sounds magical but really just saves you from writing the boilerplate?
I tested this exact thing last month and honestly, it’s not magic but it’s also not boilerplate.
We tried describing a fairly standard expense approval workflow—employee submits, manager reviews, finance approves, payment processes. The AI generated about 70% of what we needed. The core logic was there, the conditional branching made sense, and the integration points were identified.
What was missing was the stuff nobody mentions in a plain-English description. Like what happens if an approval is rejected. Or how to handle edge cases like a manager approving their own expense. Or specific integrations with our accounting system that weren’t mentioned.
But here’s the thing: rebuilding that 30% took maybe 4 hours instead of the 2-3 days it would’ve taken to build from scratch. So the time savings are real, just not as dramatic as the marketing suggests.
The bigger value I found was in the structure itself. The AI thought through the workflow logic in a different way than I would have, and it caught some edge cases I hadn’t considered. So even the parts I rebuilt, I was building them smarter.
For a BPM migration, it’s definitely worth trying. Describe your top 3-5 processes and see what it generates. If you get 60-75% useful output, you’re ahead. If you get 40%, it’s still faster than starting from zero.
Generated workflows are functional but not sophisticated. What I mean is, the AI can build a basic process with approvals, conditions, and integrations from a description. But it doesn’t understand your company’s quirks or how your teams actually work versus how they’re supposed to work.
We described a sales process once and got back a workflow that was technically correct but would never work in practice because it didn’t account for how our sales team actually negotiates pricing outside the system. The workflow was deployable but useless.
That said, for standard processes like HR onboarding, expense management, or basic order workflows, generation actually works well. The more common your process, the better the output. Unique or heavily customized processes need more human input.
If 80% of your BPM processes are standard enterprise stuff, generation tools are worth the time. If your company has heavily customized processes, they’re less valuable but still a decent starting point.
Workflow generation from descriptions produces functionally correct but not optimized workflows. The difference matters. A generated workflow will work, but it might not perform well under load, handle errors gracefully, or scale cleanly.
The technology is genuinely useful for rapid prototyping and for teams evaluating a platform. “Can this tool handle our processes?” gets answered quickly with generation tools. That’s the real value during migration evaluation.
For production deployment, plan on 2-3 iterations where you take the generated workflow, test it, find issues, and refine it. The first version is rarely deployment-ready despite what the vendors claim.
For your BPM migration, use generation to accelerate the evaluation phase. Get workflows for your key processes, test them, and see how much customization they’d need. That informs your decision about whether the platform is suitable for your organization.
Generated workflows are 60-70% functional on first shot. Standard processes work better than custom ones. Fine for prototyping, needs refinement for production.
I actually tested the AI Copilot Workflow Generation feature for a client migration and came away genuinely impressed. We described a multi-step approval and reporting workflow for their sales department, and the system generated something that was about 80% production-ready on the first pass.
What made the difference was how much the generated workflow understood about conditional logic and data flow. It didn’t just create a skeleton—it actually thought through approval rejections, escalations, and what data needed to move between steps.
We tested it immediately and hit maybe 3-4 cases it didn’t account for. But compared to building a workflow from scratch, we saved the bulk of the architecture work. Felt like starting 75% ahead instead of at zero.
The best part for migration evaluation was speed. We could model their 5 key processes in a day instead of a week. That massively accelerated the ROI conversation because they could see exactly how their workflows would work on the new platform.
For your Camunda migration, worth testing the generation on your top processes. You’ll either get 60-80% working workflows and save massive time, or you’ll realize a process is too custom and needs manual building. Either way, you learn what you’re dealing with fast.