I keep seeing claims that you can just describe what you want automated and have AI generate a ready-to-run workflow. That sounds incredible if it’s real, but I’m skeptical. In my experience, the gap between “here’s what we want” and “here’s what actually works in production” is enormous.
We’re planning an open-source BPM migration and one of the selling points I’m hearing is that business stakeholders can describe their processes and AI generates the automation. The appeal is obvious—it would cut down on the back-and-forth between business teams and engineering. But I need to understand what “production-ready” actually means here.
So my questions are:
- Has anyone actually done this? Like, you described a workflow in plain language and got something that ran without major rework?
- How complete was it? Did it need tweaking, or was it legitimately deployment-ready?
- What types of processes did this work well for versus where did it fall short?
- If it worked, how much engineering time did it actually save compared to traditional specification → build approach?
- What got lost or misunderstood in the AI generation that you had to fix manually?
I’m not asking for theoretical discussion—I want to hear from people who’ve actually tried this and what the reality looked like.
I’ve actually done this a few times now and it’s genuinely useful, but not in the way the marketing suggests.
What actually works: you describe a straightforward process—like “when a customer submits an order, verify inventory, create an invoice, send confirmation email”—and the AI generates the skeleton. It handles the obvious flow, the basic error paths, sometimes even the right API calls if it recognizes the systems involved.
What doesn’t work: anything with complex conditional logic, edge cases, or system-specific quirks. I described one workflow and it generated code that assumed a happy path only. All the “what if the API times out” or “what if the customer has no email” stuff? That’s still on you.
The real time savings came not from skipping engineering entirely, but from skipping the “write the boilerplate” phase. Instead of an engineer spending 40% of their time on scaffold code and error handling structure, they’re now spending 20% and 80% on actually making it handle reality. That’s a meaningful win, but it’s not “business person describes, AI ships”.
For your migration, I’d expect the AI to handle the workflow skeleton pretty well—it could probably generate the right BPMN structure for your common process types. But you’d still need someone to validate that it’s capturing the business logic correctly and that all the integration points are actually correct.
We tried this with a few processes during our automation rollout and the results were mixed but mostly positive. The key factor is how well-defined your process actually is before you describe it.
If you go in with a vague description, you get vague output. If you give structured information—“these are the inputs, these are the decision points, these are the outputs”—then the generated workflow is actually pretty usable. We got one invoice processing flow that barely needed any changes. Another one for approval routing needed maybe 30% rework because the rules were more complex than our initial description captured.
Production-ready really means “testable and deployable,” not “perfect.” What the AI gave us was a solid foundation that caught obvious issues and had reasonable structure. We still had to test it, tweak the error handlers, and validate the business logic, but we skipped the “what’s even the right structure for this” debate.
For process types, simple sequential + decision workflows worked great. Anything requiring human judgment or complex escalation rules needed more engineering attention.
Plain language generation works when your process is well-understood and relatively standard. The quality of the output is directly proportional to the quality of your description.
What I’ve observed is that AI-generated workflows are strongest for the common patterns—approval chains, data validation, notification sequences. They struggle when your process has unusual edge cases or requires knowledge of your specific system behaviors.
The honest assessment: you’re not replacing engineering, you’re accelerating it. The value isn’t “describe it and ship it,” it’s “describe it and have a 70% complete version ready for engineering review.” That does save real time—usually 30-40% of development time on straightforward processes.
yes, it works but not perfectly. simple sequential workflows? pretty good. complex logic? still needs rework. saves maybe 30-40% engineering time on standard processes. production-ready means testable, not flawless.
Works well for standard process patterns. Describe clearly: inputs, decisions, outputs. Expect 30-40% rework on complex workflows. Good for prototyping.
We’ve actually used AI-generated workflows extensively and the results surprised us in a good way. Plain language generation isn’t magic, but it’s legitimately useful for certain process types.
With Latenode’s approach, you describe what you want—customer order received, check inventory, create invoice, send confirmation—and the system generates a working workflow. For straightforward processes, what comes out is genuinely deployable. For complex ones, it gives you a solid starting point that engineering can refine rather than build from scratch.
The workflows it generates handle the obvious integration points, basic error handling, and the core decision logic you described. We’ve seen it handle about 70% of the logic correctly for standard processes. The remaining 30% is usually domain-specific rules or edge cases that require some engineering judgment.
Where this saves real time is in the discovery phase. Instead of business people writing specs that engineers interpret, engineers spend time validating and refining AI-generated workflows. That conversation is productive in a way specs aren’t.
For your BPM migration, this approach could let you prototype your critical processes in parallel while you’re planning the technical migration. You get working examples to validate against your actual needs, and you find incompatibilities before you’re committed to the migration path.
Check out what AI-powered workflow generation looks like in practice: https://latenode.com