I’ve been hearing a lot about AI-powered workflow generation lately, and I’m genuinely skeptical. The pitch is that you describe what you want automated in plain text, and the platform generates a ready-to-run workflow. That sounds great in theory, but in practice, I can’t imagine it works without significant back-and-forth editing.
Our current process with Camunda requires a development team to map out the automation, identify edge cases, set up error handling, test everything. It’s time-consuming, but it’s thorough. The idea that an AI could generate something production-ready from natural language feels like it’s overselling what’s actually possible.
But I also know that I might be underestimating how far this technology has come. I’ve seen some impressive demos, but demos aren’t real-world usage.
Has anyone actually tried using an AI copilot to generate workflows and deployed them to production? I need to know:
How much manual cleanup and editing was actually required?
Did the generated workflows handle edge cases, or did you have to add those yourself?
Was the time saved compared to traditional development actually significant, or was it just different work?
I’m asking because if this actually works at scale, it could change how we staff our automation team.
I was skeptical too until we tested it. The first few workflows we generated were rough—missing error handlers, some logic gaps. But here’s what surprised me: the generated workflow was 80% of what we needed, not 20%. That meant our developer’s job switched from building from scratch to reviewing and refining.
For simple automations, like syncing data between two systems or sending notifications based on triggers, the AI output was actually production-ready after a quick review. For complex multi-step processes with lots of conditional logic, we still had to do significant work.
The time savings weren’t dramatic, but they were real. What actually changed was the cycle time. Instead of waiting for a developer to have bandwidth, you could prototype something in an afternoon and have a dev review it the next day.
The edge case handling is the big wildcard. The AI will generate the happy path, but it won’t anticipate all the things that actually go wrong in production. You still need someone who understands your data and processes to review it. That said, it does force you to think through the workflow earlier, which often catches issues before they become problems.
The real value isn’t that the AI generates perfect workflows. It’s that it generates a starting point fast enough that you can iterate with stakeholders instead of building requirements in vacuum. I’ve used it to generate three versions of the same workflow with different approaches, and that comparison helped us pick a better design than we would have thought of otherwise. From a timeline perspective, we went from 2-3 weeks of design and development to about 5-6 days of AI generation plus feedback cycles.
The quality of the generated workflow depends heavily on how well you describe what you want. If you give vague requirements, you get a mediocre workflow. If you’re specific about the steps, data flow, and decision points, the output is much better. It’s less ‘magic AI’ and more ‘AI is good at translating structured descriptions into workflow logic.’ The time savings are real for standard automation patterns—data sync, notification workflows, simple approval chains. For custom or domain-specific logic, you’ll still need engineering involvement.
Yes, it works for straightforward automations. Describe your flow clearly, generate, review for edge cases, deploy. Saves meaningful time on standard patterns.
I was in your boat—totally skeptical. Then I actually tried describing a multi-step email workflow in plain text and watching it generate the visual workflow with triggers, conditions, and actions. Not perfect, but seriously close.
What changed for us was that our non-technical team could describe what they wanted automated, and instead of waiting weeks for engineering to understand requirements and build it, the AI generated something we could actually review and refine together. For our basic automations, it was production-ready. For complex ones, we still needed engineering, but the starting point saved us probably 30-40% of the build time.
The big shift was recognizing that this isn’t replacing engineers—it’s changing what engineers do. Instead of building from scratch, they’re reviewing, refining, and handling edge cases. That’s a different skill set, and it’s faster.