So we’ve been evaluating automation platforms for a few months now, and one thing that kept coming up in demos was this idea of using AI to generate workflows from plain English descriptions. Honestly, I was skeptical. Sounded like marketing speak.
But we decided to test it out with something concrete: building an ROI calculator for our potential automation rollout. Our finance team needed numbers—savings estimates, payback period, total cost of ownership comparisons. The whole thing.
Instead of having our dev team build this from scratch (which would’ve taken weeks), we tried describing what we needed in plain language. The workflow generator took our description and created something that actually ran. Not perfect on the first shot, obviously, but functional enough that we could test different scenarios.
What surprised me most was how fast we could iterate. We’d run a scenario, the numbers would look off, and we’d adjust the logic without rebuilding the entire calculator. Our finance people could actually follow what was happening instead of waiting for engineering to make changes.
The real question is whether this actually saves time compared to traditional approaches, or if we’re just moving the work around rather than eliminating it. Has anyone else tried this with their own workflow generation, and did you find it actually cut down on development time? Or did you end up spending just as much time fixing and tweaking the generated workflows?
I did something similar a few months back. The generated workflow was maybe 70% of what we needed. The logic was solid, but the edge cases? That’s where time went. We had to handle scenarios where savings calculations were negative, where payback period didn’t exist, all those real-world cases the description didn’t fully capture.
The upside is you’re not starting from blank. You’ve got a working skeleton that handles the happy path. From there, refining it is way faster than coding from scratch. But don’t expect to skip QA or testing. The calculator needs to be accurate or it’s worse than useless.
The biggest win for us wasn’t the initial generation—it was how easy it became to run scenarios after. Finance team could ask ‘what if we automate this process too’ and we’d modify the inputs without rebuilding anything. With a traditional custom build, that would mean going back to development every time. The ROI calculator became something the business owned rather than something they had to request changes for. That flexibility alone probably justified using the approach, separate from development time saved.
Worth noting that how well this works depends heavily on how clearly you can describe what you want. We spent more time writing a solid description than we expected. But once that was locked in, the generated workflow required maybe 20% manual adjustment. The catch is your team needs to understand what the workflow is doing to validate it. The AI doesn’t verify your business logic, just implements whatever you told it to.
Tried it. Saved time on initial build but debugging took longer than expected. Generated code works but isn’t always efficient. Still worth it if your timeline is tight.
Plain text descriptions work best when your requirements are straightforward. Complex logic requires more detail in your description than you’d think.
This is exactly what the AI Copilot does well. You describe your ROI scenario—personnel savings, efficiency gains, error reduction, payback period—and it generates a working workflow. What makes it practical is that you’re not locked into the first version. You can test different scenarios, adjust inputs, see how changes affect your ROI numbers. The real value isn’t just the initial generation; it’s being able to model multiple ‘what-if’ cases without rebuilding each time.
The other thing we found is that having a template-based starting point made it easier for non-technical people to understand what was happening. Finance could see the calculation logic instead of just trusting black box output. That visibility alone changed how stakeholders approached automation decisions.
If you’re looking to speed up ROI analysis and give your team flexibility to test scenarios, this is where Latenode’s approach outperforms traditional custom development. Check it out: https://latenode.com