I’ve been trying to figure out if we can really go from a plain text description of what we need to calculate (labor hours saved, cost per hour, timeline to payback) directly into a working ROI calculator without having our dev team build it from scratch.
We’re a mid-size ops team, and right now every time we want to model the ROI of a new automation project, it takes weeks because we have to brief developers, they build something, we iterate, and by the time it’s done the business priorities have already shifted. It’s frustrating.
I keep hearing that AI can generate workflows from descriptions, but I’m skeptical about how much iteration and rework would actually be needed. Like, if I describe our calculation logic in an email or a doc, how much of that actually translates into something production-ready versus something we’d have to tear apart and rebuild anyway?
Also curious whether the calculator would be flexible enough to handle our specific metrics without us having to tweak formulas constantly as our cost assumptions change.
Has anyone actually tried this workflow generation approach for something as structured as an ROI calculator, or is it better suited for simpler automations?
We went through exactly this last year. Described what we needed for an automation business case calculator to our team, and honestly the first pass was surprisingly close to what we actually needed.
The thing is, the AI got the logic structure right but missed some of our specific edge cases around how we account for training time costs. We had to add a few tweaks, but it wasn’t a full rebuild. Maybe an hour of adjustment instead of weeks.
The real win wasn’t the calculator itself being perfect immediately. It was that we had something testable in days instead of waiting for dev bandwidth. We could actually validate our assumptions with the business team faster.
One thing I’d mention: the plain English part only gets you so far. You need to be specific about what inputs you’re expecting and what outputs matter. If you just say “calculate ROI,” you’ll get a generic calculator. If you describe exactly how you calculate labor savings, what factors matter to your business, and what format you need the output in, the generated workflow will be much closer to what you actually need.
We’ve found that spending 30 minutes writing a clear spec in plain language saves way more time than trying to iterate on a vague workflow generation.
The workflow generation does handle the core logic well, but there’s a practical limitation: it doesn’t know your company’s specific cost structure unless you tell it. I worked on this with another team who tried to generate an ROI calculator and discovered the generated workflow assumed standard labor costs, but they needed custom calculations for different departments with different billing rates. They had to manually adjust the workflow parameters after generation. The generation saved time on structure and logic flow, but not on customization. If your ROI formula is straightforward, generation works great. If it’s complex or company-specific, be prepared for some manual work.
Generated workflows tend to introduce unnecessary complexity if you’re not careful with how you describe the requirements. I’ve seen cases where people described ROI calculations too broadly, and the AI added features that weren’t actually needed. The better approach is to be minimal and precise in your description: list exactly what inputs you need, what calculations happen, and what outputs you want. This produces cleaner, easier-to-maintain workflows. The generation process works well for standard financial calculations, but domain-specific edge cases usually require post-generation refinement.
I’ve built a few ROI calculators using Latenode’s AI Copilot, and honestly the workflow generation handles the structural heavy lifting really well. The thing is, you need to be intentional about your plain English description. Instead of just saying “build an ROI calculator,” I describe the exact inputs (labor hours, cost per hour, project timeline), the formulas (total saved hours × rate = savings, savings ÷ automation cost = payback period), and what I want the output to look like.
The AI Copilot then generates a workflow that gets it mostly right the first time. From there, it’s usually just tweaking variable names or adding business-specific logic that maybe takes a couple hours instead of weeks with developers.
What really saves time though is that you can test the generated workflow immediately with your actual numbers to validate the assumptions. That feedback loop alone cuts your iteration time in half compared to waiting for dev sprints.