I’m not a technical person, and that’s always felt like a blocker when we wanted to build ROI calculators. We’d have to ask someone on the engineering team to translate what we wanted into actual code, and then we’d wait.
But I’ve been reading about this idea of describing what you want in plain English and having the platform generate a workflow automatically. It sounds too convenient to be true. Feels like one of those things vendors say works but never really does in practice.
Here’s what I’d actually need: A workflow that takes our automation scenario details (tasks per day, hours saved per task, model costs, deployment costs), runs some basic calculations, and outputs a simple ROI number. Nothing fancy, just accurate.
If I could write that out in words and have something generate it, that would actually change how we approach automation planning. Right now we’re bottlenecked on the technical side just to get basic numbers.
Has anyone actually tried this? Or am I chasing vendor marketing?
It actually works, and I was skeptical too. I did exactly what you’re describing about six months ago.
I wrote out what I needed: “Take daily task volume, multiply by hours saved per task, multiply by average hourly labor cost, subtract monthly platform cost, give me the payback period.” That kind of thing. Very basic arithmetic, but I didn’t want to wait for someone to build it.
The workflow it generated wasn’t perfect on the first try, but it was functional. I had to tweak the inputs a bit to match how our business actually calculates costs, but the core logic was there. That’s the key insight—it generated something working that I could then refine, instead of me having to start from nothing.
The biggest difference from what I expected: it didn’t try to be fancy. It gave me something simple that did exactly what I asked for. Then I could actually use it instead of waiting another month for engineering to prioritize it.
One thing though: be very specific about what you want. “Calculate ROI” is too vague. “Take hours saved per month and multiply by our loaded labor cost, then divide by subscription cost” is exact enough that it knows what you’re after.
Your skepticism is justified but the technology has matured enough that this actually works for structured calculations like ROI models. The consistency requirement here is key—you’re not asking for creative work or complex decision logic, you’re asking for arithmetic on known inputs. That’s something language models handle reliably. The catch is precision in your initial description. Generic requests produce generic outputs. Specific workflows with defined inputs and clear calculation steps produce usable results. What I’d recommend is starting with your simplest ROI scenario. Write out the steps in plain language, generate it, test it with actual data, then iterate. Once you have one working model, building variations becomes faster because you can reference the existing structure.
Plain English workflow generation has demonstrated efficacy for deterministic processes with well-defined inputs and outputs. ROI calculations fit this profile precisely. The variables are known, the logical operations are straightforward, and the output format is standardized. Success depends on specificity—vague descriptions fail consistently, while precise articulation of calculation steps produces immediately usable workflows. Organizations implementing this approach typically see 70-80% generation accuracy on first attempt, with minor adjustments required to match domain-specific terminology or calculation preferences. The time savings versus traditional development are substantial, particularly for non-technical stakeholders. The process essentially trades initial precision in description for elimination of development queue delays.
Yes, it works for structured calculations like ROI models. The key is being specific in your description. Generic requests fail; detailed workflows succeed. Test with simple scenarios first.
I’ve seen this work repeatedly. The crucial part is that ROI calculations are deterministic—same inputs always give same outputs. That’s exactly what AI is good at generating.
Here’s what actually happens: You describe your calculation logic in plain language, the AI copilot generates a workflow, you test it with sample data, and it works or needs minor tweaks. Since ROI models aren’t complex compared to actual business logic, generation accuracy is high.
The real productivity unlock is that you move from “wait for engineering” to “deploy today.” You’re not technical, so previously your only option was queue up a ticket. Now you describe it, generate it, validate it against known scenarios, and you’re done.
What we’ve observed is non-technical teams then use these workflows as templates. They build one ROI calculator, then spin up variations for different automation scenarios without touching code. That’s where the efficiency compounds.