We’ve been struggling with justifying automation investments to our CFO. Every time we pitch a new workflow, we get asked the same question: what’s the actual ROI? The problem is, we usually spend weeks building financial models before we can even answer that.
Last month, I decided to try a different approach. Instead of spending time explaining what we wanted to build, I just wrote out the business goal in plain language: “Calculate the time savings and cost reduction when we automate our invoice processing from manual entry to end-to-end workflow.” That’s it. No technical specifications, no architecture diagrams.
I was skeptical it would work, but honestly, it cut our setup time pretty dramatically. We went from “let me get a developer” to having something testable in days instead of weeks. The calculator pulls in real numbers from our accounting system, spits out comparisons against the current manual process, and the math actually tracks.
My question is: has anyone else done this? And more importantly, when you start with a plain text description like this, how much customization do you typically end up doing before you trust the numbers enough to present them to leadership?
This is exactly what we did earlier this year. The big surprise for us was how much time we saved not rebuilding the logic three times because someone misunderstood the requirements.
That said, we still had to tweak the actual data connections. The template got us 80% of the way there, but pulling numbers from our specific systems required a bit of customization. Not code-heavy stuff, just mapping fields and making sure the calculation timing was right.
What helped us most was starting small. We didn’t try to model our entire finance operation. We picked one process, got the ROI calculation working, presented it to our finance team for feedback, then scaled from there. Each iteration was faster because the base structure was already solid.
We ran into a different problem. The initial description worked great, but six months later when our invoice volumes changed, the numbers were way off. We had to figure out how to keep the calculator current without rebuilding it.
The fix was making the data connections dynamic instead of static. Instead of hardcoding assumptions, we pull actual performance metrics monthly. Now when we present ROI projections, they’re based on real data, not guesses from six months ago. That actually made our CFO trust the numbers more.
The plain English approach works really well for the initial setup, but I’d be honest about the limitations. We tried this and got a working calculator, but the real value came after we had actual performance data. In the first few months, we were calculating ROI based on assumptions. Once we had real numbers from running the automation for a while, we could validate whether our projections matched reality. That’s when the calculation became credible enough for serious investment decisions. The speed of getting something working is great, but don’t mistake that for accuracy. You still need to validate your assumptions against real performance.
We did something similar but faced the challenge of keeping stakeholders aligned during the customization phase. The plain text description was clear enough technically, but different departments had different expectations about what “time savings” actually meant. Finance wanted labor hour reductions. Operations wanted throughput improvements. They’re not the same thing. We had to spend time reconciling those definitions before the calculator made sense to everyone.
Plain text descriptions are genuinely useful for avoiding miscommunication during development, but the real question you should ask yourself is whether the tool you’re using can actually handle dynamic updates. We built a pretty good ROI calculator this way, but when business rules changed three months later, we discovered that our setup wasn’t flexible enough to accommodate the updates without significant rework. If you’re going to invest time in this, make sure the underlying tool lets you adjust calculations and data sources without starting from scratch every time something changes.
The approach is solid for proof of concept, but there’s a real difference between a calculator that works and one that’s accurate enough for business decisions. We built ours in about two weeks, which felt fast. But then we spent another month validating the assumptions because we realized we were missing some indirect costs. If you’re presenting this to leadership, make sure you’ve actually tested it against your historical data. Don’t just assume the logic is correct.
Plain text descriptions def speed up the initial build. But customization always takes longer than expected. Probly 30% longer than estimates in our case.
We did exactly this with Latenode’s AI Copilot, and the difference was significant. Instead of spending time writing technical specs, we just described what we needed: “Build a calculator that compares manual invoice processing costs against automated processing, including labor, software, and error correction.”
The Copilot generated a complete workflow in about an hour. We connected it to our accounting data, ran it against three months of historical invoices, and had numbers we could actually present to finance. No developer required. The workflow pulled costs from our ERP, calculated time savings based on actual processing speeds, and even flagged the edge cases we usually mess up manually.
What changed for us was that we went from “we think automation will save money” to “here’s exactly how much it saved last quarter.” Finance went from skeptical to approving new automation projects within weeks.
The key difference from other approaches we tried was that the Copilot kept the logic transparent and flexible. When our finance team asked “what if we add international processing,” we could update the workflow without rebuilding it. That’s what made it actually useful long term.