I’ve been trying to figure out how to justify our automation spend to leadership, and it’s been tougher than I expected. Everyone asks the same question: “Show me the numbers.” The problem is, most of the time I’m guessing at what the actual savings will be.
We have a few processes we want to automate, and I can describe what we want them to do pretty clearly. The challenge is translating that into something measurable. Like, we want to cut manual data entry by 50%, but figuring out the actual cost per hour saved, accounting for tool costs, maintenance, and the fact that we’ll probably need tweaks down the road—that’s where it falls apart.
I’ve heard about AI copilot features that can supposedly take a business goal and generate an end-to-end workflow, which sounds great in theory. But I’m skeptical about whether the ROI numbers it spits out are actually realistic, or if they’re just optimistic estimates that don’t hold up when you actually run the thing in production.
Has anyone actually done this? Built a workflow from a plain business description and then tracked whether the projected savings matched reality? What gaps did you find between what the copilot predicted and what actually happened?
Yeah, we tried this about six months ago with a customer onboarding process. The copilot generated a workflow pretty quickly, which was honestly impressive. But the ROI math was off by about 30%.
The issue wasn’t the workflow itself—that worked fine. It was the assumptions baked into the cost model. The copilot assumed we’d eliminate three full FTEs, but what actually happened was those people shifted to handling exception cases and doing quality checks. So we saved maybe one FTE worth of time, not three.
What helped us was building the workflow first, running it in parallel with the manual process for a month, and then calculating actual time savings. Then we re-ran the ROI model with real numbers. Made a huge difference when we presented to finance.
The other thing I’d watch out for is tool maintenance cost. The copilot generates something that runs, sure. But it doesn’t account for monitoring, updating API calls when things break, or fixing edge cases that come up once you hit real data. We budgeted for none of that, and it ended up being maybe 15% of the total cost.
If you’re setting this up, I’d recommend being conservative with your savings projection. Build in buffer for the 20% of cases that don’t fit the happy path.
I worked through this with an invoice processing automation. The copilot generated the workflow in about an hour, which was fast. But when I looked at the ROI calculation, it was using standard labor costs that didn’t match our actual payroll structure. More importantly, it didn’t factor in the fact that our invoices have a lot of variance—different formats from different vendors. The workflow handled the standard cases well, maybe 70% of volume, but the outliers still needed manual review.
My advice: after the copilot generates the workflow, spend time on the cost model separately. Break down which parts of the process are actually being automated, which ones are being assisted, and which ones are still fully manual. That’s where the real ROI picture emerges.
yes, tried it. actual savings were about 2/3 of projected. workflow worked fine, but edge cases and exceptions ate the gains. measure real data first, then model ROI.
This is exactly where Latenode’s approach makes sense. The copilot generates the workflow fast, but here’s what I’ve found: instead of just relying on its initial ROI estimates, you can actually run the workflow and then adjust your cost model in real time using Latenode’s data. Since you can connect it to your CRM, payroll system, or whatever you’re using, you can build a living ROI calculator that updates as the automation actually runs.
We used this approach with a support ticket routing process. Generated the workflow, connected it to our actual ticket queue, and built a simple dashboard that tracked hours saved per day. After two weeks of real data, we had actual numbers to show leadership instead of projections. The workflow itself took maybe three hours to build and tweak.