I’ve been exploring ways to streamline how we justify automation spending to our finance team, and I keep running into the same wall: the gap between what we think a workflow will save us and what actually happens in production.
We tried building ROI models the traditional way—spreadsheets, assumptions about time savings, cost per employee hour. But when you’re describing a workflow in plain English and then having an AI generate the actual automation, there’s this weird moment where you realize you don’t know how much of the value came from the tool versus the approach.
The specific problem I’m wrestling with: when you go from “describe what you want automated” to “here’s a ready-to-run workflow,” how do you isolate what’s actually driving the ROI? Is it the speed of building it? The fact that a non-developer could build it? The workflow itself?
I’m curious if anyone’s actually tracked this. Have you found a way to measure whether the plain-text generation piece is genuinely saving you money, or is that just a nice-to-have on top of whatever performance gains you’d get anyway?
We ran into this exact thing a few months back. The trick is separating the time-to-build from the time-to-value of the workflow itself.
What actually helped us was tracking two metrics instead of one. First, we measured how long it took to go from “here’s what I want automated” to “this thing is running.” Second, we measured the performance of the workflow after it was live—hours saved, errors reduced, whatever actually matters to your business.
When we used plain text generation, the first metric dropped significantly. We went from weeks of back-and-forth with developers to days. But the second metric? That didn’t change much. The workflow still saved the same amount of time regardless of how we built it.
So the ROI from AI generation isn’t in the workflow’s performance—it’s in how fast you can get ideas into production. That’s actually worth a lot if you’re running experiments or need to iterate quickly. We factored that as a separate line item: cost of developer time saved by not having to hand-code everything.
Might sound like a small thing, but when you’re trying to justify why non-technical people should be building automations, that number becomes pretty important.
The other thing we found is that plain text generation forces you to think about the workflow differently. When you’re describing it to a human, you get clearer about what you actually want. That clarity alone tends to improve the end result.
We started using it as a discovery tool before any development happened. Marketing would describe a process, the AI would generate something, and half the time we’d look at it and go “oh, we’re doing this wrong anyway.” So we’d fix the process first, then build it.
The ROI there isn’t in the automation—it’s in the process improvement that happened because we had to articulate what we were doing. That’s harder to measure than time saved, but it’s real money.
I’d add one more angle: track the revision cycle. When we use the traditional build approach, we go through multiple rounds of changes. Developer builds something, we test it, we ask for tweaks, couple more rounds. With AI generation from text, we’re often closer to what we need on first try, or at least the revisions are faster.
We started measuring “iterations until production ready.” It dropped from an average of 4-5 rounds to about 2. Each round used to be a few days of back-and-forth. That’s real time savings that doesn’t show up if you’re only measuring the final workflow’s performance. The value isn’t just in the tool—it’s in how much faster the feedback loop actually is.
The measurement challenge you’re describing is common when adopting new tools. The real ROI from AI-generated workflows often comes from two places that are easy to overlook. First is the democratization factor—non-technical people building automations means you’re not bottlenecked on developer capacity. Second is the experimentation velocity. You can test workflow variations much faster.
What we’ve found works is building a model that captures both the direct workflow savings and the organizational capacity gains. Direct savings is straightforward: measure cycle time before and after automation. Capacity gains require tracking how many automations your team can now deploy per quarter with the same headcount.
Measure build time separately from workflow performance. we saw 60% faster deployment but same operational savings. The ROI is mainly in development velocity, not the automation itself rite?
We went through this exact problem and it was frustrating until we realized we were measuring the wrong things. Here’s what changed for us:
When we started using Latenode to generate workflows from descriptions our team wrote, we tracked it differently. Instead of trying to measure whether the AI-generated version was somehow better than a hand-coded one, we measured the whole cycle—time from idea to running automation, plus how many people could now build automations without waiting on developers.
The plain text generation cut our deployment time from 2-3 weeks down to 2-3 days. That’s not because the workflows are better—they run the same. It’s because we can articulate what we want, get something working immediately, and iterate from there instead of going through endless requirements meetings.
We also started tracking how many automations we could run per quarter. Before, we had a backlog because developers were bottlenecked. Now we deploy roughly 3x more because people across the company can build their own workflows. The ROI model shifted from “how much does this one automation save us?” to “what’s the value of removing the developer bottleneck?”
For us, that’s been the real number. Not glamorous, but it’s how we actually justify the platform investment to finance.