We’ve been evaluating automation platforms and kept hitting the same wall: our finance team wanted hard numbers before committing budget. They didn’t care about “potential savings” or vendor claims. So we decided to actually test it.
We took three manual processes that were eating up maybe 15 hours a week across the team. Nothing exotic—data extraction, basic analysis, sending notifications. We used the AI Copilot feature to describe what we wanted in plain English and let it generate the workflows.
Here’s what actually happened: the generated workflows weren’t perfect out of the box, but they were close enough that we didn’t need a developer to fix them. Total setup time was maybe 6 hours of tinkering across the three processes. Once they ran for two weeks, we had real data: the automation cut execution time from 15 hours to 2 hours weekly.
That 13-hour weekly savings plugged directly into our ROI spreadsheet. Finance actually said yes to the project. What surprised me most was how quickly we could iterate. Instead of waiting weeks for a developer to build something, we tweaked the workflows ourselves the same day.
Has anyone else actually used this feature to build their business case? I’m curious if the time from plain text to running workflow was similar for you, or if our experience was an anomaly.
That’s solid validation work. We did something similar but took a different angle—we benchmarked against what our old process cost in contractor hours. The AI Copilot approach let us build the workflow in a day instead of a week of back-and-forth specs and revisions.
One thing that helped us was treating the first version as a rough draft, not the final product. We ran it for a sprint, captured the actual execution logs, and used those to show both the time savings and the error rates. Finance appreciated that it wasn’t theoretical.
The tricky part for us was that one of the three workflows needed some logic that the AI didn’t quite nail on the first try. We ended up adding a few conditional steps manually, but that was maybe another 30 minutes of work. The templates would’ve saved us that step entirely if we’d started there instead.
This is the kind of proof point that actually moves the needle internally. Most people just run the platform for a week and guesstimate savings. You went the harder route and collected real metrics.
We’re in a similar spot right now—piloting the platform with a smaller team. Question though: when you moved from the 13-hour weekly number into the finance case, what assumptions did you have to defend? We’re worried they’ll ask about maintenance overhead, process changes, or whether the time savings actually stick month-to-month.
Your 6-hour setup time is interesting. That aligns with what we’ve seen, but it really depends on how much domain knowledge the people building the workflows have. We paired the workflows with someone who understands the data and the business logic, not just the platform. That made the AI output way more usable.
One nuance: the workflows stayed stable for us over the first month, but they started needing tweaks after that as edge cases showed up. Nothing dramatic, but it’s worth factoring into the maintenance hours when you’re talking ROI with finance. Not a blocker, just a realistic adjustment to the math.