I’ve been thinking about this ROI question because we were comparing Make and Zapier for some enterprise automation work, and one of the features that kept coming up was the ability to just describe what you want in plain text and have it generate a workflow.
Sounds great in theory. But I wanted to actually measure what time gets saved, because in my experience those kinds of shortcuts often just move the problem downstream. You save 30 minutes building, but then spend 2 hours fixing what didn’t generate correctly.
We tested it on a few workflows. Some were straightforward—take data from a spreadsheet, clean it up, send it somewhere. Those actually worked out of the box maybe 60-70% of the time. The time saved was real. But the more complex ones, especially anything with conditional logic or multiple AI models involved, needed significant rework.
What surprised me was that the actual time savings didn’t come from the generation part—it came from having a working starting point instead of a blank canvas. We could iterate faster because we had something to work with, even if it needed tweaks.
Has anyone else actually tracked this metric? Like, did you measure generation time plus rework time versus building from scratch? I’m trying to figure out if this moves the needle enough to actually change the platform decision.
We tracked it. Generated workflow took 15 minutes to create plus 45 minutes of testing and fixes for a moderately complex one. Building from scratch on the same workflow took about 90 minutes but needed less testing. So the time delta was about 30 minutes saved, but only if you count the generated version as ‘done’ after fixes.
The real win came on the second and third workflows of similar type. Once we had a template, replicating it was way faster. The generation actually helped us understand the pattern better, so we could iterate faster on variations.
For our ROI math, we counted about 20% time savings on average, factoring in the rework.
We did something similar but from a different angle. We measured developer time and also business stakeholder time. When we could dump a plain language description into the system and get back something 70% complete, the non-technical stakeholders felt like they had more agency. They could review it, suggest changes, and iterate without feeling like they needed a developer for every tweak. That reduced escalation cycles. The actual generation time was secondary to the reduced back-and-forth.
The time savings are real but not even close to the headline claims. Straightforward workflows save maybe 20-30% of build time when you factor in cleanup work. Complex workflows actually cost more time because you spend time fixing generated decisions that shouldn’t have been made that way in the first place. The actual value is in speed of iteration and exploration. You can test five workflow variations in the time it would take to build two from scratch. That changes how you approach platform eval.
We found the biggest time sink wasn’t building—it was explaining requirements to developers. When you could write requirements in plain language and let the system generate something, even if incomplete, that saved rounds of clarification. Developers could review and fix known issues instead of starting from scratch. Cut our requirement cycles from three rounds down to one and a half. That was the real efficiency gain for us.
generation worked for simple flows. complex stuff needed way more rework than building from scratch. maybe 20% time savings if youre lucky, after cleanup.
This is exactly what we measured with AI Copilot workflow generation. The 20% number you mentioned tracks with what we saw. But here’s the thing nobody talks about—the real time saving comes when you’re iterating on variations. You can spin up five different versions of a workflow by just tweaking the description and regenerating. Try doing that in traditional builders without losing your mind.
We also found that once the workflow is generated, because Latenode lets you customize with JavaScript if needed, you’re not stuck with whatever the AI decided. You can fix the 30% that isn’t quite right without rebuilding from scratch. With some platforms, if the generation misses your requirements, you basically start over.
For our ROI model, we didn’t count it as build time savings. We counted it as exploration time savings. The cost of testing alternative automation approaches dropped dramatically. That’s worth more than 20% time savings on a single workflow—it’s reducing the cost of evaluating multiple strategies.