Our team has been running Make for about two years now, and we’ve built up maybe 30-40 solid workflows that handle most of our business automation. Now we’re looking at consolidating everything because our licensing costs have gotten ridiculous—we’ve got workflows spread across multiple API subscriptions and Make’s per-operation pricing is just adding up.
The migration itself is what’s scaring me though. Two years of work in Make means we’ve got institutional knowledge baked into these flows. Some of them have conditional logic that took weeks to get right. The idea of rebuilding all of this by hand on another platform feels like it would take months.
I keep hearing about AI copilots that can take a plain language description and generate a ready-to-run workflow. That sounds too good to be true, but if it actually works, it could completely change how we approach this migration.
Has anyone actually used this kind of feature to move workflows between platforms? I’m not asking if it’s perfect—I know nothing automates 100%. But does it actually save meaningful time compared to building from scratch? Or are you just moving the work around instead of actually reducing it?
And more importantly, what’s the quality like? Are the generated workflows production-ready, or do you end up spending half the time debugging them anyway?
I tested AI workflow generation during a proof of concept we ran a few months back, and honestly it’s better than I expected, but with caveats.
We took three of our existing Zapier workflows and described them in plain English to the generator. The simplest one—basically “check email, extract data, add to spreadsheet”—came out production-ready with almost no tweaks. Just gave it the natural language description and it built the whole thing correctly.
The more complex ones needed adjustments though. One workflow had eight conditional branches and a custom retry logic. The AI got the basic structure right, but it missed some of the conditional branches and created the retry slightly differently than what we needed. Still, it got us maybe 70% of the way there instead of starting from nothing.
Here’s the real time savings: setup and testing. Even on the workflows that needed tweaking, we cut the build time from “several hours” down to “maybe an hour of adjustments and testing.” For three workflows that came out close to perfect, we saved probably six hours each.
Scaled across 40 workflows, if most of them are standard business logic, you’re looking at genuine time savings. Maybe not complete automation, but definitely not just moving the problem around.
The key is quality of your description. If you’re vague, the output is vague. Be specific about edge cases and conditions, and the generator handles it better. We had to re-describe a couple workflows to get better results.
The debugging part was the question for us too. We were worried the same thing—generate something and spend all our time fixing it.
Turned out the opposite was more true. When I built workflows manually, I’d make mistakes that took hours to debug because I couldn’t see the obvious problems. When the AI generated something that was maybe 80% right, the remaining issues were usually clearer to spot because they stuck out against what the AI did correctly.
So you do end up spending some debugging time, but it’s different than starting totally blank. It’s more like “fix this one thing” instead of “did I wire this whole flow correctly?”
For your 30-40 workflows, I’d suggest doing a sample. Pick five that represent different complexity levels—simple ones, ones with branching, maybe one with error handling. Generate those, see how much time you actually spend fixing them. That’ll give you a realistic estimate of your full 40.
The technology is genuinely useful for migration scenarios because it handles the repetitive structure pieces. Most business workflows follow patterns—triggers, transformations, actions, notifications. The AI is good at recognizing those patterns from descriptions.
Where it struggles is when you have deeply custom logic or workflows that depend on specific tool quirks. If your Make workflows are using some Make-specific feature that isn’t standard automation logic, the generator won’t know those patterns exist.
For your situation, I’d guess maybe 60-70% of your 40 workflows are standard enough that generation would handle them well. For the remaining 30-40%, you could probably get a solid foundation and build from there. So instead of rebuilding 40 from scratch, you’re really building maybe 12-15 from scratch and refining the others.
Testing multiple workflows with the copilot revealed an important pattern. Workflows under five steps generated almost perfectly. Workflows with ten or more steps needed more refinement. This tracks with typical AI performance on sequential logic problems. Accuracy degrades with complexity.
However, the human time to fix a partially generated workflow was consistently 60-70% faster than building the same workflow manually. The generation gives you a template. Humans finish it. That’s faster than humans building the whole thing.
Simple workflows: generated cleanly. Complex ones: needed tweaks. Still saved 50-60% time vs building from scratch. Migration viability depends on your workflow complexity distribution.
AI generation excels at standard patterns. It struggles with custom logic. For migrations, use it to accelerate simple flows, handle complex ones manually.
I’ve watched several teams do exactly this kind of migration, and the AI copilot piece actually became their secret weapon.
Here’s what happened in practice. One team had 35 Zapier workflows they wanted to move. They started by describing ten of them in plain language to the platform’s generator. Seven generated almost perfectly and needed maybe 15 minutes of tweaking each. Two needed maybe 30% rebuilding. One was too custom to use the generation at all.
That one successful experience changed how they approached the full 35. Instead of manually rebuilding everything, they used the generator as a foundation for 30 of the workflows. The remaining five were truly custom, but those were special cases anyway.
By doing it this way, their whole migration went from a projected three-week engineering effort down to about five days of actual work. The time savings were genuine because the AI handled the repetitive structural work while the engineers focused on validation and the few genuinely custom flows.
For your 40 Make workflows, describe your most typical one and test the generator. See how close it gets. That’ll tell you whether this approach actually works for your specific workflows. Most teams find that 60-70% of their standard business automations generate in a production-ready or near-production state.