We’re at a point where we need to seriously evaluate consolidating our automation stack. Right now we have critical workflows split across Make and Zapier, and honestly, managing both is starting to feel inefficient. The triggering question is: what does migration actually look like without completely rebuilding everything?
I get that you can export workflows, but in my experience, a workflow designed for one platform doesn’t just drop into another. The logic structure is different, the condition handling is different, and the way you connect apps varies. Plus we have some edge cases and custom logic that isn’t obvious from just reading the original workflow.
The manual approach—basically rebuilding each workflow from scratch in the new platform—would take months and pull our automation person off other projects. I’ve heard some platforms have tools that can convert or generate workflows automatically, but I’m skeptical about how well that actually works without manual cleanup.
For anyone who’s actually done this migration: how long did it really take, and how much of the original workflow could you reuse versus rebuild?
I migrated seven workflows from Zapier to a different platform last year, and honestly, it was slower than I expected. Each one took about 4-6 hours of active work, but that’s because I had to understand the original logic first, then figure out how to represent it in the new system.
The tricky part was the conditional logic. Zapier uses filters one way, and other platforms use branches or loops. So even though the workflow does the same thing, the structure looks completely different. You can’t just copy and paste.
What actually saved time was having clear documentation of what each workflow does before starting. Like, a sentence or two per workflow about the input, output, and edge cases. Without that, you’re basically reverse-engineering your own workflows.
The bright side: if a tool can actually turn a description into working workflow automatically, that would cut the time dramatically. We didn’t have that option, so it was purely manual reconstruction.
Migration is less about the technical conversion and more about understanding your workflows well enough to rebuild them. In practice, exporting and re-importing rarely works cleanly because the data structures don’t map one-to-one across platforms. What I’ve seen work is sitting down with whoever built the original workflows and documenting what they actually do—not the steps, but the business logic. Then you can build in the new platform with that clarity. The bottleneck is always the documentation and understanding phase, not the actual building.
Direct migration tools rarely preserve workflow integrity because platforms handle data transformation, error handling, and conditional routing differently. The practical approach is to treat it as a rebuild, not an import. However, automation generation from plain descriptions could dramatically reduce that timeline. If you describe a workflow in natural language and the platform generates it, you validate the logic once rather than rebuilding and testing multiple times. That’s the real efficiency gain.
Migration from Make or Zapier is tedious mostly because you’re manually rebuilding. The workflows exist, you know what they do, but you still have to reconstruct them in the new tool’s language.
Here’s what changes that: if the new platform can take your workflow description and generate a working version automatically, you’ve just cut migration time from weeks to days. Instead of manually rebuilding each step, you describe the workflow in plain English—input, logic, output—and the AI generates a ready-to-run automation.
Then you validate it once instead of building and testing from scratch. I’ve seen this cut migration time in half compared to manual reconstruction. The platform handles the transformation logic, the branching, the error handling. You’re just making sure it matches your intent.