We’re evaluating a shift from our current workflow platform to something simpler. The vendor comparison looks solid on paper—their pricing is lower, they claim faster deployment, all the usual promises. But I keep getting stuck on a question nobody seems to answer directly: what’s the actual cost of the migration itself?
I mean the real costs. Not just license differences. I’m talking about: developer time to understand the new platform, time to rewrite existing workflows, time to test, the period where productivity dips while everyone figures out the paradigm shift, the mistakes that happen when people are learning on production systems.
We’ve got about 40 active workflows running right now. Most are business-critical. Some are old and probably could be simplified during migration, but that requires architectural thinking that takes time. Some are pretty straightforward and should move easily. I genuinely don’t know how to estimate which is which until someone digs in.
The learning curve piece is what I’m least confident about. The new platform advertises “no-code” and “intuitive.” Every platform does. But our team isn’t new to automation. They’ve got habits and mental models built around our current setup. Those don’t transfer automatically, even with a simpler tool.
I’ve seen migrations where companies picked a new platform, migrated everything, and then spent six months wishing they’d gone back because the learning curve cost more than they saved on licensing. I also know people who did it cleanly and recouped the investment in months.
I’m trying to build an honest timeline and cost estimate. What do I actually need to account for? Has anyone gone through a migration to a new automation platform and tracked what the hidden costs actually were?
We did a migration from one platform to another a couple years ago and tracked it relatively well. Here’s what the hidden costs actually looked like:
Developer time: 40% higher than our initial estimate. Not because the new platform was hard, but because workflows that seemed straightforward turned out to have undocumented logic we’d built over time. Someone had to reverse-engineer that, learn the new system, and rebuild it well enough to pass testing.
Testing: massive. We did dry runs on non-production workflows first, then gradually moved critical systems over. That overlap period where we ran both platforms in parallel cost us two months of extra infrastructure bills. We could’ve skipped it but the risk wasn’t worth it.
Training was maybe 20% of a person’s time for three months for the core team. Not because the platform is hard—because people needed to unlearn old patterns. Our old platform did things a certain way and we’d all internalized that. The new platform was simpler but different.
So on top of the licensing savings, you need to budget probably 600-800 developer hours for 40 workflows. We found the simplest 10 migrated beautifully. The complex ones needed more thinking. Budget generously for the unknowns—every workflow had something we didn’t expect.
The payoff: we actually did save money by month 10. But months 1-4 cost more than we anticipated.
One thing nobody told us: the productivity dip during migration is real and it’s business-affecting. During the transition, teams couldn’t move as fast because they were learning and testing instead of building new automations. That sounds minor but it cost us three quarters of feature velocity across the organization.
Otherwise, our experience was similar. We allocated maybe 200 hours optimistically and needed 320. The new platform was genuinely simpler, but simplicity costs you if you’ve already learned to work within complexity. It’s like moving to a different keyboard layout that’s objectively better—that doesn’t make the learning period free.
We factored in the learning curve by treating it as a project rather than a gradual transition. We blocked off a focused sprint where our entire automation team went through structured training and built 10 test workflows together. Expensive in the short term—all that paid time not moving critical work—but it compressed the learning curve and built shared competency faster. By the time we started actual migration, everyone understood where the gotchas were. Probably saved us because we avoided mistakes later that would’ve been expensive.