We tried this pretty seriously. Described a customer intake workflow in detail and ran it through the AI copilot.
The initial result was maybe 60% there. Core logic was right, but it missed nuances like validation rules and error handling. We spent maybe a quarter the time rebuilding it compared to building from scratch, so net positive.
What worked: structured processes with clear steps. What didn’t: anything involving multiple conditional branches or unusual business logic.
The real value wasn’t the generated workflow itself. It was having a starting point that was already logically sound, so we could iterate on details instead of designing from scratch. Saved us a couple hours per workflow, which adds up.
Honest take: the AI stuff is maybe 50-70% there depending on how specific you are with the description. If you write a detailed requirements doc and feed it to the copilot, you get something usable. If you write “handle customer emails” with no detail, it’s basically brute force from scratch anyway.
Where it genuinely helps: as a code review tool. Show it your workflow, it suggests improvements or catches things you missed. That’s been more useful than starting from description.
AI workflow generation works best for standard processes you’ve probably already built before. A new customer onboarding flow? Likely generates 70% usable code. A weird custom process unique to your business? Maybe 30% of it is salvageable, and you rebuild the rest.
The time savings depends on your starting point. If you’re comparing to hiring someone to design workflows, yeah it’s faster. If you’re comparing to an experienced developer building from scratch, it’s closer. Still faster, but not dramatically.
AI generation is useful for scaffolding, not for complete automation. It gives you maybe 40-50% of a production workflow correctly, assuming your description is detailed. The remaining 50-60% requires domain expertise and testing.
Does it reduce TCO? Possibly. It reduces design-from-scratch time, helps less experienced people onboard faster, and can catch potential design flaws. But you’re not eliminating the need for skilled workflow engineers, just shifting what they spend time on.
AI-generated workflows are scaffolding, not finished products. Useful for standard flows (maybe 60% usable) but weak on edge cases. Worth the starting boost, not a revolution.
We tested the AI copilot on our own workflows here and it was genuinely useful, way more than I expected. Described a lead nurturing workflow in maybe two paragraphs, and the AI generated something that captured 70% of the logic we actually needed.
The gaps were obvious too—edge cases, some integration details. But instead of designing the whole thing from scratch, we were basically refining something that was already structurally sound.
Time savings? Yeah, probably 40-50% faster than starting blank. More importantly, it helped our less technical team members understand workflow design because they could see what the AI interpreted from their descriptions. That feedback loop was valuable.
Not replacing experienced engineers. But for standard processes and helping teams prototype faster? Legitimately useful. Reduced our workflow dev time enough that it did move the needle on project costs.