I’ve read the marketing around AI copilots generating workflows from plain English descriptions, but I’m skeptical about how much rebuilding actually happens after the initial generation.
We have a fairly complex workflow running on Zapier right now: pull data from a CRM, transform it based on specific rules, aggregate results, then send via email with conditional logic. It works but maintaining it is a pain. I’m wondering if there’s a realistic path to moving this without essentially rebuilding it from scratch anyway.
The question I keep coming back to is: if I describe what this workflow does to an AI copilot and it spits out a Latenode automation, how much of that is actually usable versus how much ends up being scaffolding that I have to customize anyway? I want to understand the real time savings before I commit to evaluating a migration.
Has anyone actually used this kind of AI-assisted generation to migrate a non-trivial workflow? What did the quality and completeness look like?
I tested this with a moderately complex workflow—about 8 steps with conditional branching. Gave the copilot a description of what it did and what needed to happen. The generated workflow got maybe 60% of the logic right on the first pass. The structure was solid, the integrations were correct, but the data transformation rules and some of the conditional branches needed tweaking.
The time investment was frontloaded in writing a clear description. Once I did that, iteration was faster than building from zero because I had a working baseline to modify. Edit took maybe 2-3 hours where building from scratch would’ve been 5-6. The improvement compounds if you’re migrating multiple workflows because you get better at writing effective copilot prompts.
From what I’ve seen, the copilot is strongest when your workflow is linear and your rules are straightforward. It struggles with highly customized logic or unusual data structures. We had a workflow that looked simple on the surface but did some weird transformations on arrays. The copilot generated something that didn’t handle that correctly. I had to write custom code for that piece anyway, which I might’ve done regardless.
The key variable is complexity. Simple workflows—fetch data, send notification, log result—the copilot nails that in working form immediately. Workflows with heavy custom logic or unusual API patterns need human intervention. Where I’ve seen real value is not in the initial generation being perfect, but in having a structured starting point that’s faster to modify than beginning blank. Document your current workflow thoroughly before you hand it to the copilot. That documentation becomes your copilot prompt, and the quality of that translates directly to the quality of generated output.