I’ve been reading about AI Copilot features on automation platforms, and the pitch sounds almost too good to be true. Describe your workflow in plain English, and the system generates it for you. No diagram clicking, no flow logic to build.
We’re evaluating moving from n8n self-hosted to something with better AI support, partly because our team keeps getting blocked waiting for engineering to build custom workflows. Our business users keep submitting automation requests that take weeks to build out because they have to go back and forth with engineers on the detailed spec.
The promise of describing something like “when we get a new customer lead, extract the data from the form, validate it against our CRM, send a personalized email, and flag it for follow-up in Slack” and having that actually work seems like it could cut our build time significantly.
But I’m skeptical. The examples I’ve seen in marketing materials are always simple things. What I want to know is what actually breaks when you move from generated workflow to production. Do you need engineering to rewrite most of it? How much of what the AI generates is actual working code versus scaffolding you still have to complete?
We’re trying to figure out if this is a real time-saver or if we’re just moving the work around instead of actually eliminating it.
Has anyone here actually used AI workflow generation at scale? What percentage of generated workflows shipped to production without substantial rework?