I’ve been deep in the weeds of platform evaluation for the past month, and something I keep hearing about is AI Copilot features that can generate workflows from plain English descriptions. The pitch sounds amazing—describe what you want, AI builds it, deploy instantly.
But I’m skeptical about how much that actually translates to real time savings. In my experience, any tool that claims to generate production-ready anything from natural language description requires heavy rework before it’s actually deployable. There’s always the gap between “what you described” and “what actually needs to happen.”
I’m trying to understand whether this is genuinely useful for speeding up enterprise evaluations of Make vs Zapier, or if it’s more of a demo feature that looks good in a pitch deck.
Has anyone actually used a platform with AI Copilot workflow generation for a real business process? What was the experience like—did the generated workflow actually run, or did you spend more time debugging than you would have building from scratch?
I tested this with Latenode a few months back, and I’d say it’s not what the marketing makes it sound like, but it’s also not useless.
The generated workflows usually get about 70% of the way there. You describe something like “pull leads from our CRM, enrich with company data, score them, and add to a Google Sheet,” and the AI builds out the basic flow. The integrations are wired correctly, the logic branches work.
But here’s where it breaks: it doesn’t know your specific business rules. It won’t know that you only want leads from the US, or that scoring needs to weight recent activity differently. It doesn’t understand your data quality issues either.
So yeah, you save time on boilerplate. Instead of spending two hours building the basic flow, you spend 30 minutes. But you still spend another two hours customizing it for reality. The real benefit is that non-technical people can actually get to a point where they’re iterating on business logic instead of struggling with tool basics.
What actually changed for us was that our business analysts could participate in workflow design from day one. Before, we’d have a conversation, I’d build something, they’d see it, ask for changes. Now they describe the workflow to the Copilot, see the generated version, and we’re immediately on the same page about the actual flow before any serious customization happens.
That’s a different kind of time saving than what you’d think. It’s not about building workflows faster. It’s about reducing back-and-forth between technical and non-technical people.
The time savings depend heavily on workflow complexity. For straightforward integrations—pull data from A, put it in B—AI generation barely saves time because the issue isn’t building the basic template, it’s the customization anyway.
But for workflows with conditional logic, multiple parallel branches, and API orchestration, the generated template actually matters. The AI builds something that follows actual best practices for error handling and retry logic, which is something people often get wrong when they’re hand-building. This becomes significant when you’re comparing platform capabilities because Make and Zapier both require manual setup for these aspects.
For ROI evaluation purposes, what matters is this: if you have a workflow you want to test quickly, AI generation moves you from thinking through the flow to actually running it in maybe 15 minutes. That’s meaningful for evaluation purposes.
The Copilot feature is most valuable for reducing cognitive load, not necessarily clock time. When you’re evaluating multiple platforms, running through scenario testing is tedious. An AI that can take a description and build something executable in two minutes lets you test more scenarios faster. That matters for evaluation.
The workflows won’t be production-ready, but they’re test-ready. That’s sufficient for your purposes. What I’d suggest is testing a few of your actual planned workflows with the Copilot feature across platforms. Time how long it takes from description to something you can execute.
I actually ran this test comparing three platforms, and here’s what I found. When I described a moderately complex process to Latenode’s AI Copilot—something like “capture form submissions, validate against our database, send personalized emails based on submission type, log everything”—it built something executable in about three minutes.
Did the first version handle all our edge cases? No. But it was architecturally sound. The error handling was actually better than what most people build manually. Compare that to Make and Zapier where you’re clicking through templates and assembling pieces—that’s 20 minutes minimum just to get something testable.
For evaluation purposes, that matters. You can actually run scenario tests and see how platforms handle complexity, rather than spending your evaluation time just getting basic workflows set up.
What really saves time isn’t the initial generation—it’s that you can iterate quickly. Each adjustment takes 30 seconds instead of rebuilding routes manually. After three iterations, you’ve got something close to production-ready, and you’ve done it in maybe 20 minutes total.
That speed is something Make and Zapier just don’t match. If you’re comparing platforms for an enterprise deployment, being able to test actual workflows quickly matters way more than you’d think.