I’ve been hearing about this AI copilot feature that supposedly lets you describe what you want in plain English and it generates a workflow. Sounds too good to be true, but I’m curious if anyone’s actually used it for evaluation purposes.
Here’s our situation: we’re evaluating Make vs Zapier, and normally the evaluation process is painful. You have to build a test workflow in both platforms, which takes days per platform, and then you can’t really compare because they’re built differently. But if we could describe a workflow in plain text and get something executable in minutes, that could actually change how we evaluate.
The question is whether the generated workflows are production-quality or if they’re just scaffolding that requires heavy customization anyway. If it’s the latter, you haven’t really saved time—you’ve just shifted the work downstream to engineering.
Has anyone tested this approach for platform evaluation? Does the plain language workflow generation actually produce something you can use, or is it more of a starting point that requires serious rework?
I tested this for evaluation and it was genuinely useful, but not in the way I expected.
The plain language generation was decent at capturing the high-level flow, but it definitely wasn’t production-ready. Where it saved time was in the conversation loop. Instead of building from scratch in the UI, I could describe what I needed, see what it generated, then iterate. That iteration was faster than traditional UI-based building because the AI understood context across the whole workflow.
For platform evaluation specifically, it was great because I could test the same scenario in both platforms quickly. Same description, both platforms’ generators would try to build it, and I could see where each platform struggled. That comparison was actually meaningful because both were starting from the same specification.
The catch is that the generated workflows still needed tweaking. Error handling, edge cases, performance optimization—all that still required manual work. But the time to functional first version dropped from days to hours.
I’d be careful about using this for make vs zapier evaluation, though. The quality of the generated workflow depends heavily on how well the platform’s integration library matches what you’re trying to do. If you’re describing a workflow that needs Make’s specific connectors, Zapier’s generator might produce something that technically works but isn’t idiomatic to Zapier’s design patterns.
For evaluation, the real value isn’t the plain language generation itself—it’s forcing yourself to articulate requirements clearly. That clarity is useful regardless of platform. But don’t assume that because a generator produced a workflow quickly, both platforms are equally capable of running it efficiently.
We used plain language workflow generation as part of our evaluation and it compressed our testing timeline significantly. Instead of spending two weeks building test workflows, we could describe five different business processes in plain language, generate them in both platforms, and have a working comparison in three days.
The generated workflows were about 70% functional out of the box. Error handling and edge case management still required engineering time. But the important part was that it let us test both platforms with the same specification, which gave us actual apples-to-apples comparison data. Sometimes the generator would fail on a specific requirement in one platform but not the other—that told us about platform limitations without needing a full custom build.
For evaluation purposes, this approach is solid. The time saved is real. The caveat is that you still need engineering resources to validate and refine, so don’t expect to hand it off to business users entirely.
Plain language workflow generation works well for prototyping and evaluation. The real test is whether the generated workflows align with your team’s operational standards and whether they scale. Use it to compress the evaluation timeline, but validate the output rigorously before considering it production-ready.
We use the AI copilot workflow generation for this exact purpose and it honestly changes the evaluation game. We describe a workflow—say, “extract data from our CRM, enrich it with AI analysis, then send summaries via email”—and get a runnable workflow in minutes.
The difference is that the Latenode generator understands the full breadth of available models and integrations, so it’s not just scaffolding. It actually produces workflows that execute. We tested it against Make and Zapier for the same scenarios and the time-to-test-result was dramatically different. Make and Zapier’s approach requires more manual refinement because they’re built around predefined connectors, not AI-backed generation.
What really shifted our evaluation was the iteration speed. Describe the workflow, generate, run it, see what breaks, describe the fix, regenerate. That feedback loop is tight enough that we could evaluate both platforms in days instead of weeks.
For your Make vs Zapier decision specifically, this should be part of your evaluation methodology. The platform that can turn plain language descriptions into executable workflows fastest gives you real advantages in agility, not just evaluation speed.