I’ve been curious about this for a while. There’s something almost too good to be true about typing out what you want your automation to do in regular English and having the system actually build it for you. I know some platforms talk about this, but I’m skeptical about how well it actually works in practice.
Like, right now I’m trying to automate a process where we pull data from an API, do some basic transformation, and then generate a CSV report. It’s not crazy complex, but it’s also not trivial. I’m wondering if I could just describe that workflow in plain text and have an AI actually generate something usable, or if that’s more of a marketing promise than reality.
Does anyone have real experience with this? Have you actually fed a plain-English description into something and gotten a working automation out of it on the first shot, or do you end up tweaking a lot of the generated workflow?
I’ve done exactly this, and the honest answer is: it works better than you’d think, but it’s not magic.
Your use case is actually perfect for it. Data pull, transform, export. That’s a pattern AI can handle well. I’d describe it something like “fetch data from this API, keep only these fields, and write to a CSV.” Latenode’s copilot will generate a solid foundation from that.
The first version might be 80% there. You’ll tweak parameters, maybe adjust the transformation logic, add error handling. But the overall structure is right, the connections work, and you’re not building from blank.
What impressed me is that it understands the logical flow. If you mention pulling data, transforming it, and exporting, it doesn’t create a confusing graph. It builds a linear chain that makes sense.
I’d say try it. For your use case, you’re probably 30 minutes away from something production-ready instead of starting from nothing.
I’ve tested this approach several times, and for straightforward workflows like yours, it genuinely saves time. The AI understands basic patterns well—API calls, transformations, outputs. Where it struggles is with edge cases or very specific business logic.
For your CSV report workflow, I’d expect the copilot to nail the general structure. You might need to refine the transform step or adjust field mappings, but you won’t be building it from scratch. I’ve had workflows where the initial generation was 85% usable, which beats manually assembling everything.
The reality is somewhere between “wow this is amazing” and “it’s just okay.” Plain English descriptions work well when you’re describing a clear sequence of steps. Where people get disappointed is when they expect the AI to understand nuanced business requirements from a vague description.
Be specific about what fields you need, what transformations matter, where the data comes from. Give it that clarity, and the generated workflow is usually a good starting point. Expect to review and adjust, but you’re not writing the whole thing.
The hype is real, but with caveats. Plain text description gets you started fast. You’ll absolutely need to review and refine the output. The AI builds a reasonable structure, but it doesn’t know your specific API response format or your exact CSV requirements. That said, having a generated scaffold that you tweak is genuinely faster than writing everything from scratch. I’ve done both approaches, and the copilot method wins for time-to-deployment in almost every case.
AI workflow generation works well for standard patterns. Your API-to-CSV workflow is textbook territory. The generator will likely produce something close to correct on the first pass. You might adjust the transformation logic, fix field mappings, or refine error handling, but the overall structure and flow will be sound. I’ve tested this across various use cases, and it consistently saves time when the workflow is straightforward. For more unusual or custom logic, you’ll need to manually refine more. The real advantage is that you’re not staring at a blank canvas. You’re iterating on something that already works.
The capabilities are real, not oversold. I’ve seen plain English descriptions turn into production workflows with minimal tweaking. The key is being specific in your description and understanding that the AI might miss domain-specific details. For data workflows, the success rate is high. For workflows involving complex conditional logic or API-specific quirks, expect more manual refinement. Your use case should generate something very usable, though.