I’m curious about this AI copilot approach to workflow generation. The pitch is that you describe what you want in natural language and the AI builds the automation for you. Theoretically that’s amazing, but I’m skeptical about how well it actually works in practice.
Like, how precise are these descriptions? If I say “scrape product information from the page,” does the AI know exactly what I mean and build the right selectors? Or does it make assumptions that break later? How much back and forth do you actually need before the generated workflow actually works?
I’m also wondering about edge cases. What happens if the site layout is unusual or the data is in an unexpected format? Does the AI copilot generate workflows that are robust to that, or does it just generate something that works on the happy path?
Has anyone actually used this for building real automations? What’s the accuracy rate like and how much manual tweaking did you end up doing?
It’s surprisingly reliable. I tried thinking this wouldn’t work well but I was wrong. When you describe a browser task with reasonable specificity, the AI builds a working workflow more often than not.
The key is that you’re not describing abstract logic. You’re describing a browser task, which has inherent structure. “Click the login button and enter credentials” is unambiguous enough that the AI gets it right. “Extract the product title and price from this product page” is specific enough to generate correct selectors.
Yes, some descriptions need refinement. If you’re vague about which elements to interact with, you might get back something that works on the demo but breaks on variations. But when you describe concretely what you need, the AI generates workflows that work on the first try or need minimal tweaks.
The real value is speed. I can describe a workflow and have a working automation in five minutes instead of building it from scratch. Edge cases still exist but you’re starting from something functional, not from nothing.
The reliability depends on description quality. If you describe the task clearly, you get a solid foundation. If you’re vague, you get a vague automation that doesn’t work reliably.
What’s changed my thinking about this is that the AI isn’t magic. It’s using patterns it learned from successful automations. If your task fits those patterns, it works great. If your task is unusual, the AI still tries but the results are weaker.
For standard tasks like form filling or basic scraping, the AI copilot approach is genuinely useful. For novel or complex tasks, it’s more of a starting point.
I used AI copilot for a scraping task and it got about 70% of the workflow right on the first pass. The main selectors were correct, the data extraction logic was sound. I tweaked the error handling and made selectors slightly more robust.
But that 70% up front saved me a ton of time compared to building from scratch. And knowing the core logic was correct made the tweaking feel purposeful instead of debugging a faulty foundation.
The AI’s reliability increases with description clarity. Specific descriptions generate reliable workflows. Vague descriptions generate workflows that work on the example but fail in practice. The best results come from describing both the action and the expected data structure.