I’ve been experimenting with the AI Copilot Workflow Generation feature, and I’m genuinely curious about real-world success rates here. The idea of describing what I want in plain English and having it generate a ready-to-run automation sounds amazing, but I’m wondering if anyone’s actually experienced this working reliably without needing to go back in and fix things.
My concern is: when you describe something like “log into this site, navigate to the reports section, extract all user data from the table, and save it as CSV,” does the generated workflow usually handle all those steps correctly? Or does it typically miss edge cases, get confused by the navigation flow, or generate code that needs debugging?
I’m specifically interested in browser automation tasks since those tend to have a lot of variability depending on how a site is structured. Has anyone here actually built something meaningful using plain English descriptions, or do you find yourself having to write code anyway after the initial generation?
I’ve used this feature on several data extraction projects, and the success rate really depends on how specific you are in your description. When I’m detailed about what I need—like mentioning specific buttons, form fields, and the exact flow—the generated workflow usually handles it on the first pass.
The AI gets confused when descriptions are vague. So instead of saying “get the data,” I describe: “click the blue ‘Load More’ button three times, wait for the table to render, then extract the email and status columns.”
With that level of detail, I’ve had workflows that work immediately. Without it, yeah, you’re tweaking things. The platform learns from your corrections too, so subsequent automations get smarter.
Worth testing yourself. Start simple—maybe a login flow or basic data grab—and see how it performs. You might be surprised.
I’ve had mixed results with this. When the description is really specific about selectors and sequences, it tends to work well. The problem I ran into was with dynamic content. If a page loads data asynchronously, the AI might generate a workflow that tries to extract before the content actually renders.
What helped was building smaller, focused workflows instead of trying to describe one massive multi-step process. I’d break it into: authenticate, navigate, wait, extract. Each step isolated. Then the AI’s output was much more reliable.
Also, I found that including visual examples or screenshots in your description helps significantly, if the platform supports that.
I’ve been using plain English descriptions for about three months now, and honestly, the first-try success rate is maybe 60-70% for straightforward tasks. The AI handles login flows and basic navigation pretty well, but it struggles with unusual page structures or when there’s JavaScript rendering happening after page load. The learning curve is shorter than I expected though—once you understand what details matter in your descriptions, the success rate climbs significantly. I’d recommend starting with simpler automations to get a feel for it before tackling complex multi-step workflows.
The AI Copilot generates usable code most of the time, but the quality varies based on how well you can articulate the task. In my experience, straightforward scraping tasks have a higher success rate than complex interactions. The key is being precise about selectors, wait conditions, and the exact sequence of actions. When I’ve had to refine workflows, it’s usually because I wasn’t specific enough initially, not because the AI misunderstood my intent.
been testing this for a few weeks. simple tasks work great, but complex flows need tweaking. the better your description, the beter the output. precision matters alot