I’ve heard the pitch about AI copilot workflow generation—describe what you want in plain English, and the AI generates a ready-to-run workflow. Sounds amazing in theory. But I’m concerned about how well this actually works in practice.
The gap between a plain text description and actual working automation feels huge. How does the AI know which selectors to use? How does it handle edge cases that aren’t mentioned in the description? What happens when the generated workflow needs to wait for something or handle a failed condition?
I tried describing a simple task to one tool and it generated something that was close but had issues with timing and selector specificity that I had to fix manually. It made me wonder if the copilot is doing real work or just a template fill-in.
For people who’ve experimented with this, how reliable is AI-generated automation really? Are you getting mostly-working workflows that need minor tweaks, or do you need significant rework? What types of tasks does it handle well versus where does it struggle?
AI-generated workflows are reliable when the description is specific. This is key: vague descriptions get vague outputs. “Get the product price” generates something decent. “Get the price from the main product section if it’s in stock and highlight it” generates something much better.
The AI isn’t magic, but it’s genuinely useful for scaffolding. It generates a starting point that works maybe 70% of the time out of the box, then your job is tweaking the 30%.
I described a login automation with MFA, and the AI generated something that got me 90% there. I had to adjust one wait timer and add a credential handler. Versus hand-building it, which would’ve taken two hours, this took forty minutes total.
The real reliability win comes from the fact that you can regenerate the workflow from your description. If the site redesigns or you want to adjust the logic, re-describe it and regenerate. You’re not stuck with yesterday’s implementation.
I was skeptical too. Tried it on a crawler task, and the output was usable but not perfect. Probably 65% functional without edits, then another 30% with minor adjustments, then 5% that needed real work.
What surprised me was that the generated workflow understood the semantic intent. It picked reasonable selectors even when multiple elements could’ve matched. It handled async operations better than I expected.
The unreliability comes from specificity gaps. The AI can’t know your edge cases unless you describe them. If you mention them in your description, it handles them better.
Generated workflows are reliable for straightforward tasks and unreliable for edge cases. Simple description like “navigate to this URL and extract the headline” works well. Complex conditional logic or rare error states need manual intervention.
The sweet spot is using AI generation as a starting point, then validating on real pages before deploying. But that validation step is where most of the time gets spent, so the time savings aren’t always dramatic.
ai generation works 70% out of box for clear descriptions. specific details help. simple tasks reliable, complex ones need manual work. starts u off fast tho.
AI-generated workflows reliable for basic tasks, unreliable for edge cases. Useful as scaffolding, not one-shot solution. Describe specifics to improve output quality.