I tested this workflow generation feature specifically, and the experience was honestly better than I expected. The AI took my description of “consolidate data from five sources and flag anomalies” and produced something that was about 75% there. The remaining 25% was edge cases and our internal validation rules.
Where it shined: it got the schema mapping right, understood the data relationships, and built the transformation logic correctly. What it missed: our specific error thresholds and how we wanted to handle incomplete datasets.
But here’s the thing—the time to prototype dropped from maybe four hours to thirty minutes. That’s not nothing when you’re trying to validate assumptions with stakeholders. And the generated workflow actually stayed deployed in production after tweaking; we didn’t throw it away as some throwaway prototype.
The ROI holds up because the biggest cost isn’t build time, it’s iteration time. Getting a functional draft in front of people fast means fewer rounds of “can you change this?” before deployment.
Check it out at https://latenode.com