One thing we’ve run into while working with teams on AI workflows is that getting the model to do the task isn’t usually the difficult part. We’ve seen AI used to classify requests, summarize information, and suggest next steps. The output itself looked good, but things got messy when the model wasn’t sure what to do. For example, if an AI system gets something wrong 10% of the time, someone still has to deal with those 10%. If there’s no clear way to flag, review, or reroute those cases, the team ends up with another problem to manage. We’ve started paying a lot more attention to that part when designing these workflows. The “what happens when AI gets it wrong?” question is often just as important as the AI task itself. For those running AI in production, what do you do with the cases where the model isn’t confident? submitted by /u/Growth_Natives
Originally posted by u/Growth_Natives on r/ArtificialInteligence