In the early days, I used to read every single support ticket and review myself. It was manageable. I felt close to users and confident about what to build next. Then we grew. Feedback started coming from everywhere ,support tickets, emails, app reviews, Slack threads, sales calls. I was still reading a lot of it, but something changed. I was busy, not clear. I knew what people were saying, but I couldn’t see the bigger picture. That’s when we decided to test an AI tool to help with feedback analysis. At first, I was skeptical. I thought it would just summarize comments. But what actually helped ; It grouped feedback by similar underlying problems (not just keywords). It showed which issues were repeating over time. It highlighted sentiment trends so we could see if frustration was increasing after a release. Instead of reading 500 comments, we could see 5–7 real themes. Then we would go back and read specific examples inside those themes to understand context. Important part: AI didn’t replace judgment. It reduced noise. We still decide what matters, but now we’re not guessing. Roadmap discussions became less about ,I saw three users asking for this, and more about This problem has shown up 84 times across tickets and reviews in the last month. other teams are using AI for this? Is it actually helping, or are you still mostly doing it manually? submitted by /u/Pure-Key-4649
Originally posted by u/Pure-Key-4649 on r/ArtificialInteligence