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Ask HN: What 'AI feature' created negative ROI in production?
Not demos, real usage. What broke first: data quality, evals, cost/latency, user trust, or support load?”
- rtbruhan00 9mo agoWe implemented an AI-powered customer support triage system that initially looked promising in testing. In production, it actually increased our support costs by ~30% because: The AI would confidently misroute 15-20% of tickets, requiring human review of ALL AI decisions and the Customers lost trust after a few bad experiences and started explicitly requesting human agents also Support agents spent more time correcting AI mistakes than they saved The breaking point was data quality - our training data was too clean compared to real customer queries. We ended up rolling back to rule-based routing with AI as an optional suggestion tool instead.
- kajolshah_bt 9mo agoThis is such a classic failure mode: even a 15–20% confident misroute is brutal because it forces “review everything,” kills trust, and increases repeats/reopens. When you rolled back, did you keep AI as suggestions only + rules-based routing? And what metric exposed it fastest for you: recontact rate, handle time, or escalation to humans?
- Yiin 9mo agodid you generate this reply with chatgpt or do you just naturally like to construct sentences like AI?
- LEDThereBeLight 8mo agoEven the OP was ChatGPT - he couldn’t even be bothered to remove the quote at the end.