Why Successful AI Projects Fail After Launch And How To Prevent It

AI projects often fail after launch due to silent degradation from real-world data drift and lack of output quality monitoring. Techniques like context compaction and specific metrics tracking help maintain performance and reduce liabilities from incorrect AI outputs.