I launched my teleradiology company right before the pandemic hit, and we nearly died because we rushed to implement AI-powered tools without properly training our radiologists on the workflow integration. We bought expensive AI software that was supposed to flag critical findings, but our docs kept missing alerts because the notifications didn't match how they actually moved through cases--the AI popped up at the wrong moment in their reading sequence. The biggest mistake I see is implementing AI without mapping your *actual* workflows first. We lost two hospital contracts early on because our AI triage system would mark pediatric chest X-rays as "routine" when experienced pediatric rads knew they needed urgent attention--the AI was trained on adult data. Once we spent three months shadowing our own radiologists and customizing the AI to mirror their decision trees, our turnaround times dropped 40% and critical finding callbacks improved. Here's what saved us: we stopped asking "what can this AI do?" and started asking "what specific problem are we solving, and does this AI actually solve it the way our people work?" For teleradiology covering 24/7 pediatric emergencies across time zones, we needed AI that could handle weird pediatric anatomy variants and flag cases by actual clinical urgency, not just technical image quality. When we switched to that mindset and rebuilt our implementation around real radiologist behavior patterns, everything clicked.