This has been a recurring lesson for me, especially working with teams that ship models quickly and then expect them to “hold up” on their own. The first warning signs were never accuracy drops in dashboards—they were operational signals. Teams started questioning individual predictions, business users asked for manual checks, and exceptions quietly increased. The model was still passing its offline benchmarks, but its outputs no longer matched how the business actually behaved.