When we analyzed 800+ Party City locations for multiple clients during their bankruptcy auction, I tracked what we called "confidence-action divergence" - when our model showed high confidence scores but customers weren't acting on those recommendations. The earliest signal wasn't in our prediction accuracy metrics, but in deal velocity dropping 40% despite strong site scores. Our model was correctly identifying demographic fits and traffic patterns, but it wasn't accounting for lease complexity factors that were actually driving customer decisions. We were telling Cavender's that sites scored 85/100, but they were passing because of hidden renewal clauses our model couldn't see. The explanations we provided focused on revenue potential while customers were actually deciding based on operational risk. The fix was immediate: we deployed Clara, our lease analysis AI, to surface the decision factors our customers were actually weighing. Within 48 hours, we rebuilt our scoring explanations to include lease terms, exit clauses, and operational complexity alongside demographic data. Deal velocity recovered to normal levels because our explanations finally matched what drove real decisions. Now I monitor "explanation-to-action lag" - if customers take longer than usual to move forward after receiving our analysis, it means our model explanations have drifted from their actual decision process, regardless of prediction accuracy.