Many companies silo data science and machine learning teams who optimize AI metrics like precision, F1 and accuracy away from teams focused on optimizing KPIs such as revenue and churn. This gap can lead to models being put into production that have no obvious business goal. It is imperative that business leaders understand how to close this gap and have machine learning engineers working with non-engineering teams in order to build models that fit into the business' KPI goals.