Enterprise customers rarely announce when they are becoming a churn risk. Warning signals are often scattered across product usage, customer sentiment, support interactions, complaints, and operational data. In this session, I will share lessons from building an AI-assisted account-health and churn-risk system at Molecule, a cloud-native energy trading platform supporting approximately $100 billion in commodity value daily. I will discuss how to combine heterogeneous signals, identify meaningful patterns, and translate predictive insights into actionable recommendations for account teams. The session will also explore how AI can augment human judgment rather than replace it, and how to evaluate these systems based on measurable business outcomes. Attendees will leave with a practical framework for applying AI to complex enterprise workflows and turning fragmented data into earlier, better-informed decisions.