We look at some real-world examples where AI governance fell short, highlighting why robust governance frameworks are essential. We discuss three cases that showcase the consequences of poor accountability, transparency, and oversight in AI systems: 1. Air Canada Chatbot Case (2024): A man relied on misinformation from an AI chatbot about bereavement discounts, leading to a lawsuit that underscored the importance of accountability in AI-driven customer interactions. 2. Apple Card Bias Controversy (2019): Allegations of gender bias in credit limit decisions revealed challenges in explainability and transparency, despite no evidence of intentional discrimination. 3. COMPAS Algorithm in Criminal Justice: A ProPublica investigation into the proprietary COMPAS algorithm raised concerns about bias in predicting recidivism rates, highlighting the critical need for transparency in high-stakes AI applications. We connect these cases to governance principles such as accountability, transparency, and fairness, emphasizing their role in maintaining trust, balancing value and risk, and upholding societal ethics.