The financial services industry continues to struggle with data quality, despite significant investments in data spending. The traditional "shift left" approach of ensuring data quality at the point of entry is insufficient, as most issues arise during integration and at the "egress point," where information flows to decision-makers. AI-powered generative models (GenAI) can detect nuanced patterns that traditional validation rules overlook, such as gradual drift in values or unusual combinations of valid values. These insights expose a fundamental limitation in traditional data validation: the inability to capture complex, cross-system interactions. By leveraging GenAI and modern data composition tools, financial institutions can transform anomaly detection into a strategic business capability that validates their narrative across all data domains. The key lies in building adaptive systems where AI-powered anomaly detection serves as a continuous feedback mechanism, identifying issues such as semantic inconsistencies, temporal anomalies, and business rule violations. As these techniques mature, artificial intelligence emerges as the critical technology to unlock this potential, enabling intelligent stakeholder engagement through conversational AI interfaces that turn data quality from a technical challenge into a collaborative, intuitive dialogue.