Data governance faces significant challenges as organizations increasingly integrate automation and AI into their operations. Winfried Etzel argues that traditional views of data governance, which rely on the idea that data has inherent meaning, are outdated. In today's fast-paced, distributed environments, this assumption doesn't hold. Instead, data's value emerges from its context and purpose. As organizations shift from compliance-focused governance to one that enables value creation, they must rethink their approaches to ensure accountability and relevance. Etzel highlights the limitations of both Old School and New School governance methods. These approaches often treat data as if it has a fixed essence, which leads to practices like enforcing rigid definitions and standards. While these methods may work in centralized systems, they fail in distributed contexts where data is used across various domains. Here, meaning must be negotiated continuously. The transition from essence-based to existence-based governance reflects this need. Data governance should not focus on controlling data but rather on structuring socio-technical systems to sustain meaning and accountability over time. The rise of AI complicates these dynamics further. Generative AI systems operate on statistical patterns rather than understanding intent, which can lead to outputs that are misaligned or unfair. Etzel points out that many AI failures—such as bias and hallucinations—stem from inadequate data governance that fails to negotiate context or maintain accountability. As decision cycles speed up due to AI, traditional governance models may become ineffective, highlighting the need for a more adaptive and intentional approach to data governance that can keep pace with technological advancements.