Christian Gralingen discusses the challenges and strategies around governing artificial intelligence (AI) as it scales within organizations. From 2022 to 2025, in-depth interviews with senior leaders across major financial and regulatory institutions revealed that effective AI governance requires adaptive frameworks tailored to the type of AI system and associated risks. The paper identifies the necessity to shift from static compliance measures to integrating risk controls directly into operational workflows, incentivizing conclusive judgments across multidisciplinary teams, and fostering governance systems as dynamic, learning entities. Key risk areas arise both during AI development—such as data bias and model validation—and after deployment, where AI interacts with complex environments and human operators, leading to risks like model drift and propagation of errors across systems. Different types of AI require specific controls: rules-based measures suit narrow, static models, while adaptive systems demand ongoing alignment and propagation-risk management. Examples from banking and market surveillance highlight the importance of embedding governance in daily operations and ecosystem-wide cooperation. Ultimately, organizations that treat AI governance as an evolving capability, not a static checklist, will better manage risks and unlock the technology’s full potential.
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