AI governance provides the policies, processes, and oversight needed to ensure AI systems are safe, ethical, compliant, transparent, and accountable while enabling organizations to scale AI adoption with confidence. This guide argues that governance should be viewed as an enabler of innovation rather than a barrier, helping organizations mitigate risk, build trust, improve decision-making, and maintain regulatory compliance. It highlights the need for AI-specific governance beyond traditional data privacy or model risk management, emphasizing oversight of AI use cases, models, and datasets. The framework centers on three stages: establishing organization-wide governance foundations, evaluating and onboarding AI systems through an intake process, and continuously governing, monitoring, and improving deployed systems. Successful AI governance requires clear ownership, cross-functional collaboration, ongoing adaptation, and integration with evolving regulations and standards such as the EU AI Act, NIST, and ISO 42001.

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