This fact sheet presents a practical framework for establishing and scaling AI governance. It outlines a continuous lifecycle built around four stages: defining AI use cases, identifying and understanding data, documenting models and results, and verifying and monitoring performance. The document emphasizes that AI governance is an ongoing discipline requiring visibility into data, models, lineage, quality, and compliance. By aligning governance capabilities with each stage of the AI lifecycle, organizations can reduce risk, improve transparency, and ensure AI initiatives deliver trusted, compliant, and measurable business outcomes.

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