AI governance basics for working professionals

• Explain why an organization has AI rules by connecting them to three sources: frameworks like NIST's AI RMF that require transparent policies based on organizational risk priorities[^5]; regulations like the EU AI Act that sort AI systems by risk level and assign obligations accordingly[^1][^3]; and management-system standards like ISO/IEC 42001 that sit above individual tools as a procurement trust signal.
• Classify an AI system as high-risk or not under the EU AI Act by checking whether it falls into categories like medical diagnosis, hiring decisions, or credit scoring, and explain how the Act treats "trustworthy" as shorthand for "acceptable risk" rather than measuring whether people actually trust the system[^1][^3].
• Locate the organizational gap by asking the three readiness questions, which AI systems are in use, who is accountable when one is wrong, and whether you can show you are compliant, and recognize that workforce AI literacy under EU AI Act Article 4 is itself a governance control your organization is on the hook for[^2].
• Create an inventory-and-accountability checklist for one AI system that integrates governance framework requirements[^5][^6] and the EU AI Act's obligations for high-risk systems[^3], showing how rules shape an organization's AI policies in practice.

Key concepts · tap to reveal

• “governance only matters if you build models from scratch, so a third-party API or a vendor's pre-trained model is the vendor's problem”

  the EU AI Act defines