From the course: Building an AI Governance Program

AI governance maturity assessment

From the course: Building an AI Governance Program

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AI governance maturity assessment

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Let's focus on evaluating the current state of your AI governance program. Think of this as a self-check to see what's in place now and where the program needs to evolve. Why start here? Because governance looks different depending on how mature your AI initiatives are. If you're just piloting, your needs differ from a full-scale production model. Having a clear map means you focus resources where they will make the most impact on your business needs. I've provided a handout in the exercise files with a list of sample questions you can include in your assessment. You'll answer each question with yes, partially, or not yet. The result will place you somewhere on an AI governance maturity spectrum. Using tiers from the NIST AI RMF framework, that can fall into four tiers, Partial, risk-informed, repeatable, adaptive. Why does this matter? If you're still ad hoc, for example, individual teams doing their own thing with no central oversight, you're exposed to risks and missed opportunities. If you're further along, you might have processes but may still lack integration across the business or it might not align with the organization's strategic vision. This course will help close those gaps by breaking down what strong AI governance looks like at each stage of maturity. You'll learn how to move from isolated or ad hoc efforts to coordinated risk-informed practices. We'll connect the concepts to real business operations and show how to strengthen oversight, accountability, and consistency. It's designed so you'll have a clear understanding of where to focus next to build a more resilient and trusted AI program. Next up, we'll look at the real cost of governance failures and why waiting isn't an option.

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