AI can create significant value—but also significant risk. Organizations deploying AI face:
AI operators need to understand risk management, not just AI capabilities.
| Risk | Likelihood (1-5) | Impact (1-5) | Score | Priority |
|---|---|---|---|---|
| [Risk name] | L × I | H/M/L |
Likelihood factors:
Impact factors:
Governance structure:
AI GOVERNANCE HIERARCHY
Executive Oversight
├── AI Ethics Board / Committee
│ └── Policy decisions, ethical review
│
├── AI Center of Excellence
│ └── Standards, best practices, support
│
├── Business Unit Leaders
│ └── Use case prioritization, ownership
│
└── AI Analysts / Users
└── Responsible use, issue escalation
| Policy Area | What to Define |
|---|---|
| Approved uses | What AI can be used for |
| Prohibited uses | What's off-limits |
| Data handling | What data can/can't go into AI |
| Human oversight | When human review required |
| Quality standards | Minimum acceptable quality |
| Documentation | What must be recorded |
| Incident response | How to handle AI failures |
| Principle | What It Means | How to Implement |
|---|---|---|
| Transparency | Users know they're interacting with AI | Disclosure, explainability |
| Fairness | AI doesn't discriminate | Bias testing, diverse data |
| Accountability | Someone owns outcomes | Clear responsibility chain |
| Privacy | Personal data protected | Data minimization, anonymization |
| Safety | AI doesn't cause harm | Testing, monitoring, kill switches |
| Human control | Humans can override AI | Escalation paths, overrides |
AI risks fall into several categories:
Technical risks:
Operational risks:
Legal/regulatory risks:
Reputational risks:
AI governance is the system of rules, practices, and processes that guide how AI is developed, deployed, and monitored within an organization.
Key components:
AI regulation is evolving rapidly:
Current/emerging regulations:
Compliance considerations:
| Level | Characteristics | Typical Organization |
|---|---|---|
| 1: Ad Hoc | No formal governance, individual decisions | Early AI adopters |
| 2: Emerging | Some policies, inconsistent enforcement | Growing awareness |
| 3: Defined | Clear policies, assigned responsibilities | Maturing programs |
| 4: Managed | Systematic monitoring, metrics-driven | Sophisticated users |
| 5: Optimizing | Continuous improvement, leading practice | AI-mature organizations |
When an AI system causes problems:
| Step | Actions |
|---|---|
| 1. Contain | Stop the AI system if necessary |
| 2. Assess | Understand what happened and impact |
| 3. Communicate | Notify affected parties |
| 4. Investigate | Root cause analysis |
| 5. Remediate | Fix the immediate problem |
| 6. Prevent | Address systemic issues |
| 7. Document | Record lessons learned |
Your organization is seeing rapid, uncoordinated AI adoption. Different teams are using various AI tools with no central oversight. You've been asked to propose a governance framework.
Build a basic framework:
1. Risk assessment: Identify the top 5 risks of uncontrolled AI adoption in your organization.
2. Policy recommendations: Draft 5-7 key policy statements (e.g., "All AI-generated customer communications must be reviewed by a human before sending").
3. Roles and responsibilities: Who should be responsible for:
4. Process design: How should a new AI use case be proposed, evaluated, and approved?
5. Controls: What technical or procedural controls would reduce the risks you identified?
In Module 4.4: Future-Proofing, you'll learn how to stay ahead of AI evolution—building adaptable skills and systems that remain valuable as technology advances.
You've reached the end of this module. Review the checklist below to ensure you've understood the key concepts.