Practical AI governance implementation

Module 3

Why it matters

Once you’ve identified promising AI use cases, the next step is to ensure they’re built—and scaled—responsibly. That means putting governance in place early, not retrofitting it after deployment.

A solid governance framework reduces risk, aligns stakeholders and helps meet regulatory requirements like the EU AI Act. Most importantly, it builds trust internally and externally.

This module helps you translate your use case into a governed, accountable initiative, with the right guardrails in place from day one.

Exercise: Build an AI governance framework

Use this guided worksheet to define how your organization, or your team, will oversee AI in practice.

1. Identify high-risk AI use cases

Which of your use cases could have significant legal, ethical or operational consequences?

Think about:

Use of personal or sensitive data
Decisions that affect people’s access to services (e.g., hiring, lending, healthcare)
Use cases subject to the EU AI Act’s “high-risk” classification

List at least 1–2 use cases that require close oversight.

2. Define oversight structures

Who will be responsible for governance? How will you ensure ongoing accountability?

Consider:

Forming an AI Governance Council
Appointing role-based reviewers (e.g., ethics lead, compliance officer, business owner)
Setting checkpoints at critical stages (e.g., before model deployment or after retraining)

Sketch out your proposed structure and key decision-making roles.

(continued)

Output: Governance draft plan

By completing this exercise, you’ll have the foundation of an AI governance plan for at least one use case, including:

A summary of the high-risk use case
Defined governance roles and oversight points
A simple checklist or approval process
A pathway to formalize this in Collibra

Next Step: In Module 4, we’ll look at applying ethics and risk mitigation strategies to this use case and bringing your governance plan to life.

3: Establish your policy assessments

What rules and requirements must your
AI use case meet?

Start simple. Your policy assessment might include:

Has the data been assessed for bias and quality?
Are users informed when AI is being used?
Is the model explainable to stakeholders?
Is there a defined escalation path for errors or harm?

List the core policy items that every AI project
should check off before launch.