Learn by doing

Module 2

Why it matters

AI literacy isn’t just about absorbing knowledge. Of course, the rubber meets
the road when you apply it to solve real problems. That’s why in this module, we pair role-specific learning goals with a hands-on use case ideation workshop.

Whether you’re an executive, an engineer or an analyst, the fastest way to build meaningful AI literacy is to identify a business challenge in your own world and shape it into a viable AI use case that’s ethical, transparent and aligned with governance standards from day one.

Role-based paths and use case ideation

Step 1: Pick your learning path

Executives and decision-makers
Focus: Align AI with business goals, lead governance

Build toward: Understanding risks, evaluating ROI, defining and guiding AI strategy, aligning AI to business objectives

Learn by doing: Sponsor and review AI use cases, ensure alignment with strategic and ethical priorities

Technical teams (Data Science, Engineering, IT)
Focus: Model development, data quality, technical governance

Build toward: Bias detection, explainability,
model monitoring

Learn by doing: Frame the technical architecture of a use case, assess data quality, and map lineage

Governance, risk and compliance teams
Focus: Policy, ethics, regulatory compliance

Build toward: Applying EU AI Act, setting governance controls

Learn by doing: Identify high-risk use cases and map them to compliance workflows using Collibra

Business and operations teams
(HR, Marketing, Finance, etc.)
Focus: Practical literacy, interpreting AI, raising red flags

Build toward: Understanding AI impact, knowing when to ask questions

Learn by doing: Describe the challenge you want AI to solve, and collaborate with data teams to frame a viable use case

Step 2: Use case ideation workshop

Now it’s time to apply what you’re learning to your actual work. Use this template to define a responsible AI use case. Bring your team together—cross-functional collaboration is key.

1. Define the business problem

What’s the specific challenge or opportunity you want to address with AI?

2. Identify the stakeholders

Who will build the model? Who will use it?
Who will be affected by its decisions?

3. Understand the data

What data sources will the AI rely on? Are they clean, complete, and fair? Could they introduce bias?

4. Describe the decision the AI will support

Is this a low-stakes recommendation engine or a high-risk automated decision process (e.g., hiring, lending, health)? Is human oversight in place?

5. Outline the success criteria

How will you know it’s working? What metrics matter? Accuracy, fairness, business impact?

Step 3: Bring it into Collibra

Once your use case is drafted, use Collibra to begin documenting and governing it:

Record the use case and its purpose
Document the data sources and lineage
Assess the risk classification (per EU AI Act)
Assign roles and responsibilities
Enable ongoing monitoring and transparency

What you’ll walk away with

A clear, contextualized learning path
A defined use case you can develop with your team
A foundation for ethical, scalable AI governance
using Collibra