The AI Governance Playbook

One-liner: Design a practical AI governance framework for a team or project — covering when to use AI, how to verify outputs, and what requires human judgment.

🔧 Jump in (Tinkerers start here)

Pick a real team, project, or organization you work with. You’re going to design an AI usage framework they could actually adopt.

Step 1 — Map the AI touchpoints. Send this prompt:

I’m designing an AI governance framework for a [team type/project type] that does [describe the work]. Map out all the places where team members might use AI in their workflow. For each touchpoint, classify the risk level:

Low risk: AI errors are easily caught and consequences are minor (e.g., drafting internal emails, brainstorming)

Medium risk: AI errors could waste significant time or create confusion (e.g., research summaries, data analysis, first drafts of client deliverables)

High risk: AI errors could cause reputational, legal, or financial harm (e.g., published content, financial recommendations, legal language, customer-facing decisions)

Present this as a table with: Touchpoint | Description | Risk Level | Why

Step 2 — Design the verification tiers. Based on the risk map, create a tiered verification system:

Based on the risk map above, design a 3-tier verification system:

Tier 1 (Low risk): What’s the minimum verification needed? What can proceed without review? Tier 2 (Medium risk): What checks are required? Who reviews? What’s the turnaround expectation? Tier 3 (High risk): What’s the full review process? Who signs off? What documentation is needed?

For each tier, specify:

Verification steps (checklist)

Who is responsible

What “approved” looks like

What happens when issues are found

Step 3 — Write the team guidelines. Now produce the actual document:

Write a 1-page “AI Usage Guidelines” document for this team. It should be practical, not corporate. Include:

When to use AI — Green light scenarios

When to be careful — Yellow light scenarios with required verification

When NOT to use AI — Red light scenarios or scenarios requiring explicit approval

Verification standards — The tier system from above, simplified

Attribution — When and how to disclose AI usage

Escalation — What to do when you’re unsure whether AI use is appropriate

Write it in the tone of a senior colleague giving practical advice, not a legal department issuing mandates.

Step 4 — Red-team the framework. Test it:

Now role-play as a team member who wants to use AI in a gray area. Come up with 3 realistic scenarios where the guidelines are ambiguous or where a reasonable person might interpret them differently. For each scenario, suggest how to clarify the guideline.

Revise the guidelines based on the edge cases.

📋 Plan first (Planners start here)

Here’s what you’re about to do:

Choose your context — Pick a real team or project. The framework should be one you could actually share or implement.
Map AI touchpoints and risk levels — Identify every place AI could be used in the workflow and classify each by potential harm from errors.
Design tiered verification — Create different verification processes for different risk levels. Not everything needs the same scrutiny.
Write the guidelines — Produce a practical 1-page document that a team member could reference in their daily work.
Red-team with edge cases — Test the framework against ambiguous scenarios. Fix any gaps before sharing.

“Done” looks like: A complete, practical AI governance framework (risk map + tiered verification + 1-page guidelines) that you could present to your team, plus documentation of edge cases you tested against.

🧭 Why this matters (Strategists start here)

Individual verification habits (from EP-Intermediate-01) don’t scale to teams. When five people use AI differently with different standards, the team’s AI output quality is only as good as the weakest link. A governance framework creates shared standards without bureaucracy — it tells people what’s safe to do quickly and what requires care, without making every AI interaction feel like a compliance exercise. This is also the document organizations will pay for: a practical, calibrated AI usage policy that actually gets followed.

Reflection

Which risk classification was hardest to assign? What does that ambiguity tell you?
Would your team actually follow these guidelines? What would make them ignore it?
Did the red-teaming step reveal fundamental gaps, or just edge cases?
What surprised you about the output?
What did you have to fix or override?
How would you explain what you just did to a colleague?
💬 Discuss: Try explaining your result to someone who hasn’t used AI for this task. What questions do they ask? (Social Learners)

⬆️ Level up

You’ve reached the advanced level for Ethical Prompting & Judgment. From here, consider:

Presenting this framework to your actual team and iterating based on feedback
Combining this with AC-Advanced-01 to add governance to multi-agent workflows
Building a case study of how the framework changed AI usage behavior in your team

Back to Ethical Prompting & Judgment | 🔴 Advanced Level