AI Governance in Practice & Escalation | AI Champion Training

Why Governance Matters

By 2026, AI governance isn't a “responsible AI” side-quest—it's a prerequisite for scaling. Most enterprises now report regular AI use, and many are already experimenting with AI agents, but the hard part is still moving from pilots to enterprise impact.

That's why governance has moved up the org chart. McKinsey's global survey work has found CEO oversight of AI governance is among the factors most correlated with bottom-line impact, and organizations are increasingly “elevating governance” while trying to mitigate a growing set of gen-AI risks like inaccuracy, cybersecurity, and IP infringement. At the same time, readiness remains uneven—Deloitte reported that only 25% of leaders felt “highly” or “very highly” prepared to address GenAI governance and risk issues.

Regulation also turned governance from “best practice” into “table stakes.” The EU AI Act entered into force in August 2024 and includes phased obligations—such as AI literacy requirements (February 2025), GPAI governance obligations (August 2025), and broad applicability in August 2026 (with some extended timelines).

NIST's AI Risk Management Framework puts it plainly: governance means making sure “roles and responsibilities and lines of communication” for managing AI risk are documented and clear across the organization. Effective governance isn't bureaucracy—it's how you scale AI responsibly, protect customers and the business, and keep momentum when something goes wrong.

What Governance Provides

Without Governance	With Governance
No visibility into AI deployed across the org	Central registry of all AI use cases
Inconsistent practices and risk exposure	Standardized processes and controls
Reactive compliance (after problems occur)	Proactive risk management
Unclear accountability when issues arise	Defined owners and escalation paths

Workplace Scenario: Customer Email AI

You are planning to deploy a new AI tool that analyzes incoming customer emails and recommends actions—categorizing complaints, routing urgent issues, and suggesting response templates.

Part 1: Approval Before deployment, you need to prepare an AI Use Case Intake Form for governance review. This documents what the AI does, what data it uses, and what risks exist.

Part 2: Incident After deployment, the AI miscategorizes several complaint emails as "routine inquiries," causing delayed responses to frustrated customers. One customer escalates publicly on social media. You need to handle this incident appropriately.

AI Governance Structure

Organizations commonly choose one of three governance models:

Model	Description	Best For
Centralized	Dedicated AI governance office reviews all initiatives	Regulated industries, early AI adoption
Federated	Governance embedded in business units with central coordination	Large, diverse organizations with mature AI
Hybrid	Central team sets standards; business units execute within guardrails	Most medium-to-large enterprises

Key Governance Roles

Role	Responsibility
AI Ethics Officer	Sets principles, reviews high-risk cases, handles escalations
AI Risk Manager	Assesses and monitors risk across AI portfolio
AI Product Owner	Defines business requirements and owns value delivery
Technical Lead	Ensures architecture meets standards and security requirements
AI Champion	Drives adoption, surfaces concerns, bridges business and governance

The Champion's role: You're the connector between your team and governance. You help ensure use cases get properly documented and reviewed, and you raise concerns before they become incidents.

Knowledge Check

Test your understanding with a quick quiz

Form Practice: AI Use Case Intake Form

Complete this intake form for the Customer Email AI scenario:

AI Use Case Intake Form

Fill out each section of this intake form as if you were submitting the Customer Email AI for governance review. Use the hints and examples for guidance.

Scenario: You are deploying an AI tool that analyzes incoming customer emails to categorize complaints, route urgent issues, and suggest response templates. Your customer service team of 15 agents will use this tool daily.

Use Case Description*

Data Used*

Potential Harms*

Harm Type	Specific Risk

Risk Level*

Risk Mitigations*

Risk	Mitigation

Human Oversight*

Success Metrics*

Reflection Exercise

Apply what you've learned with a written response

Decision Tree: When to Escalate

Use this flowchart to determine when escalation is required:

Step 1: Assess the Risk Level

Does the AI involve any of the following?

Personal identifiable information (PII)
Decisions affecting health, safety, or legal rights
Customer-facing interactions
Financial transactions or recommendations

If YES to any: Proceed to Step 2. If NO to all: Standard approval process may suffice.

Step 2: Check for Novel Concerns

Is there something unusual about this use case?

First time using this AI capability
No precedent or playbook exists
Cross-functional conflict about approach
Uncertainty about appropriate controls

If YES: Escalate to your governance contact for guidance.

Step 3: Evaluate Incident Severity

If an issue has occurred, how severe is it?

Severity	Criteria	Escalation Level
Low	Minor error, quickly corrected, no external impact	Team lead
Medium	Customer impact, policy question, repeated issue	Department manager + governance contact
High	Public exposure, regulatory concern, significant harm	Senior leadership + legal + communications
Critical	Safety risk, major compliance breach, media involvement	Executive team + board notification

Step 4: Document and Communicate

When escalating, include:

Clear statement of the issue and urgency level
Relevant context (what happened, when, who's affected)
Options you've considered
Your recommended path forward
Decision needed and timeline

Knowledge Check

Test your understanding with a quick quiz

Role-Play: Reporting the Incident

The Customer Email AI has miscategorized complaint emails, and a customer has posted publicly about delayed responses. Practice escalating this issue.

Scenario Details

What happened: AI categorized 12 urgent complaints as "routine inquiries" over 3 days
Impact: Average response time for these customers was 72 hours vs. target of 24 hours
Visibility: One customer posted on LinkedIn mentioning the company by name
Root cause: Unknown—could be model drift, edge case, or data issue

Your Escalation Communication

Draft a brief escalation message (3-5 sentences) that includes:

What happened
Who is affected
Severity level
Your recommended immediate action
What decision you need

Example:

"Our customer email AI has been miscategorizing urgent complaints as routine inquiries—12 cases over the past 3 days, with one customer posting publicly about delayed response. I recommend we immediately pause AI categorization for the 'urgent' threshold and revert to manual triage while we investigate. I've notified the customer service manager. Need decision: Should we issue proactive outreach to the affected 12 customers? Please advise by EOD."

Reflection Exercise

Apply what you've learned with a written response

Governance Documentation Requirements

Essential Documents for Each AI Use Case

Intake Form – Describes the use case, data, risks, and mitigations
Approval Record – Documents who approved, when, and with what conditions
Monitoring Plan – Defines metrics to track and review frequency
Incident Log – Records any issues and how they were resolved

Maintaining Your AI Registry

UK ICO guidance on explaining AI-assisted decisions highlights the need for clear internal policies, responsibilities, and reporting lines for explainability and oversight. An AI registry provides this visibility:

Field	Purpose
Use Case Name	Identify the AI system
Owner	Who is accountable
Risk Level	Enables prioritized oversight
Status	Active, paused, retired
Last Review Date	Ensures ongoing attention
Incidents	Tracks issues for patterns

Completion: Governance Artifacts

To complete this module, submit two artifacts:

Artifact 1: AI Use Case Intake Form

Complete the intake form for a real or hypothetical AI use case in your context. Include:

Description and data used
At least 3 potential harms
Risk level with justification
Mitigations for each harm
Human oversight approach

Artifact 2: Escalation Memo

Write an escalation memo for a hypothetical AI incident. Include:

Clear description of the issue
Impact and affected parties
Severity level
Recommended immediate action
Decision requested

Assessment Criteria:

Criterion	Intake Form	Escalation Memo
Completeness	All sections addressed	All required elements included
Specificity	Concrete details, not generic	Clear, actionable information
Risk Awareness	Thoughtful harm identification	Appropriate severity assessment
Quality	Mitigations are realistic	Communication is professional

Practical Exercise

Complete an artifact to demonstrate your skills

Key Takeaways

Governance enables responsible AI at scale—it's not bureaucracy, it's protection
Document use cases properly: intake forms create accountability and enable oversight
Know when to escalate: risk level, novel concerns, and incident severity all matter
Champion role: You're the connector between your team and governance structures
Inaccuracy was companies most-cited gen-AI risk

Sources

Next Steps

In the next module, we'll explore AI Operating Models—understanding how to organize AI capabilities (centralized, federated, or hybrid) and how Champions work within each structure.