Chapter 11 · Group 4: Establish Governance and Accountability
Who is responsible when the AI is wrong?
Build a use-case-specific accountability matrix covering business outcomes, data, technology, model behavior, human oversight, security, compliance, monitoring, incidents, change management, value realization, and retirement.
Builds a complete accountability map for a specific AI use case across 12 dimensions. This tool applies the framework from Chapter 11 of The Enterprise AI Blueprint by W. Perry Underdown and returns a directional, structured read — not an audit finding.
Approximately 15 minutes · Results saved to your workspace · Free to start
Clarifies accountability for
- Business outcomes and value
- Data quality and stewardship
- Model behavior and evaluation
- Human oversight and escalation
- Security, privacy, and compliance
- Monitoring, incidents, and retirement
Output: A shareable RACI-style accountability matrix.
What the assessment covers
Section 1
Use Case Profile
Section 2
1. Business Owner
who owns the business outcome?
Section 3
2. AI Responsibility
who is accountable for how the AI behaves?
Section 4
3. Human Decision Authority
who has final say when a human must decide?
Section 5
4. Evidence
who ensures decisions/actions are logged and auditable?
Section 6
5. Authority
who defines what the AI is allowed to do?
Section 7
6. Boundaries
who defines the limits the AI must stay within?
Section 8
7. Exceptions
who handles cases that fall outside normal rules?
Section 9
8. Appeals
who handles disputes about an AI-driven outcome?
Section 10
9. Monitoring
who watches ongoing performance?
Section 11
10. Intervention
who can pause or override the AI?
Section 12
11. Incident Accountability
who owns response when something goes wrong?
Section 13
12. Lifecycle Accountability
who owns this from launch through retirement?
Continue the journey
Other tools in Establish Governance and Accountability
AI Governance Maturity Assessment
Assess governance from intake and evaluation through deployment, monitoring, review, and retirement — and check whether rigor increases as autonomy and business impact increase.
Assess AI Governance →
AI Operating Model Diagnostic
Evaluate your current operating model against the failure patterns that stall enterprise AI programs.
Diagnose Your Operating Model →
Create your workspace and start AI Accountability & RACI Builder.
Your answers, scores, and reports stay in one workspace so you and your team can track progress across all 16 tools.