2.2 Roles & Responsibilities (RACI Matrix)
Clear accountability is the cornerstone of effective AI governance. This section defines the key roles, their responsibilities, and provides a comprehensive RACI matrix for AI governance activities across the enterprise.
Research indicates that unclear roles cause nearly one-third of project failures. Organizations with well-defined RACI matrices deploy AI 40% faster and face 60% fewer post-deployment compliance issues compared to those with siloed approaches.
Understanding the RACI Framework
The RACI matrix is a structured framework that clarifies AI governance roles across all stages of the AI lifecycle. Each activity is assigned to stakeholders under one of four categories:
Responsible
The person(s) who perform the work to achieve the task. Multiple people can be responsible, but should have clear delegation.
Accountable
The single person who is ultimately answerable for the activity. Only one person can be accountable for each task.
Consulted
Key stakeholders whose input is sought before decisions are made or work is completed. Two-way communication.
Informed
Stakeholders who need to be kept up-to-date on progress. One-way communication, typically after completion.
2.2.1 The AI Ethics Board / RAI Council
The AI Ethics Board (also known as the Responsible AI Council) serves as the highest governance body for AI-related ethical decisions within the organization.
Composition
An effective AI Ethics Board should include diverse perspectives:
| Role | Function | Key Contribution |
|---|---|---|
| Executive Sponsor | CEO, COO, or designated C-suite | Strategic alignment, resource allocation, final escalation point |
| Chief AI Ethics Officer | Dedicated ethics leadership | Ethics strategy, policy development, external engagement |
| Chief Data Officer | Data governance leadership | Data quality, lineage, privacy compliance |
| General Counsel | Legal leadership | Regulatory compliance, liability assessment, contract review |
| CISO | Security leadership | AI security, adversarial threats, data protection |
| Business Unit Leaders | Operational leadership | Use case context, business impact assessment |
| External Advisor | Independent perspective | Industry best practices, academic insights, bias |
Core Responsibilities
- Policy Governance: Establishing and maintaining AI ethics policies and principles
- High-Risk Review: Approving or rejecting high-risk AI applications
- Incident Response: Overseeing major AI-related incidents and remediation
- Standards Setting: Defining fairness metrics, bias thresholds, and transparency requirements
- External Engagement: Representing the organization in regulatory and industry discussions
- Culture Building: Championing responsible AI practices throughout the organization
The AI Ethics Board should meet at minimum monthly, with emergency sessions available for urgent matters. Document all decisions with clear rationale for audit trails.
2.2.2 The Chief AI Officer (CAIO) vs. CISO Roles
As AI becomes central to business operations, the Chief AI Officer role has emerged as a critical C-suite position. Chief AI Officer recruitment has tripled in the last five years, reflecting the growing strategic importance of AI leadership.
CAIO vs. CISO: Distinct but Complementary Roles
| Dimension | Chief AI Officer (CAIO) | Chief Information Security Officer (CISO) |
|---|---|---|
| Primary Focus | AI strategy, ethics, and value creation | Information security, risk management, compliance |
| AI Responsibility | AI development lifecycle, model governance, ethical AI | AI security, adversarial threats, data protection in AI |
| Risk Domain | Bias, fairness, transparency, regulatory compliance | Cybersecurity, data breaches, model attacks |
| Key Metrics | AI ROI, model performance, ethics KPIs | Security incidents, vulnerability metrics, compliance scores |
| Collaboration Point | AI Security Posture Management, Adversarial Testing, Data Governance | |
CAIO Core Responsibilities
Strategic Leadership
- Define AI vision and roadmap
- Align AI initiatives with business strategy
- Champion AI adoption across units
- Report to board on AI progress
Governance Oversight
- Chair or co-chair AI Ethics Board
- Establish AI governance frameworks
- Ensure regulatory compliance
- Manage AI risk portfolio
Operational Excellence
- Oversee AI talent acquisition
- Manage AI technology stack
- Drive MLOps maturity
- Ensure model quality standards
2.2.3 Model Owners & Data Stewards
Model Owners
Model Owners are accountable for specific AI models throughout their lifecycle. They serve as the single point of accountability for model performance, compliance, and incidents.
| Responsibility Area | Specific Duties |
|---|---|
| Documentation | Maintain Model Cards, system documentation, and change logs |
| Performance | Monitor model accuracy, drift detection, and retraining triggers |
| Compliance | Ensure model meets regulatory requirements and internal policies |
| Risk Management | Conduct periodic risk assessments and impact reviews |
| Incident Response | Lead investigation and remediation for model-related incidents |
| Stakeholder Communication | Report to governance bodies, communicate with deployers |
Data Stewards
Data Stewards are responsible for the quality, integrity, and appropriate use of data within their domains. For AI governance, they play a critical role in ensuring training data meets quality and ethical standards.
Data Quality
Ensure data accuracy, completeness, timeliness, and consistency for AI training and inference.
Data Lineage
Document data provenance, transformations, and dependencies for traceability.
Bias Assessment
Evaluate datasets for representation bias, historical bias, and sampling issues.
Access Control
Manage data access permissions and ensure appropriate use for AI applications.
Complete AI Governance RACI Matrix
The following matrix defines accountability across the AI lifecycle, aligned with the NIST AI Risk Management Framework's four functions: Govern, Map, Measure, and Manage.
Governance Activities
| Activity | CAIO | Ethics Board | Model Owner | Data Steward | Legal | CISO | Internal Audit |
|---|---|---|---|---|---|---|---|
| AI Strategy Definition | A/R | C | I | I | C | C | I |
| Policy Development | A | R | C | C | R | C | C |
| Risk Appetite Setting | C | A/R | I | I | C | C | I |
| Use Case Approval (High-Risk) | C | A | R | C | C | C | I |
| Regulatory Compliance | A | I | C | C | R | C | R |
Development & Deployment Activities
| Activity | CAIO | Model Owner | Data Steward | Dev Team | Legal | CISO | QA |
|---|---|---|---|---|---|---|---|
| Data Collection & Curation | I | A | R | R | C | C | I |
| Model Development | I | A | C | R | I | C | I |
| Bias Testing | I | A | C | R | C | I | R |
| Security Testing | I | A | I | R | I | R | R |
| Model Card Documentation | I | A/R | C | R | C | I | I |
| Deployment Decision | C | A | C | I | C | C | C |
Monitoring & Maintenance Activities
| Activity | CAIO | Model Owner | Data Steward | Ops Team | CISO | Internal Audit |
|---|---|---|---|---|---|---|
| Performance Monitoring | I | A | C | R | I | I |
| Drift Detection | I | A | R | R | I | I |
| Continuous Bias Monitoring | I | A/R | C | R | I | I |
| Incident Response | C | A/R | C | R | R | I |
| Model Retraining | I | A | R | R | C | I |
| Annual Governance Audit | A | C | C | I | C | R |
Implementation Steps
Conduct Role Gap Analysis
Map existing roles against required AI governance positions. Identify gaps and overlaps in current accountability structures.
Deliverable: Role Gap Assessment Report
Timeline: 2-4 weeks
Define Role Charters
Create detailed role charters for each AI governance position, including scope, authority, reporting relationships, and success metrics.
Deliverable: Role Charter Documents
Timeline: 3-4 weeks
Establish AI Ethics Board
Form the AI Ethics Board with appropriate composition, define charter, meeting cadence, and decision-making protocols.
Deliverable: AI Ethics Board Charter, First Meeting
Timeline: 4-6 weeks
Assign Model Owners
Inventory all AI models and assign Model Owners. Ensure owners understand their accountability and have necessary authority.
Deliverable: Model Ownership Registry
Timeline: 2-3 weeks
Operationalize RACI Matrix
Embed RACI assignments into project management tools, workflow systems, and governance platforms. Train all stakeholders.
Deliverable: Integrated RACI Workflows, Training Completion
Timeline: 4-6 weeks
Establish Review Cadence
Define quarterly reviews of RACI effectiveness, role performance, and governance outcomes. Update matrix as organization evolves.
Deliverable: Governance Review Calendar
Timeline: Ongoing
- 100% of AI models have assigned Model Owners
- AI Ethics Board meets monthly with documented decisions
- RACI assignments clear for all AI lifecycle activities
- Reduction in governance-related project delays by 40%
- Increase in first-time compliance approval rates