Overview & objectives

The AI Governance Operating Model establishes a formal structure for responsible AI decision-making. It ensures strategic direction, operational control, and accountability throughout design, deployment, and monitoring phases. This model connects corporate governance, technical management, and compliance oversight within a unified AIMS (AI Management System).

Governance principles

• Accountability: Final responsibility for AI behaviour remains with the organisation, not the system.
• Transparency: Roles, authorities, and records are clearly defined and published internally.
• Independence: Oversight functions operate independently of development teams.
• Escalation: Defined routes for appeal, override, and intervention.
• Competence: Decision-makers trained in AI risk, ethics, and law.

Roles & responsibilities

• Authorising Officer (AO): Senior executive responsible for AI policy approval, risk acceptance, and certification readiness.
• AIMS Manager: Oversees system implementation, documentation, and audit coordination.
• Oversight Officer: Monitors human-in-the-loop operations and risk metrics.
• Compliance Lead: Manages regulatory mapping, evidence registers, and reporting.
• Model Owner: Accountable for lifecycle integrity (data → model → release → monitor).
• Developers / Data Scientists: Execute risk controls, bias testing, explainability, and validation.
• Ethics & Risk Committee (ARC): Cross-functional body reviewing ethical and social impact risks.

AI governance committees

• AI Governance Board (AIGB): Strategic oversight of AI portfolio, policy, and risk appetite.
  • Meets quarterly; chaired by the AO; reviews dashboards, CAPA trends, and certifications.
• Ethics & Risk Committee (ARC): Evaluates use cases, fairness, and reputational risk before deployment.
• AI Change Advisory Board (AI-CAB): Approves major model releases and dataset changes.
• Operational Oversight Forum: Monthly review of incidents, PMM data, and retraining needs.

Decision rights & approvals

Integration with AIMS

• Governance outputs feed AIMS documentation and audit trail.
• Roles linked to competence matrix and evidence responsibilities.
• Decisions captured in change-control logs with versioning and signatures.

Escalation & oversight

• Escalate policy breaches → AIGB → Board of Directors (within 5 days).
• Critical incidents → Compliance Lead → National authority (AI Act Art 62).
• Oversight findings → feed into CAPA, PMM, and management review.

Documentation & evidence

• Maintain governance org chart, ToRs, minutes, and decision logs.
• Each committee decision linked to an AIMS evidence record.
• Version control via internal doc management system (e.g., Zoho WorkDrive).

Examples & RACI matrix

Activity | Developer | Oversight Officer | Compliance | AO | Ethics Committee
---------|------------|------------------|-------------|----|-----------------
Data collection & cleaning | R | C | I | I | A
Model training & testing | R | A | C | I | C
Bias & robustness evaluation | R | A | C | I | A
Model deployment | R | C | I | A | I
Monitoring & retraining | R | A | C | I | C
  

Common pitfalls & mitigation

• Ambiguous roles: publish governance chart and ToRs for all committees.
• No evidence of leadership review: ensure minutes and actions are archived.
• Overlapping approvals: use RACI to streamline authority lines.
• Passive oversight: empower committees with escalation authority and data access.

Implementation checklist

• Governance structure approved and published.
• Committee ToRs, memberships, and schedules defined.
• Decision rights mapped and documented.
• Escalation procedures tested and logged.
• Evidence packs stored in AIMS repository for audits.

© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 09 Nov 2025 • This page is general guidance, not legal advice.