AI Governance Board (AIGB)

Definition

An AI Governance Board (AIGB) is a formal, cross-functional decision-making body that sets direction, policies, and oversight for how an organization designs, buys, deploys, and monitors AI systems. It typically defines accountability, approves high-risk AI use cases, and ensures controls exist for risk management, compliance, and operational monitoring.

In many governance frameworks, “govern” is treated as the organizing function that establishes roles, processes, and oversight needed to manage AI risk across the AI lifecycle.

How it relates to marketing

Marketing teams increasingly use AI for audience segmentation, personalization, next-best-action, media optimization, conversational experiences, and generative content. An AIGB helps marketing leaders ensure these uses align with:

• Customer data permissions and privacy expectations
• Brand standards and disclosure requirements
• Bias/fairness and customer impact considerations
• IP/copyright and content provenance expectations
• Vendor and third-party model risk controls

At the enterprise level, board and executive oversight of AI has become a common governance topic (often because “AI risk” has a habit of showing up right after “AI opportunity” on agendas).

How to calculate

An AIGB itself isn’t “calculated,” but its coverage and effectiveness can be measured using operational KPIs. Common examples:

• AI use case review coverage
  (# of AI use cases reviewed by AIGB ÷ total # of AI use cases in production or pilot) × 100
• Policy compliance rate
  (# of AI systems passing required controls ÷ # of AI systems assessed) × 100
• Model monitoring coverage
  (# of production AI systems with defined monitoring + alerting ÷ # of production AI systems) × 100
• Risk remediation cycle time
  Average days from issue identification to closure for AI-related findings (privacy, bias, security, model drift, vendor gaps)
• Third-party AI assessment completion
  (# of AI vendors assessed to standard ÷ # of AI vendors in scope) × 100

How to utilize

Common AIGB use cases include:

• Approving (or rejecting) AI use cases based on risk tiering (especially customer-facing use cases in marketing)
• Maintaining an AI inventory (systems, owners, data inputs, vendors, models, purposes)
• Defining AI policies and standards (acceptable use, human review, disclosure, data handling, retention, incident response)
• Establishing lifecycle controls aligned to recognized frameworks (for example, governance plus mapping/measuring/managing risk)
• Overseeing adoption of AI management system standards (for example, ISO/IEC 42001 as an AI management system foundation)
• Reviewing exceptions and documenting risk acceptance decisions
• Coordinating cross-functional response for AI incidents (model failures, harmful outputs, data leakage, compliance issues)
• Setting requirements for marketing-specific AI (brand safety guardrails, claims substantiation workflows, consent checks)

Compare to similar approaches

Approach	What it is	How it differs from an AIGB	Where it shows up in marketing orgs
Data Governance Board	Governs data quality, access, definitions, stewardship	Focuses on data; may not cover model behavior, AI risk, or AI-specific controls	Customer data access, consent enforcement, taxonomy alignment
Model Risk Management (MRM) Committee	Oversees model risk (often quantitative/regulated contexts)	Typically narrower: models and validation; may not cover broader AI policy and enterprise use cases	MMM/attribution models, propensity models, credit-like scoring in certain industries
AI Ethics Committee	Reviews ethical considerations and societal impact	Can be advisory; may lack operational authority over deployment and controls	Fairness in targeting, sensitive segmentation, vulnerable audiences
AI Center of Excellence (AI CoE)	Builds capabilities, patterns, reusable assets	Usually enablement-oriented; may not be the final approval authority	Shared services for experimentation, prompt libraries, evaluation tooling
Security/Risk Committee	Oversees security and enterprise risk	Covers AI as one risk category; may not own AI lifecycle governance	AI vendor security reviews, threat modeling for AI tools
Product/Technology Steering Committee	Prioritizes product/tech investments	Focuses on roadmaps and funding, not necessarily AI controls and compliance	Funding genAI tooling, personalization engines, decisioning platforms

Best practices

• Establish a written charter: purpose, authority, scope, escalation paths, and decision rights (including exception handling). Public-sector examples of AI governance charters show how membership, authority, and cadence are commonly defined.
• Define membership by function and decision rights: marketing, legal/privacy, security, data, risk/compliance, procurement, and business owners.
• Create a risk-tiering approach: route low-risk use cases through lightweight review and reserve AIGB time for higher-risk decisions.
• Require minimum documentation per use case: intended purpose, data inputs, evaluation results, monitoring plan, human oversight, and vendor details.
• Operationalize lifecycle controls: pre-launch review, post-launch monitoring, incident handling, and periodic reassessment.
• Integrate with existing governance: data governance, security reviews, procurement, and marketing compliance so approvals don’t become a parallel universe.
• Make outcomes auditable: decision logs, rationale, and evidence artifacts (especially important as standards-based AI management approaches mature).

Future trends

• Standardization around AI management systems (including increased use of ISO/IEC 42001-style management system approaches).
• More board-level visibility and governance expectations, with clearer patterns for how boards and executive committees oversee AI strategy and risk.
• Continuous controls monitoring for AI (automated policy checks, logging, evaluation pipelines, drift detection)
• AI assurance and third-party audits becoming more common for high-impact systems and vendors
• Stronger governance for generative AI content: provenance, watermarking/metadata, claim substantiation, and marketing disclosure workflows

Related Terms

• AI governance
• AI policy
• Responsible AI
• AI risk management
• NIST AI Risk Management Framework (AI RMF)
• ISO/IEC 42001
• Model governance
• Data governance board
• Model monitoring (drift/performance)
• AI inventory (model and use-case registry)