Business Problem
Why leaders need this framework
AI use cases move from experimentation into consequential work without consistent classification, decision ownership, action boundaries, evidence, monitoring, escalation, or authority to stop.
Governance Model
AI Governance Framework
Create clear accountability, controls, evidence, and operating boundaries for responsible enterprise AI.
Executive Summary
The decision this framework enables
Create clear accountability, controls, evidence, and operating boundaries for responsible enterprise AI.
Executive outcome: A risk-proportionate AI operating model that defines who may decide, what the system may do, and how outcomes remain accountable and reviewable.
Executive audience: Executives, transformation leaders, enterprise architects, and platform owners
Core Model
The dimensions leaders must examine together
No single score replaces evidence or judgment. The dimensions make cross-functional conditions visible before a solution is selected.
Use-case classification
Purpose, users, affected stakeholders, decision role, and operating context are explicit.
Risk tier
Potential impact, likelihood, scale, sensitivity, and reversibility determine control intensity.
Human accountability
A named person remains accountable for the decision, action, and business outcome.
Data and privacy controls
Collection, access, use, retention, confidentiality, and privacy obligations are enforced.
Model and prompt governance
Approved models, configurations, prompts, versions, changes, and limitations are controlled.
Grounding and evidence
Sources, retrieval, confidence, traceability, and factual limitations support responsible use.
Action authorization
The system's allowed recommendations and actions are bounded by explicit authority.
Monitoring
Quality, drift, behavior, exceptions, controls, adoption, and outcomes are observed after launch.
Escalation
People know when, where, and how to challenge, override, pause, or elevate an outcome.
Incident response
Detection, containment, notification, correction, learning, and restart authority are defined.
Auditability
Material inputs, outputs, decisions, actions, versions, approvals, and exceptions can be reconstructed.
Value and outcome review
The use case continues only while it improves the defined outcome within acceptable risk.
Diagnostic Questions
Questions that move the conversation from assumption to evidence
- What decision or action will AI influence, recommend, or execute?
- Who is affected and what harm could occur if the system is wrong or misused?
- Which person remains accountable for each material outcome?
- What evidence, boundaries, and authorization are required before action?
- How can a user challenge, override, or escalate an output?
- What monitoring or outcome would require correction, suspension, or retirement?
Business Friction
How the framework connects to operating value
Good governance reduces ambiguity, unnecessary approval, and distrust while preserving accountability. Poor governance either blocks useful work or permits AI to accelerate unmanaged friction.
Application
A practical executive sequence
- 01
Classify the use case
Document purpose, decision role, stakeholders, data, actions, scale, and business outcome.
- 02
Assign risk and accountability
Select a risk tier and name the business owner, control owners, and launch authority.
- 03
Design controls into work
Define data, model, grounding, action, human-review, escalation, and audit requirements.
- 04
Validate before launch
Test expected behavior, foreseeable misuse, failure handling, user understanding, and operating support.
- 05
Monitor value and risk
Review incidents, drift, exceptions, trust, decision quality, and business outcomes; adjust or stop when required.
Executive Outputs
What the work produces
- AI use-case and risk classification
- Accountability and decision-rights map
- Control and evidence requirements
- Action-authorization boundaries
- Monitoring, escalation, and incident model
- Launch checklist and review cadence
Measures of Success
What should improve
- Every material AI outcome has a named accountable owner
- Controls are proportionate to use-case risk
- Users can understand boundaries and escalate concerns
- Material actions and exceptions are reconstructable
- Business outcomes improve without unacceptable risk or loss of trust
Failure Patterns
What leaders should avoid
- Treating governance as a late approval gate
- Applying one control process to every risk tier
- Assigning accountability to a committee or system
- Monitoring model activity without business outcomes
- Allowing autonomous action beyond explicit authority
- Failing to retire a use case whose value or trust has deteriorated
Executive Decision
The decision leadership must make
Should this AI use case be approved, approved with conditions, paused, or rejected, and who remains accountable after launch?
Related Frameworks
Continue through the methodology
Dave AI metadataApproved structured framework source
Content Studio metadataCanonical, human-reviewed, read-only definition
Framework identifierai-governance-framework
Downloadable Tool
AI Governance Operating Model and Launch Checklist
A practical operating model for classification, accountability, controls, authorization, monitoring, escalation, incident response, and launch approval.