Skip to main content

​
AIGO — AI Governance Operating Framework

​
AI Governance Maturity Model

Version: 0.1
Status: Draft
Working Name: AIGO
Full Name: AI Governance Operating Framework

​
1. Purpose

The AIGO AI Governance Maturity Model provides a structured approach for organizations to assess, understand, and improve the maturity of their AI governance capabilities. The maturity model is intended to help organizations determine their current governance capability, identify gaps, establish improvement priorities, and measure progress over time. The maturity model should be used as an improvement and decision-support mechanism rather than as a certification scheme.

​
2. Maturity Model Principles

The AIGO maturity model is based on the following principles:
  • maturity should reflect actual organizational capability;
  • maturity should consider both documented processes and operational effectiveness;
  • maturity should be assessed using evidence where practical;
  • maturity should be evaluated in relation to organizational context;
  • maturity should be proportionate to AI risk and complexity;
  • organizations may have different maturity levels across different domains;
  • higher maturity does not automatically mean lower AI risk;
  • maturity assessments should support improvement; and
  • maturity should be reviewed as organizational capabilities evolve.

​
3. Purpose of Maturity Assessment

A maturity assessment may be used to:
  • establish a baseline;
  • identify governance gaps;
  • prioritize improvements;
  • support management decisions;
  • evaluate governance capability;
  • measure progress;
  • support internal assurance;
  • inform investment decisions; and
  • communicate governance capability to relevant stakeholders.
Organizations should define the intended purpose of each maturity assessment before performing it.

​
4. Maturity Model Scope

The AIGO maturity model may be applied to:
  • an entire organization;
  • an AI governance program;
  • an AI governance domain;
  • an AI portfolio;
  • an individual AI system;
  • a business unit;
  • a specific governance capability; or
  • a particular AI System Profile.
The scope of the assessment should be clearly defined.

​
5. Maturity Dimensions

AIGO maturity may be evaluated across multiple dimensions. Potential dimensions include:
  • governance;
  • strategy;
  • accountability;
  • risk management;
  • controls;
  • lifecycle management;
  • data governance;
  • security;
  • privacy;
  • human oversight;
  • transparency;
  • monitoring;
  • incident management;
  • third-party governance;
  • assurance; and
  • continuous improvement.
Organizations may add or remove dimensions according to their context.

​
6. Maturity Levels

AIGO defines five maturity levels:
  1. Initial
  2. Developing
  3. Defined
  4. Managed
  5. Optimized
The levels represent increasing organizational capability, consistency, measurement, integration, and improvement. The levels should not be interpreted as a simple measure of organizational quality.

​
7. Level 1 — Initial

At the Initial level, AI governance activities are generally ad hoc, reactive, or dependent on individual knowledge. Characteristics may include:
  • limited formal governance;
  • unclear accountability;
  • inconsistent processes;
  • limited documentation;
  • reactive risk management;
  • inconsistent controls;
  • limited monitoring;
  • limited evidence; and
  • significant dependency on individual expertise.
AI governance may exist, but it is not yet consistently established across the organization.

​
8. Level 2 — Developing

At the Developing level, the organization has begun establishing repeatable AI governance practices. Characteristics may include:
  • emerging governance structures;
  • defined responsibilities in selected areas;
  • developing policies;
  • initial AI inventories;
  • repeatable risk assessments;
  • emerging controls;
  • initial lifecycle processes;
  • increasing documentation;
  • developing training; and
  • basic monitoring.
Practices may still vary significantly across teams or AI systems.

​
9. Level 3 — Defined

At the Defined level, AI governance processes are formally established and documented. Characteristics may include:
  • approved governance policies;
  • defined roles;
  • documented processes;
  • established AI inventory;
  • structured risk management;
  • defined controls;
  • lifecycle integration;
  • documented evidence requirements;
  • defined assessment methods; and
  • consistent governance terminology.
Processes are intended to be applied consistently across relevant organizational areas.

​
10. Level 4 — Managed

At the Managed level, AI governance processes are consistently implemented, measured, and monitored. Characteristics may include:
  • organization-wide governance;
  • measurable governance objectives;
  • effective control monitoring;
  • formal risk reporting;
  • performance indicators;
  • assurance activities;
  • systematic evidence management;
  • management oversight;
  • defined exception handling; and
  • continuous monitoring.
Governance performance is actively managed using information and evidence.

​
11. Level 5 — Optimized

At the Optimized level, AI governance is continuously improved using measurement, evidence, experience, and emerging information. Characteristics may include:
  • continuous improvement;
  • advanced automation;
  • predictive risk management;
  • integrated assurance;
  • mature governance analytics;
  • proactive emerging-risk management;
  • strong organizational learning;
  • systematic optimization; and
  • highly integrated governance capabilities.
The organization actively adapts its governance capability to changing AI technology, risks, requirements, and organizational objectives.

​
12. Maturity Level Characteristics

The maturity levels should be evaluated across several characteristics. These may include:
  • process definition;
  • consistency;
  • ownership;
  • documentation;
  • implementation;
  • measurement;
  • evidence;
  • monitoring;
  • assurance;
  • automation; and
  • improvement.
An organization may demonstrate different characteristics at different levels.

​
13. Process Maturity

Process maturity evaluates whether AI governance processes are:
  • informal;
  • repeatable;
  • documented;
  • consistently implemented;
  • measured; and
  • continuously improved.
Process maturity should consider actual operational behavior rather than documentation alone.

​
14. Governance Maturity

Governance maturity evaluates the organization’s ability to establish and maintain effective AI governance structures. Considerations may include:
  • governance bodies;
  • accountability;
  • decision rights;
  • policies;
  • escalation;
  • reporting;
  • oversight; and
  • management involvement.

​
15. Risk Management Maturity

Risk management maturity evaluates the organization’s capability to identify, assess, treat, monitor, and communicate AI risks. Maturity may progress from:
  • informal risk identification;
  • repeatable assessments;
  • defined risk methodology;
  • measured risk management; to
  • proactive and continuously improving risk management.

​
16. Control Maturity

Control maturity evaluates the organization’s ability to define, implement, operate, monitor, test, and improve AI governance controls. Considerations may include:
  • control definition;
  • ownership;
  • applicability;
  • implementation;
  • evidence;
  • testing;
  • effectiveness;
  • remediation; and
  • continuous improvement.

​
17. Lifecycle Governance Maturity

Lifecycle governance maturity evaluates how effectively governance is integrated across the AI lifecycle. Higher maturity should demonstrate that governance is not limited to initial approval but continues through:
  • development;
  • testing;
  • deployment;
  • operation;
  • monitoring;
  • change;
  • incident management; and
  • retirement.

​
18. Evidence Maturity

Evidence maturity evaluates the organization’s ability to generate, maintain, protect, retrieve, and use governance evidence. Maturity may progress from:
  • limited evidence;
  • inconsistent evidence;
  • defined evidence requirements;
  • systematic evidence management; to
  • integrated and automated evidence collection.

​
19. Monitoring Maturity

Monitoring maturity evaluates the organization’s capability to continuously or periodically monitor AI systems and governance activities. Monitoring may include:
  • system performance;
  • risk indicators;
  • control effectiveness;
  • incidents;
  • model behavior;
  • data quality;
  • security events;
  • privacy events; and
  • governance metrics.

​
20. Assurance Maturity

Assurance maturity evaluates the organization’s ability to independently or objectively assess whether AI governance is functioning as intended. Assurance activities may include:
  • self-assessment;
  • management review;
  • control testing;
  • internal audit;
  • independent assessment;
  • external assessment; and
  • technical validation.
Higher maturity should provide greater confidence that governance claims are supported by evidence.

​
21. Continuous Improvement Maturity

Continuous improvement maturity evaluates whether the organization systematically learns from governance experience. Improvement inputs may include:
  • incidents;
  • audit findings;
  • control failures;
  • risk assessments;
  • stakeholder feedback;
  • monitoring results;
  • regulatory developments;
  • technology changes; and
  • lessons learned.
Higher maturity should demonstrate that improvement is systematic rather than reactive.

​
22. Maturity Assessment Method

A maturity assessment should define:
  1. Assessment scope
  2. Assessment criteria
  3. Evidence requirements
  4. Assessment participants
  5. Rating methodology
  6. Assessment period
  7. Findings
  8. Improvement priorities
  9. Approval
  10. Review date
The assessment method should be documented and repeatable.

​
23. Evidence-Based Assessment

Maturity ratings should be supported by appropriate evidence where practical. Evidence may include:
  • policies;
  • procedures;
  • records;
  • risk assessments;
  • control results;
  • training records;
  • monitoring reports;
  • audit reports;
  • governance meeting records;
  • system configurations; and
  • other relevant artifacts.
The existence of documentation alone should not automatically justify a maturity rating.

​
24. Assessment Interviews

Interviews may be used as part of a maturity assessment. Participants may include:
  • executives;
  • governance teams;
  • risk teams;
  • security teams;
  • privacy teams;
  • AI system owners;
  • developers;
  • operators;
  • business users; and
  • assurance personnel.
Interview results should be considered together with available evidence.

​
25. Maturity Scoring

Organizations may use numerical scoring to support maturity assessments. For example:
  • Level 1 = 1
  • Level 2 = 2
  • Level 3 = 3
  • Level 4 = 4
  • Level 5 = 5
Numerical scores should support analysis but should not replace professional judgment.

​
26. Domain-Level Maturity

Organizations may calculate maturity separately for each governance domain. For example: Domain-level maturity helps identify uneven capability across the organization.

​
27. Overall Maturity

Organizations may calculate an overall maturity indication where useful. An overall rating should consider:
  • domain maturity;
  • critical capabilities;
  • risk exposure;
  • organizational context;
  • material deficiencies; and
  • assessment evidence.
A simple mathematical average should not automatically determine the final maturity rating. Critical weaknesses may require the overall maturity level to remain below the highest individual domain rating.

​
28. Maturity Gaps

A maturity gap exists where the organization’s current capability does not meet its desired maturity level. Gap analysis should identify:
  • current state;
  • target state;
  • capability gap;
  • risk associated with the gap;
  • improvement action;
  • responsible owner; and
  • target completion date.

​
29. Target Maturity

Organizations should define target maturity according to their:
  • AI strategy;
  • AI risk profile;
  • regulatory environment;
  • organizational size;
  • complexity;
  • stakeholder expectations;
  • operational requirements; and
  • available resources.
Not every organization requires Level 5 maturity across every capability.

​
30. Risk-Based Maturity

Maturity targets should be proportionate to AI risk. Higher-risk environments may require stronger maturity in areas such as:
  • risk management;
  • controls;
  • security;
  • privacy;
  • human oversight;
  • monitoring; and
  • assurance.
Lower-risk environments may use simpler governance capabilities where appropriate.

​
31. Maturity Improvement Planning

Organizations should establish improvement plans where material maturity gaps are identified. An improvement plan may include:
  • identified gap;
  • desired maturity;
  • improvement objective;
  • actions;
  • owner;
  • priority;
  • resources;
  • target date;
  • dependencies; and
  • success criteria.

​
32. Maturity Prioritization

Improvement priorities should consider:
  • risk;
  • regulatory obligations;
  • business importance;
  • stakeholder impact;
  • dependency relationships;
  • effort;
  • cost;
  • feasibility; and
  • expected benefit.
High-risk capability gaps should generally receive appropriate priority.

​
33. Maturity Assessment Frequency

Maturity assessments should be performed periodically according to organizational needs. Assessment frequency may depend on:
  • AI risk;
  • organizational change;
  • governance maturity;
  • regulatory developments;
  • major AI initiatives;
  • significant incidents; and
  • changes in organizational strategy.
Higher-risk environments may require more frequent assessments.

​
34. Maturity Assessment Triggers

A reassessment may be triggered by:
  • significant AI incidents;
  • major governance changes;
  • new regulations;
  • major technology changes;
  • acquisition of new AI capabilities;
  • substantial changes in AI risk;
  • organizational restructuring;
  • significant audit findings; or
  • material changes in AI strategy.

​
35. Maturity and AI System Profiles

AI System Profiles may have different expected governance maturity requirements. For example, an autonomous AI system may require stronger maturity in:
  • human oversight;
  • monitoring;
  • security;
  • incident management;
  • change management; and
  • assurance.
Profile-specific maturity expectations should remain proportionate to actual risk.

​
36. Maturity and Controls

Maturity should consider whether controls are:
  • defined;
  • implemented;
  • operating;
  • monitored;
  • tested;
  • effective; and
  • continuously improved.
A large number of documented controls should not automatically indicate high maturity.

​
37. Maturity and Evidence

Evidence should demonstrate actual governance capability. Examples may include:
  • completed assessments;
  • operating control records;
  • governance decisions;
  • monitoring results;
  • remediation records;
  • audit findings;
  • incident records; and
  • improvement results.
Evidence should be relevant, reliable, and appropriate to the capability being assessed.

​
38. Maturity and Automation

Automation may support higher maturity but should not be treated as a maturity requirement by itself. Automation may improve:
  • consistency;
  • scalability;
  • monitoring;
  • evidence collection;
  • reporting;
  • control execution; and
  • governance efficiency.
Automated processes should remain subject to appropriate oversight.

​
39. Maturity and Organizational Culture

AI governance maturity includes organizational behavior and culture. Relevant considerations may include:
  • leadership commitment;
  • employee awareness;
  • accountability;
  • willingness to report issues;
  • challenge and review;
  • cross-functional collaboration; and
  • continuous learning.
Strong governance requires appropriate organizational behavior in addition to documented processes.

​
40. Maturity and Competence

Organizations should maintain sufficient competence to support their AI governance objectives. Competence may include:
  • AI knowledge;
  • risk management;
  • security;
  • privacy;
  • legal and regulatory knowledge;
  • data governance;
  • technical skills;
  • operational knowledge; and
  • assurance capabilities.
Competence requirements should be proportionate to organizational responsibilities and AI risk.

​
41. Maturity and Training

Training and awareness may support AI governance maturity. Training may cover:
  • AI governance;
  • acceptable AI use;
  • risk awareness;
  • security;
  • privacy;
  • responsible AI;
  • incident reporting;
  • human oversight; and
  • role-specific responsibilities.
Training effectiveness should be considered where appropriate.

​
42. Maturity and Third Parties

Organizations should consider third-party governance maturity where AI systems depend on external providers. Considerations may include:
  • supplier assessment;
  • contractual controls;
  • provider monitoring;
  • service reviews;
  • dependency management;
  • exit planning; and
  • third-party assurance.

​
43. Maturity and Incident Management

Mature AI governance should include the ability to identify, report, respond to, investigate, and learn from AI-related incidents. Incident maturity may include:
  • defined procedures;
  • reporting channels;
  • roles;
  • escalation;
  • investigation;
  • containment;
  • remediation;
  • root-cause analysis; and
  • lessons learned.

​
44. Maturity and Change Management

Mature organizations should assess the governance implications of AI system changes. Change management may consider:
  • model changes;
  • data changes;
  • architecture changes;
  • new capabilities;
  • increased autonomy;
  • new integrations;
  • new users;
  • new use cases; and
  • changes in risk.
Material changes should trigger appropriate reassessment.

​
45. Maturity and Governance Reporting

Mature AI governance should provide management with useful information about governance capability and performance. Reporting may include:
  • maturity levels;
  • capability gaps;
  • risk trends;
  • control effectiveness;
  • incidents;
  • remediation;
  • assurance results; and
  • improvement progress.

​
46. Maturity and Continuous Monitoring

Higher maturity should include the ability to monitor governance capability over time. Monitoring may identify:
  • maturity changes;
  • declining performance;
  • emerging gaps;
  • recurring deficiencies;
  • improvement progress; and
  • areas requiring additional investment.

​
47. Maturity Benchmarking

Organizations may use benchmarking to compare their AI governance maturity over time or against defined internal targets. External benchmarking should be interpreted carefully because organizations differ in:
  • size;
  • industry;
  • AI use;
  • risk exposure;
  • regulatory environment; and
  • organizational structure.
Benchmarking should not be treated as proof of governance effectiveness.

​
48. Maturity Evidence Integrity

Evidence used for maturity assessments should be sufficiently reliable to support the conclusions drawn from it. Organizations should consider:
  • evidence source;
  • evidence date;
  • completeness;
  • authenticity;
  • relevance;
  • consistency; and
  • independence where appropriate.
Material maturity claims should be supported by appropriate evidence.

​
49. Maturity Assessment Independence

For material or high-risk assessments, organizations should consider whether independent review is appropriate. Independent review may be performed by:
  • internal audit;
  • risk functions;
  • compliance functions;
  • qualified assessors;
  • external reviewers; or
  • other appropriately independent personnel.
The level of independence should be proportionate to the purpose and risk of the assessment.

​
50. Maturity Assessment Limitations

A maturity assessment provides an indication of governance capability at a particular point in time. It does not by itself establish:
  • legal compliance;
  • regulatory compliance;
  • absence of AI risk;
  • control effectiveness in every circumstance;
  • absence of incidents; or
  • fitness for every AI use case.
Organizations should interpret maturity results within their broader governance and risk context.

​
51. Maturity Improvement Cycle

AIGO recommends a continuous maturity improvement cycle:
  1. Assess current capability.
  2. Identify gaps.
  3. Determine target maturity.
  4. Prioritize improvements.
  5. Implement improvements.
  6. Measure results.
  7. Review evidence.
  8. Reassess maturity.
  9. Update improvement priorities.
The cycle should be repeated as organizational needs evolve.

​
52. Maturity Governance

The maturity assessment process should have defined ownership and governance. Governance should establish:
  • assessment authority;
  • methodology;
  • responsibilities;
  • evidence requirements;
  • approval;
  • review;
  • reporting; and
  • improvement processes.

​
53. Maturity Records

Organizations should maintain appropriate records of maturity assessments. Records may include:
  • assessment scope;
  • methodology;
  • participants;
  • evidence;
  • ratings;
  • findings;
  • target maturity;
  • improvement plans;
  • approvals; and
  • review dates.
Records should support historical comparison and traceability.

​
54. Maturity Versioning

Changes to the AIGO maturity model should be versioned. Version changes may include:
  • new maturity levels;
  • revised criteria;
  • new dimensions;
  • revised assessment methods;
  • clarification;
  • changes in evidence requirements; and
  • other material changes.
Historical assessments should remain interpretable where practical.

​
55. Maturity Model Extensibility

Organizations may extend the AIGO maturity model with additional dimensions or criteria. Extensions should:
  • document their purpose;
  • define assessment criteria;
  • identify evidence requirements;
  • preserve traceability to AIGO;
  • avoid unnecessary duplication; and
  • document any differences from the core AIGO model.

​
56. Maturity and External Frameworks

AIGO maturity assessments may be mapped to other organizational or external maturity models where useful. Mappings may support:
  • comparison;
  • integration;
  • reporting;
  • assessment efficiency; and
  • organizational alignment.
External mappings should be maintained separately from the core maturity definitions where practical.

​
57. Maturity and Assurance

Maturity results may be used as an input to assurance planning. Lower maturity may indicate a need for:
  • additional review;
  • stronger controls;
  • increased monitoring;
  • additional testing; or
  • improvement activity.
Maturity should not replace risk-based assurance.

​
58. Maturity and Governance Decisions

Maturity information may support decisions concerning:
  • AI deployment;
  • governance investment;
  • control requirements;
  • staffing;
  • training;
  • assurance;
  • technology adoption; and
  • risk treatment.
Maturity information should be considered together with actual system risk and organizational context.

​
59. Maturity Continuous Improvement

The AIGO maturity model itself should be periodically reviewed and improved. Improvement inputs may include:
  • implementation experience;
  • assessment results;
  • stakeholder feedback;
  • emerging AI risks;
  • changes in governance practice;
  • regulatory developments;
  • external research; and
  • lessons learned.

​
60. Document Status

Document: AIGO AI Governance Maturity Model Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Type: AI Governance Maturity Model Identifier Prefix: AIGO-MAT This document defines the foundational maturity model for assessing and improving AI governance capability within the AIGO framework. Organizations may adapt the maturity model according to their organizational structure, AI systems, risk profile, regulatory environment, governance objectives, and organizational maturity while maintaining evidence-based assessment, appropriate accountability, and continuous improvement.