Artificial Intelligence Governance Index™
A structured governance assessment framework and methodology developed by AIGX™ Research to evaluate the maturity of an organization’s AI governance capabilities.
A standardized approach to a question every board is now being asked.
The Index provides a consistent method for assessing how organizations establish oversight, manage risk, implement governance policies, and support responsible AI adoption across the enterprise.
It is designed to measure governance maturity consistently, using documented evidence, defined governance criteria and analyst review — rather than self-declared maturity levels or policy statements.
The output is an evidence-based governance assessment that identifies organizational strengths, maturity gaps and areas for improvement. It is intended to support executive decision-making, governance planning, internal benchmarking and responsible AI adoption initiatives.
Eight domains, assessed against documented evidence.
Together they cover the organizational capabilities that determine whether AI can be deployed and overseen responsibly at enterprise scale.
- Governance & oversightAccountability structures, decision rights, policy and board-level visibility.
- Organizational readinessSkills, operating model, training, documentation and management capability.
- Risk managementClassification, impact analysis, control design, testing and remediation.
- Responsible AI practicesIntended use, transparency, fairness, human oversight and recourse.
- Enterprise architectureSystem design, integration, data flows and technical control points.
- Cybersecurity governanceAccess, model and data security, third-party controls and incident readiness.
- Regulatory alignmentMapping to applicable obligations, frameworks and jurisdictional requirements.
- Operational governanceMonitoring, change management, lifecycle controls and evidence continuity.
What the Index is designed to do.
Standardize the method
Establish a consistent methodology for assessing AI governance maturity.
Evaluate capability
Provide a common framework for assessing enterprise AI governance capabilities.
Support oversight
Give executives and boards a structured basis for AI governance oversight.
Surface gaps
Identify governance risks, maturity gaps and improvement opportunities.
Enable comparison
Make assessments comparable across organizations and industries.
Build toward ratings
Support future AI Governance Ratings™ and benchmarking initiatives.
An index earns the name through use, not through naming.
An assessment framework becomes a benchmark index when it is applied consistently across a broad enough population of organizations to allow meaningful comparison. That generally requires six things.
Standardized method
A defined methodology, applied the same way every time.
Repeatable process
Independent assessment that produces the same result from the same evidence.
A growing dataset
A meaningful body of completed organizational assessments.
Comparable scoring
Results that hold their meaning across organizations, sectors and jurisdictions.
Methodological control
Governance that keeps the method consistent as it evolves.
Market recognition
Adoption by enterprises, industry participants, regulators or investors.
What the Index looks like once a benchmark exists.
The view below shows how aggregated assessments would present sector medians, maturity distribution and anonymized cohort position. It is a design illustration of a planned capability, not current data.
| Entity | Sector | Designation | Percentile | 12-month movement |
|---|---|---|---|---|
| ENT-041 | Health systems | AIGR-80 | 88th | Up one band |
| ENT-126 | Banking | AIGR-80 | 79th | Unchanged |
| ENT-017 | Insurance | AIGR-70 | 61st | Up one band |
| ENT-093 | Public sector | AIGR-60 | 34th | Unchanged |
| ENT-058 | Clinical AI vendor | AIGR-50 | 12th | Down one band |
Illustrative only
Entities, sectors, designations and distributions shown above are for illustrative purposes only. They are not issued ratings, do not describe any real organization, and no benchmark cohort of this kind exists today. Benchmark capability depends on a sufficient body of completed assessments, and cohorts would be constructed under consent and confidentiality rules with results reported in aggregate.
Where the Index stands today.
Today
The Artificial Intelligence Governance Index™ is an enterprise AI governance assessment methodology and framework for measuring AI governance maturity, organizational readiness, and responsible AI capabilities.
In time
As adoption expands and a meaningful body of assessments is established, the Index has the potential to serve as a recognized benchmark for AI governance maturity across industries and markets — producing sector benchmarks, maturity distributions, industry trends and comparative analytics. That distinction depends on broad market use and demonstrated comparability over time, rather than being created simply by naming it an index.
Ongoing
AIGX™ Research maintains and continuously improves the Index methodology. Criteria, evidence requirements and scoring guidance are reviewed as responsible AI guidance develops and as new regulatory and compliance standards take effect — among them the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and ISO/IEC 23894, and comparable instruments in other jurisdictions. Material changes are versioned and documented with a rationale and an effective date, so an assessment can always be read against the methodology version under which it was produced.
Assess one part of your organization.
A scoping conversation maps your AI governance capabilities to the Index domains and identifies where the evidence gaps sit today.
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