Governance maturity models are structured frameworks that help organizations assess the strength and progress of their governance practices.
These models offer levels or stages to describe how well governance processes are defined, measured, and improved over time. They are often used to guide companies toward better decision-making, transparency, and risk management.
Strong governance maturity matters because it helps organizations build accountability, manage compliance, and reduce operational and ethical risks—especially in areas like AI development and deployment. For teams working on AI governance, these models offer a clear path to move from basic controls to well-managed, optimized systems aligned with legal and ethical standards.
According to a 2023 Deloitte report, only 23% of organizations rate their governance processes as mature or advanced—yet 78% of those that do report higher trust levels from stakeholders.
What is a governance maturity model
A governance maturity model breaks governance into different levels, typically from ad hoc or reactive practices to optimized and continuously improving processes.
The idea is to assess where an organization currently stands and identify actions needed to advance. These levels help set goals and compare maturity across different departments or domains.
For AI governance, this could mean assessing how data is managed, how models are monitored, how decisions are audited, and how risks are tracked. Many models are adapted from IT or corporate governance frameworks and adjusted to include AI-specific issues such as fairness, transparency, and model accountability.
Key stages of maturity
Most models include 4 to 5 levels, usually with the following structure:
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Initial: Processes are informal, undocumented, and reactive.
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Defined: Governance processes are documented but inconsistently applied.
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Managed: Responsibilities are assigned and metrics are in place.
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Monitored: Governance performance is regularly tracked and reviewed.
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Optimized: Governance is continuously improved based on feedback and outcomes.
Each stage builds on the last. An organization at the “defined” level may have basic governance policies for AI model documentation. At the “monitored” level, that same organization would have versioned model audit trails and incident response protocols.
Why AI teams use governance maturity models
AI systems carry high regulatory, ethical, and reputational risks. A maturity model helps AI teams:
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Identify weak spots in documentation, explainability, or risk assessments
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Build repeatable governance workflows instead of ad hoc decisions
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Prepare for audits by regulatory bodies or certification efforts
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Set roadmaps for moving from compliance to leadership
For example, a team building an AI hiring tool may use a maturity model to assess if they track bias metrics, if those are reviewed regularly, and whether decisions based on AI outputs are logged and audited.
Real-world examples
One widely used model is COBIT from ISACA, which has been extended for emerging tech. Another is the CMMI model, commonly applied to software governance and increasingly adapted for AI.
In 2023, a large healthcare provider in Canada applied a maturity model to its AI diagnostic tools. They started at the “defined” level—using spreadsheets for model inventory and documentation. Within six months, they moved to the “monitored” stage by integrating a governance dashboard that tracked real-time performance drift and flagged data anomalies.
Best practices for using a maturity model
Using governance maturity models effectively takes planning. Start by picking a model that fits your organization’s goals and regulatory environment. Then build a cross-functional team to assess current practices and agree on what “maturity” looks like for you.
Key best practices include:
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Start simple: Don’t aim for full optimization right away. Focus on getting from ad hoc to defined.
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Use real metrics: Document current gaps with measurable indicators, not just opinions.
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Update regularly: Governance maturity should be reviewed at least annually.
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Involve leadership: Governance is not just for compliance officers. It must be a leadership concern.
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Use external frameworks: Reference ISO/IEC 42001 for AI-specific governance maturity planning.
FAQ
What is the difference between a maturity model and a governance framework?
A maturity model shows how advanced or consistent your governance practices are. A framework, like NIST or ISO, gives you rules and principles to follow. Many organizations use both: the framework to know what to do, and the maturity model to track progress.
Can small organizations benefit from a maturity model?
Yes. Even teams with limited resources can map where they are and decide what to prioritize next. Simple tools like spreadsheets or scorecards can be enough to apply a basic model.
Are there AI-specific maturity models?
Some organizations are creating AI-specific extensions. For example, the AI Risk Management Framework by NIST can be used alongside maturity models to assess risk handling at different levels.
How often should we reassess our maturity?
Ideally every 6 to 12 months. Major changes, like new model deployment, regulatory updates, or incidents, should trigger a quicker review.
How do we communicate maturity level to stakeholders?
Use clear language. Instead of saying “we’re at level 2,” explain what that means—like “our model monitoring is manual and not reviewed weekly.” Visuals or dashboards can also help communicate maturity to non-technical leadership.
Summary
Governance maturity models help teams improve and measure their governance processes over time. They offer a practical way to assess risks, document progress, and set realistic goals, especially in complex areas like AI.
While the models vary, their value lies in guiding better decisions, building trust, and preparing for audits and future growth.
