The AI Governance Maturity Model: What is Your Organization's Level?

The world of AI Governance isn’t as black and white as it might appear at first. There are lots of businesses that think they have an ironclad governance strategy. That might be true for some businesses, but most are missing something. AI governance isn’t either-or. You’re not compliant or non-compliant. You’re operating somewhere along a spectrum, and most of us overestimate where we are. 

So, where do you really stand? 

Below, we’ve provided an AI Governance Maturity Model. The aim is to give you a clear idea of how far along the governance spectrum you are, and give you a clear path to reach the next step.

This is where most businesses start out. At this stage, AI is likely being used sporadically and without any meaningful accountability. 

Teams across the company are adopting AI tools independently. HR might have picked up an AI powered screening software, or maybe the marketing team is experimenting with the newest generative AI tool. 

There could be any number of third-party AI being used throughout the business without any formal structure. Tools are being adopted without proper scrutiny, there’s no central inventory and risk assessments are performed ad hoc (if they’re done at all). 

At this level you’re only having conversations about governance when something goes wrong. No named owners, no defined oversight, and no structured documentation. 

Most businesses never manage to pull themselves out of this level, leaving them playing catch-up forever.

The major change at this level is the recognition that formal AI governance actually matters. This could be brought on by a level 1 business being unable to meet regulatory minimums, or a near miss. Even a client’s own due diligence could be the catalyst for this step. 

Regardless of the reason, there’s now a shift towards more formalised governance practices. Internal policies start to take shape, and somebody has likely been put in charge of compliance efforts alongside their existing role. There’s likely more awareness of the major frameworks, like the EU AI Act. In line with these frameworks there’s probably a step towards proper documentation. 

Level 2 moves away from the chaos of Level 1, but governance is still a reactive process. It’s fragile. Any new AI tool triggers a new review, and internal processes are inconsistent across departments. While there’s formal awareness, the implementation lags behind. 

Many businesses preparing for AI regulation currently fall into this category. They know what they need to do, but they haven’t built the foundation yet.

This is where real governance systems and structure come into play. AI systems are formally identified and categorised. There’s a consistent risk management process, and there are clearly defined roles and responsibilities. 

Reliable, usable documentation becomes part of the process. A Level 3 business keeps track of their data sources, and has clear guidelines on what triggers a human review. 

We’ve moved away from reactivity by adopting proper change management processes for the retraining of models and updates to third-party tools. Governance is integrated into everyday workflows and crisis is something that’s prepared for, instead of a catalyst for change. A Level 3 business might not be actively pursuing certification but will likely partially align with standards like ISO/IEC 42001. 

There’s a formalised structure and reliable governance processes in place that allow a business at this level to operate proactively, but scaling these operations can be a significant challenge, especially when dealing with multiple jurisdictions or a growing organisation.

At this level, AI governance is a fully integrated part of the organisation’s broader risk and compliance structure. 

Regulatory obligations are mapped across jurisdictions. Controls are aligned within a single governance structure that can tackle any of the challenges scale brings, dealing with anything from the EU AI Act, to sector specific challenges and internal policy frameworks. 

Oversight now extends to the executive level. Leadership teams receive reports on AI risk and accountability is allocated to management level staff. Procurement and vendor due diligence include AI risk considerations as standard practice. 

Documentation is companywide and maintained systematically, with periodic reviews, and linked to clearly defined escalation processes. 

Internal audits assess whether controls are operating as intended, and changes to AI systems trigger structured review rather than improvised response. 

At this stage, maturity already supports the adoption of a major framework, so pursuing formal certification becomes a natural next step. 

Governance here in Level 4 can stand up to regulatory scrutiny and client audits, while serving as the foundation for AI related growth.

At this level, governance is driven by intent, rather than regulatory pressure. 

AI oversight is embedded at the design stage, while risk considerations shape expansion strategies and the development, or adoption, of new AI tools. 

Governance is treated as part of long-term competitiveness by leadership and there’s clear visibility of AI risk during executive level meetings. Governance metrics are monitored alongside financial and operational indicators. Accountability is understood as a strategic responsibility. 

Regulatory change doesn’t cause disruption because the underlying management system is already adaptable and documentation is kept up to date as the default. 

Businesses at this level are often certified under standards like ISO/IEC 42001, being able to show a commitment to clarity and sustainable confidence in the deployment of their AI systems. 

Very few businesses operate consistently at this level. 

To operate at this level requires investment, cultural alignment, and executive commitment. Something very few businesses can reach or maintain long term. If governance reaches this level, the use of AI becomes consistently scalable and predictable. A Level 5 business is fully prepared for any emerging laws or regulations and can operate across multiple jurisdictions with ease.

Most businesses don’t need to completely overhaul their entire process, but progression through these levels rarely happens by accident. It requires a formalised governance management system. 

For many organisations, they find that structure through the adoption of standards like ISO/IEC 42001. It provides a framework for building a mature and reliable AIMS (AI management system). A strong AIMS is the backbone of sustainable AI governance that can withstand scrutiny, and consistently meet regulatory requirements. 

If you’re looking to move your business towards sustainable AI governance, check out our AI GRC course catalogue. Our training provides the tools your team needs to successfully navigate the complexities of AI use in the modern world of business.