How Can Your Organization Arrange AI Governance Properly?

It has become increasingly clear that the intersection of artificial intelligence (AI) and governance is pivotal for organizations looking to leverage the power of AI while mitigating associated risks.

The rapid evolution of AI — from narrow, sector-specific tools to general-purpose models that permeate every aspect of business — coupled with stringent regulatory frameworks such as the EU AI Act, makes a structured and comprehensive approach to AI governance not just desirable but essential.

In the same way that companies would never run their finances without a CFO or manage cybersecurity without a CISO, they can no longer afford to deploy AI without a robust governance model that defines responsibilities, controls, and accountability.

This article explores in detail how organizations should design and implement AI governance, covering strategy, stakeholders, compliance with the EU AI Act, risk assessment, technical and organizational controls, and the emerging debate on whether a Chief AI Officer (CAIO) is inevitable.

1. AI Strategy and Core Principles

Effective AI governance begins with a clearly defined strategy. This must come from the very top of the organization: boards and senior executives.

A top-down approach ensures that AI use aligns with the company’s broader vision and values. At its core, an AI governance strategy should rest on a few guiding principles:

• Ethical usage: AI systems must avoid discriminatory outcomes, respect human rights, and serve clear, legitimate purposes.

• Trust and transparency: Users, customers, and regulators need confidence that AI decisions can be explained and, if necessary, challenged.

• Compliance with regulation: Beyond the EU AI Act, organizations must also consider GDPR, NIS2, DORA, sectoral rules, intellectual property laws and global AI regulations.

• Value creation: Governance is not just about risk; it should also ensure that AI is used to enhance competitiveness and innovation.

Legal, risk, and compliance teams then play the role of translating these high-level principles into operational policies, technical controls, and contractual frameworks.

2. Internal Stakeholders and Committees

No single person or function can manage AI governance in isolation. Successful organizations establish and AI governance committee, which brings together cross-functional expertise:

• Legal and compliance

• IT and data science

• Cybersecurity

• Risk management

• HR and ethics officers

This committee typically:

• Approve AI use cases and policies

• Oversee third-party/vendor AI risks

• Monitor compliance with laws and internal standards

• Report regularly to senior management and the board

For many companies, this collective approach is preferable to assigning all responsibility to a single individual, such as a Chief AI Officer. While the CAIO debate is gaining traction, committees ensure that diverse perspectives are captured. However, as AI becomes more embedded, pressure is building for a single C-suite figure to own AI strategy — a topic we will revisit.

3. Mapping AI Use Cases Under EU Rules

A cornerstone of AI governance is knowing what counts as AI in the first place.

The EU AI Act adopts a deliberately broad definition: software that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing the environment. This means that even seemingly benign systems — like chatbots, recommendation engines, or simple scoring models — may fall under regulation.

Practical steps for organizations include:

• Conducting a comprehensive inventory of all AI systems in use (including those purchased from vendors).

• Identifying whether these systems are internally developed, outsourced, or cloud-based.

• Assessing whether they fall within the territorial scope of the EU AI Act, which applies even to non-EU companies if their systems are used in the EU.

Failing to properly classify AI systems can create legal exposure, especially if an organization assumes a system is “low risk” when regulators might label it “high risk.”

4. Risk Identification and Categorization

Once AI systems are mapped, the next step is to categorize them based on regulatory and business risks. The EU AI Act distinguishes between:

• Prohibited AI: systems that manipulate behavior, exploit vulnerabilities, or enable social scoring.

• High-risk AI: systems used in areas such as employment, credit scoring, law enforcement, and critical infrastructure.

• General-purpose AI (GPAI): large models with broad applications, requiring specific governance.

• Minimal risk AI: most consumer-facing applications.

From a corporate governance perspective, this classification exercise is not merely a box-ticking activity. It is about anticipating risks that can materialize in multiple dimensions:

• Legal and regulatory: fines, injunctions, and liability under the AI Act or GDPR.

• Reputational: public backlash if AI is seen as biased or opaque.

• Operational: reliance on AI systems without adequate fallback or human oversight.

• Strategic: deploying AI in areas that could lock the company into risky technologies.

Read on the topic the article “AI Act on Prohibited Practices Is Now in Force – Are You Ready?“.

5. Implementing Controls and Oversight

For each category of AI use case, companies must design controls that mitigate risks and ensure compliance. These include:

• Human oversight: ensuring humans remain “in the loop” for critical decisions.

• Bias and fairness testing: regularly auditing datasets and outputs.

• Transparency: providing clear explanations to users and regulators.

• Security: protecting AI systems from adversarial attacks or data breaches.

• Data governance: ensuring data used for training and operation complies with GDPR and other privacy laws.

Vendor management also becomes critical. Contracts with AI providers should include:

• Obligations to comply with the EU AI Act and other applicable laws.

• Rights to audit or receive audit reports.

• Warranties regarding data quality, bias mitigation, and transparency.

6. The Role of the Chief AI Officer

This brings us to one of the most debated topics in AI governance: should organizations appoint a Chief AI Officer (CAIO)?

The case for a CAIO is strong:

• Just as CFOs own financial integrity, a CAIO could own AI integrity.

• Boards increasingly want a single accountable executive to brief them on AI risks and opportunities.

• Regulators may soon expect a clear line of accountability.

A CAIO would be responsible for:

• Defining the AI governance framework.

• Integrating AI strategy with business, legal, and compliance.

• Representing the company in regulatory interactions.

• Overseeing incident response and crisis communication when AI goes wrong.

Critics argue that AI is too cross-cutting to be owned by one person. But the same was once said about cybersecurity before the role of the CISO became standard. History suggests that as risks mature, so does the demand for a dedicated C-suite leader.

7. AI Risk and Compliance Assessment Frameworks

To operationalize governance, organizations should adopt formal AI risk and compliance assessment frameworks. These typically involve:

• Initial screening: Does the system qualify as AI under the EU AI Act?

• Impact assessment: What is the potential harm to individuals, business, and society?

• Risk mitigation: Which controls are necessary (technical, legal, organizational)?

• Ongoing monitoring: How will the system be updated and audited?

Such frameworks are not theoretical. Regulators already expect AI impact assessments similar to GDPR’s Data Protection Impact Assessments (DPIAs) and in some cases a FRIA is needed (Read on the topic the article “What is the Fundamental Rights Impact Assessment (FRIA) under the AI Act?“).

8. Human Oversight and Accountability

One of the most contentious issues in AI governance is how to ensure meaningful human oversight.

Too often, oversight is reduced to a human clicking “approve” on an AI decision. True oversight requires:

• Competence: humans must be trained to understand AI limitations.

• Authority: they must have the power to override or suspend systems.

• Resources: oversight should not be symbolic but supported by tools and processes.

In governance terms, this translates into clear accountability: documenting who is responsible for each AI system and ensuring they have the mandate to act.

9. Audits and Continuous Monitoring

The EU AI Act introduces requirements for conformity assessments for high-risk AI. Beyond compliance, organizations should embrace regular audits to build trust with customers and regulators.

Possible practices include:

• Independent third-party audits of algorithms and datasets.

• Continuous monitoring of AI systems post-deployment to detect “model drift.”

AI governance is not a one-off exercise; it must be a living process that adapts as systems evolve and regulations change.

10. AI Literacy and Cultural Change

A frequently overlooked pillar of AI governance is education. The EU AI Act itself introduces obligations on AI literacy, requiring organizations to train employees who interact with AI.

AI governance will fail if only lawyers and data scientists understand it. Every employee who uses AI must have basic literacy in:

• The limits of AI systems

• How to escalate issues or errors

• Ethical use of AI in their daily role

Embedding AI governance into corporate culture is as important as having committees and frameworks. On the topic, you can read the article “AI Act Literacy: The European Commission’s Q&As Raise the Bar Beyond Simple Training“.

11. AI Governance and ESG

AI governance does not exist in a vacuum; it intersects with environmental, social, and governance (ESG) obligations.

• Environmental: AI systems, especially large models, consume vast amounts of energy. Governance should include sustainability metrics.

• Social: Ensuring fairness, accessibility, and avoidance of bias.

• Governance: Transparency, accountability, and board oversight.

Investors are increasingly scrutinizing how companies govern AI as part of ESG due diligence. Strong AI governance can therefore enhance capital access and reputation.

12. Incident Response and Crisis Management

No governance framework is complete without a plan for when things go wrong.

Key elements of AI incident response include:

• Clear escalation protocols when an AI system fails or produces harmful outputs.

• Communication strategies for regulators, customers, and the public.

• Root cause analysis and remediation.

The companies that handle AI crises transparently and responsibly will be the ones that preserve trust.

AI Governance as a Strategic Imperative

The organizations that invest in solid AI governance stand to gain the most from AI’s capabilities, enjoying a measurable return on investment while avoiding legal and reputational pitfalls.

For those that delay, the risks are stark: regulatory fines, personal liability for directors, public backlash, and the loss of competitive trust.

AI governance is not a differentiator but a baseline expectation, just like financial compliance or data protection. The only real question is whether organizations will proactively build governance now, or wait until a crisis forces their hand.

As with every transformative technology, leadership matters. And AI governance is the leadership test of our time.

On the topic, you can read the articles available HERE and the presentation of our AI Act compliance tool available HERE.