Responsibility for AI governance does not rest with a single person but is distributed across multiple roles within an organization. In practice, ultimate responsibility often lies with management or a designated AI lead, while implementation and oversight are shared by IT, legal, compliance, and operational teams. AI-driven intelligence requires a structured approach in which everyone knows their role. This article answers the most frequently asked questions about AI governance, from definition to implementation.
What does AI governance mean in practice?
AI governance refers to the set of policies, processes, roles, and responsibilities through which an organization manages, monitors, and accounts for the use of artificial intelligence. In practice, this involves specific agreements regarding who is authorized to deploy AI systems, how risks are assessed, how decisions are documented, and how compliance with laws and regulations is ensured.
Governance goes beyond a manual or internal regulations. It also encompasses day-to-day practices: Who approves a new AI model before it goes into production? Who is responsible if an AI system makes a mistake? How are employees trained in the responsible use of AI tools?
In practical terms, AI governance in 2026 also means taking into account the EU AI Act, the world’s first comprehensive AI regulation. This law, published on July 12, 2024, and phased in gradually through 2031, requires organizations to classify AI systems by risk level and implement appropriate control measures accordingly. High-risk systems require extensive documentation, human oversight, and compliance assessments. Prohibited applications, such as manipulative AI techniques or social scoring by governments, have been banned since February 2, 2025.
Practical AI governance consists of at least the following elements:
- An inventory of all AI systems used or developed within the organization
- A risk classification for each system based on the EU AI Act or an internal risk model
- Clear ownership for each system, including a designated person or team responsible for it
- Documentation of decisions, training dates, and intended uses
- A process for reporting and handling incidents
- Periodic evaluation and adjustment of the governance framework
Who are the key roles in AI governance?
Within a well-functioning AI governance structure, at least four roles are essential: a senior executive with ultimate responsibility, an AI coordinator or Chief AI Officer, a legal or compliance officer, and the operational owners of individual AI systems. These roles may be combined in smaller organizations, but the responsibilities must not be left undefined.
Strategic and operational roles
At the executive level, management bears ultimate responsibility for AI policy. This means that they establish frameworks, approve budgets, and the organization is accountable to regulatory authorities. In larger organizations, this task is often supported by a Chief AI Officer or an AI Governance Board, which translates policy into action.
The operational owners are the managers or team leaders who work with AI systems on a daily basis. They identify anomalies, report incidents, and ensure that employees use the systems correctly. They are also the first point of contact when problems arise in practice.
Legal and Technical Roles
Legal and compliance staff monitor compliance with laws and regulations, including the EU AI Act and the GDPR. They assess whether new AI applications meet applicable requirements and provide advice on risk management.
IT and data teams are responsible for the technical implementation of governance measures, such as access control, logging, model version control, and technical documentation. Without their involvement, governance remains nothing more than a policy on paper.
What is the difference between AI governance and AI compliance?
AI compliance is a component of AI governance, but it is not the same thing. Compliance focuses on adhering to external laws and regulations, such as the EU AI Act or sector-specific standards. AI governance is broader: it also encompasses internal policy decisions, ethical principles, risk appetite, and the way in which an organization intends to use AI responsibly, even in areas where the law does not impose specific requirements.
A practical example illustrates the difference. The EU AI Act requires providers of high-risk AI systems to maintain technical documentation and ensure human oversight. That is compliance. But an organization may also decide never to use AI for employee evaluations, even if the law does not prohibit it. That is a governance choice based on the organization’s own values and risk appetite.
Compliance is reactive by nature: you respond to what the law requires of you. Governance is proactive: you decide for yourself how you want to use AI and what limits to set, before a regulator does it for you. Organizations that focus solely on compliance miss the opportunity to build trust with customers, employees, and partners.
How do you set up an AI governance structure within your organization?
Setting up an AI governance structure starts with identifying all the AI systems you’re already using, followed by assigning ownership, drafting policies, and establishing oversight and reporting processes. You don’t have to do everything at once, but you do need a concrete starting point.
A proven approach consists of the following steps:
- Take stock: Identify which AI systems and tools are used within the organization, including purchased software with AI functionality.
- Classify: Assess each system’s risk level based on the EU AI Act categories: prohibited, high-risk, limited-risk, or minimal-risk.
- Assign ownership: Determine who is responsible for the use, documentation, and incident reporting for each system.
- Develop a policy: Establish internal guidelines for evaluating, approving, and monitoring AI applications.
- Train employees: Ensure AI literacy at all levels. The EU AI Act explicitly requires this under Article 4, which takes effect on February 2, 2025.
- Establish oversight: Determine how and how often you will evaluate and adjust the governance framework.
Start small if the organization does not yet have a formal governance structure. A working group with representatives from IT, legal, and operational teams can already accomplish a great deal without setting up a large-scale program.
What risks arise in the absence of clear AI governance?
Without AI governance, your organization faces legal, operational, and reputational risks. The EU AI Act imposes fines of up to 35 million euros or 7% of global annual revenue for violations of the most serious prohibitions. But the risks go beyond fines: uncontrolled use of AI can also result in unintended harm to customers, employees, or society.
Specific risks associated with a lack of governance include:
- Legal liability: Using high-risk AI without the required documentation or conformity assessment results in non-compliance under the EU AI Act, subject to corresponding fines.
- Data privacy violations: AI systems that process personal data without adequate safeguards violate the GDPR.
- Operational errors: Without supervision, AI systems can make decisions based on outdated or incorrect data, resulting in harm to customers or processes.
- Reputational damage: Unintentionally discriminatory or manipulative AI outputs can seriously damage customer trust and public opinion.
- Loss of control: Without ownership and documentation, no one knows exactly which AI systems are active, what they do, and who is responsible.
National market surveillance authorities are responsible for enforcing the EU AI Act with regard to high-risk AI. In January 2026, Finland became the first member state to formally grant enforcement powers to its authority. The Netherlands will follow suit, meaning that oversight is becoming increasingly concrete.
When will your organization be ready for formal AI governance?
Your organization is ready for formal AI governance as soon as you use AI systems that affect customers, employees, or business processes, and as soon as more than one person is involved with those systems. That is already the case for most organizations. Waiting for the “right moment” only increases the risks.
There are, however, signs that indicate that formalization is urgent:
- You use AI tools that no one knows exactly how they work or who manages them
- There are no internal guidelines for approving new AI applications
- Employees use AI tools on their own initiative without central coordination
- There is no process for reporting AI-related errors or incidents
- You operate in sectors that the EU AI Act classifies as high-risk, such as government, education, or critical infrastructure
Formal governance doesn’t have to be complicated. A clear policy document, a designated person in charge, and an annual review already provide a solid foundation. Build on that as the use of AI within the organization grows.
How Pegamento Helps with AI Governance
We understand that for many organizations, AI governance feels like a complex issue where it’s hard to know where to start. Drawing on our experience with Agentic AI for customer service, we help organizations deploy AI in a responsible and controlled manner. Agentic AI represents the evolution from task-oriented bots to self-thinking assistants that not only follow instructions but also take the initiative and act independently. It is precisely this autonomy that requires clear governance.
What we can do for you:
- Understanding Your Current Situation: We help you identify which AI systems you’re already using and the associated governance risks
- Practical solutions without unnecessary complexity: No costly custom development, but a smart combination of proven modules tailored to your organization’s size and industry
- Everything under one roof: From consulting and implementation to management and support, without having to coordinate multiple vendors
- Compliance-focused solutions: Our approach takes into account the EU AI Act and related laws and regulations, supported by our ISO 27001, ISO 9001, and ISO 26000 certifications
- Human-centered technology: We strengthen human connections rather than replace them, making governance simpler and more transparent
Would you like to know where your organization currently stands in terms of AI governance? Contact us, and we’d be happy to discuss this with you—with no obligation.
Frequently Asked Questions
How does AI governance for small organizations differ from that of large companies?
Small organizations do not need to set up an extensive governance structure to operate in a compliant and responsible manner. A basic document that includes an AI inventory, a single designated person in charge, and a simple approval process for new tools already provides a strong foundation. The difference compared to large organizations lies mainly in scale: while a multinational may need an AI Governance Board and a Chief AI Officer, an SME can assign these tasks to an existing IT or compliance staff member. It is essential that these responsibilities are assigned, regardless of the organization’s size.
What should I do if an employee has started using an AI tool without authorization?
Start by assessing the situation: which tool is it, what data is being processed, and what are the risks? Use this as an opportunity to establish clear internal guidelines for approving AI tools, so employees know what process to follow. Don’t react punitively; instead, view it as a sign that there is a need for AI literacy and clear communication about what is and isn’t permitted. This type of ‘shadow AI’ is one of the biggest governance blind spots in many organizations.
How do I know if an AI system I’m purchasing from a supplier complies with the EU AI Act?
Ask your supplier explicitly about the system’s risk category under the EU AI Act and whether they, as the provider, can provide the required conformity documentation. For high-risk systems, providers are required to maintain a technical file and conduct a conformity assessment. However, as a user, you are also responsible for ensuring proper use within the established frameworks, so make sure that contractual agreements regarding the division of responsibilities are clearly defined.
How often should an AI governance framework be reviewed?
A minimum review cycle of once a year is a good starting point for most organizations, but in practice, the governance framework deserves more frequent attention. You should also review the framework when introducing a new AI system, when making significant changes to existing systems, in the event of an incident, or when new laws or regulations come into effect. Given the phased implementation of the EU AI Act through 2031, it is wise to check each year which new obligations have come into effect.
What is the biggest misconception about AI governance that costs organizations dearly?
The most common misconception is that AI governance is a one-time project rather than an ongoing process. Organizations draft a policy document, then stop there, thinking they’re done—all while AI systems continue to evolve, new tools are introduced, and legislation changes. A second costly misconception is that governance is only relevant for organizations that develop AI themselves: even organizations that use only off-the-shelf AI software have governance obligations and face risks if the software is used without oversight.
How do I involve employees in AI governance without creating resistance?
Don’t communicate governance as a control mechanism, but as a way to protect employees and provide clarity on what they are allowed to do with AI. Involve representatives from operational teams early in the process so that policies align with daily practice rather than being at odds with it. Practical AI training tailored to their specific work context is more effective than abstract compliance sessions. Furthermore, Article 4 of the EU AI Act mandates AI literacy at all levels, which provides a concrete reason to address this in a structured manner.
Can I reuse existing compliance processes, such as those for the GDPR, for AI governance?
Yes, and that is also recommended. Processes such as risk assessments, incident reporting, and processing records that you’ve already set up for the GDPR provide a solid foundation for AI governance. AI systems that process personal data fall under both frameworks simultaneously, so integration saves you from duplicating efforts. Keep in mind, however, that the EU AI Act imposes additional requirements that go beyond the GDPR, such as technical documentation of models, human oversight, and conformity assessments for high-risk systems. Use the GDPR framework as a foundation and build the AI-specific layers on top of it.
