Supervision of AI: more than rules, embedded in the business

In many organizations, AI immediately tends to think primarily in terms of rules, policies and risks. This reflex is understandable, especially now that the EU AI Act sets tough requirements. But AI oversight goes beyond a legal checklist. It's about embedding in daily practice: in processes, roles and decision-making.

Just as data governance is no longer part of one department, but is intertwined in finance, marketing and operations, AI oversight must also be integrated across the organization. Only then will it work in practice.

Four pillars of business supervision

1. Ownership by the line

Supervision only works when owns the business itself of the AI applications they use. A customer service department that uses an AI assistant is therefore also responsible for the quality, monitoring and incident handling of that assistant. Not IT or Risk. This way, supervision stays close to impact and value.

2. Product cycle supervision

Supervision must be embedded in how you develop and manage products or services. That means:

• Before the start: registration of each AI application in an AI register (purpose, data, owner, risk, supplier).
• Before going live: an impact check in which risks such as discrimination, data use and dependency are explicitly weighed.
• In production: monitoring performance, bias and data drift with automatic alerts.
• In case of incidents: an established process for escalation and correction, just like in the case of security or privacy incidents.

3. Transparency and Accountability

Supervision requires that decisions traceable and explainable are. With an AI model that reviews leads, it must be clear why a lead scores high or low. There should always be an opportunity to overrule a decision. This makes both internal adjustment and external accountability possible.

4. Cooperation in the three lines

Supervision works best in a multi-track model:

• First line (business): owner and executor.
• Second line (risk, legal, security): sets frameworks, advises and tests.
• Third line (audit): independently checks whether agreements are being fulfilled.

Practical example

A customer service department uses an AI assistant to answer customer emails more quickly. Supervision then means that:

• The team leader is the owner and responsible for monitoring and incidents.
• AI performance is part of the weekly KPI review alongside NPS and waiting times.
• WHITE ensures that the tool is technically stable and receives updates.
• Risk periodically tests whether the AI still complies with privacy and data use frameworks.
• Audit later checks whether all agreements and processes have been followed.

This means that supervision is a concrete part of the customer process and not an independent compliance activity.

Why this works

By embedding supervision into the business, you prevent rework and delays afterwards. Teams know exactly what steps to take, decisions are faster, and the organization shows reliability to customers, partners and supervisors. Supervision is therefore not a brake, but a way of allowing innovation to grow in a controlled and scalable way.

Conclusion

Supervision of AI requires more than rules. It requires ownership in the line, anchoring in processes, transparency and cooperation between business, IT and risk. Only then will supervision be created that both limits risks and makes innovation possible.