AI Act AI system definition and insurance

The Commission published Guidelines on the definition of an AI system to facilitate the application of the first AI Act’s rules. This is also important for the insurance sector to better understand what falls within and outside the scope of the AI Act.

The definition of an AI system entered into application on 2 February 2025, together with other provisions set out in Chapters I and II of the AI Act, notably Article 5 on prohibited AI practices.

As the definition of an AI system is crucial for understanding the scope of the AI Act, including the prohibited practices, the Guidelines are adopted in parallel with the Commission’s guidelines on prohibited artificial intelligence practices.

These Guidelines take into account the outcome of a stakeholder consultation and the consultation of the European Artificial Intelligence Board.

Article 3(1) of the AI Act defines an AI system as follows:

“‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”

This definition comprises seven main elements:

1. A machine-based system;
2. Designed to operate with varying levels of autonomy;
3. That may exhibit adaptiveness after deployment;
4. That, for explicit or implicit objectives,
5. Infers, from the input it receives, how to generate outputs,
6. Such as predictions, content, recommendations, or decisions,
7. That can influence physical or virtual environments.

All these aspects are clarified in the Guidelines.

Additionally, the Guidelines also cover systems that fall outside the scope of the AI system definition. Recital 12 explains that the AI system definition should distinguish AI systems from “simpler traditional software systems or programming approaches and should not cover systems that are based on the rules defined solely by natural persons to automatically execute operations.”

Some systems have a limited capacity to infer but may nevertheless fall outside the scope of the AI system definition due to their restricted ability to analyze patterns and autonomously adjust their output. Such systems may include those used to improve mathematical optimization or to accelerate and approximate traditional, well-established optimization methods, such as linear or logistic regression.

While these models have the capacity to infer, they do not transcend ‘basic data processing.’ An indication that a system does not transcend basic data processing is that it has been used in a consolidated manner for many years.

This includes, for example, machine learning-based models that approximate functions or parameters in optimization problems while maintaining performance.

These systems aim to improve the efficiency of optimization algorithms used in computational problems by speeding up optimization tasks through learned approximations, heuristics, or search strategies.