The EU AI Act is the first major attempt by a government body to regulate artificial intelligence in a general-purpose, risk-based framework. This law doesn’t just aim to control how AI is built and deployed—it starts at the root by deciding what AI is. If a system isn’t considered AI under this Act, it won’t be subject to its regulations.
That might seem like a simple task. But defining AI is more complicated than it looks. Technologies evolve rapidly, and what might look like a basic software tool today could integrate learning and decision-making functions tomorrow. That’s why the European Commission issued detailed guidance to clarify exactly what qualifies as an AI system under the AI Act. Anyone developing, deploying, or regulating digital technologies in the EU—or doing business with European markets—must understand this definition.
This blog explains what the EU considers artificial intelligence, how it interprets the definition, what it includes and excludes, and what the implications are for developers and organizations across industries.
Defining AI Systems Under the EU AI Act
The central definition provided in Article 3(1) of the AI Act frames an AI system as:
“A machine-based system 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 is functional and technology-agnostic. Instead of naming specific techniques or platforms, it identifies the features and capabilities that a system must exhibit to fall under AI regulation. That approach helps the law remain applicable even as specific AI methods evolve.
The components of this definition reflect not just how these systems work but also their purpose. AI systems are not just static tools; they are dynamic, learning-oriented technologies. They process data in complex ways to solve problems, produce outcomes, or support human decision-making. Unlike traditional software, which runs on fixed instructions, AI systems are expected to evolve in how they generate outputs, especially after they’ve been deployed. This can include changes due to continued learning, feedback loops, or interactions with new data sets.
Key Components of the EU AI Act AI System Definition
According to the EU Commission, an AI system is defined as a machine-based system designed to operate with varying levels of autonomy. The key components include:
Machine-Based Systems
The first requirement is that the system must be machine-based. This might sound obvious, but it’s important to separate AI from purely human-driven processes. A machine-based system can include anything running on digital computing infrastructure—servers, embedded devices, or cloud-based platforms. It’s not just about the presence of a computer; it’s about the system being operated through programmed logic, capable of handling information in structured or unstructured formats.
The emphasis here is on systems that depend on coded algorithms and mechanical logic, either executing pre-defined functions or learning from the data. These systems may use machine learning, symbolic AI, or even statistical pattern recognition, as long as they operate through digital processing mechanisms. Systems that only assist users with data entry or basic computing are not included unless they meet further criteria.
Autonomy
A major hallmark of an AI system under this law is its ability to operate with autonomy. This doesn’t mean the system must act entirely on its own without human supervision. Instead, it must be capable of making decisions or producing results without needing real-time human input for every action it takes.
For example, a recommendation engine on a shopping platform that adapts based on browsing history and makes product suggestions automatically is autonomous. A calculator, which only computes values as input by a human user, is not. This threshold ensures that systems with genuine functional independence—however limited—are covered by the AI Act. The more autonomy a system has, the greater the potential risk, and therefore the more likely it is to be subject to stricter rules within the Act.
Adaptiveness
An adaptive system changes its internal behavior or structure based on how it performs or interacts with the environment. This is one of the key differences between AI and traditional software. Traditional software always behaves in the same way unless explicitly reprogrammed. AI systems, by contrast, might refine their responses or improve accuracy based on new data without human intervention.
For instance, a fraud detection tool that improves its risk assessment through new transaction patterns is adaptive. It doesn’t require a programmer to tell it how to adjust each rule—it adjusts by identifying signals in the data it processes. This criterion excludes hard-coded systems that respond predictably and do not change their structure, even if they use complex rules. Adaptiveness gives a system the ability to evolve, which also introduces more unpredictability and, potentially, more risk.
Objective-Oriented Operation
The Act specifies that AI systems work toward objectives, whether they are clearly programmed or inferred. The inclusion of both “explicit” and “implicit” goals allows the law to include a wide range of systems. Explicit goals are those clearly defined by the developer—like optimizing delivery routes or recommending the next movie to watch. Implicit goals may emerge from the training data or system behavior—such as improving user engagement or predicting user intent.
This focus on goal-driven operation separates AI from basic tools that process data passively. A spreadsheet application that calculates formulas has no objective beyond what a user gives it. An AI tool, however, might suggest what cells to review or flag anomalies without being prompted to do so. Systems that demonstrate purpose and direction in their actions—especially when they are optimized for outcomes—meet this test of objective orientation.
Inference Capability
One of the most critical elements in this definition is inference. AI systems aren’t just calculating—they’re interpreting. They analyze input data and generate outcomes that were not explicitly programmed line-by-line. Inference is what allows an AI system to recognize speech, recommend a product, identify faces, or predict maintenance needs in industrial equipment. It’s the process of turning raw data into informed action.
This capability distinguishes AI from rigid, rule-based tools. An Excel macro follows instructions exactly; it doesn’t draw conclusions. An AI model trained to identify sentiment in social media posts is generating insights through inference, not simple execution. The emphasis on inference helps ensure that systems which could introduce uncertainty or affect decision-making are brought into the regulatory fold.
Influence on Physical or Virtual Environments
To fall under the AI Act, a system must affect its environment—either physically, virtually, or both. This includes changes made directly by the system, like adjusting thermostat settings, or indirectly, like providing input that leads to decisions.
For example, an AI-based hiring tool that scores candidates can influence hiring decisions, even if humans make the final call. A self-driving car’s steering algorithm influences physical movement. Both fall under this definition. This criterion filters out systems that might analyze data internally but have no real-world output or consequence. It’s a practical check to ensure that only systems with the potential to affect users or systems are regulated.
Exclusions from the AI System Definition
The EU AI Act doesn’t include every digital tool under its AI umbrella. Systems that rely solely on deterministic logic or simple statistical functions are excluded if they lack inference or adaptive behavior.
For example, an insurance company might use a basic scoring system for applications that assigns risk based on pre-set criteria. A system that can’t learn, adapt, or infer beyond set rules is not considered an AI system. This prevents overregulation of older systems that can’t adapt, learn, or behave unpredictably. Calculators, static dashboards, or forms that just show data likely don’t qualify as AI, despite their complexity.
Real-World Examples of AI Systems
To understand how this definition works in practice, consider the following examples:
- A customer service chatbot trained on historical conversation data and able to improve responses through user feedback qualifies as AI.
- A factory control system that adjusts machine settings based on predictive maintenance models built from past performance data also fits.
- An online learning platform that customizes quizzes and content based on student performance and engagement metrics meets the criteria.
These examples reflect the mix of autonomy, adaptiveness, inference, and goal-driven outputs that bring systems under the AI Act’s regulation.
EU AI Act Implications for Developers and Businesses
Understanding what counts as AI under the EU AI Act is not just theoretical. It determines whether businesses must meet certain legal obligations. Systems that fall under the AI label may be classified into risk tiers: unacceptable risk (banned), high-risk (heavily regulated), limited risk (subject to transparency), or minimal risk (unregulated). High-risk AI systems must comply with strict requirements on data governance, transparency, human oversight, and accuracy. Failing to meet these rules can result in heavy penalties.
For developers, this means ensuring their product documentation and internal practices reflect how their systems operate. For businesses deploying AI solutions, it means vetting vendors and verifying compliance. Legal liability can extend to both creators and users. Understanding the threshold of what constitutes AI is the first and most important step in staying compliant under this evolving legal framework.
Stay Compliant with Confidence
The EU AI Act presents a comprehensive approach to identifying and managing artificial intelligence technologies. By offering a clear and functional definition of what counts as AI, the regulation ensures that only those systems with the potential for real-world impact, adaptive behavior, and goal-driven output are subject to legal oversight.
If you are unsure whether your technology falls under the EU AI Act, or if you need help preparing your systems for compliance, reach out to Stevens Law Group. Their experienced legal team is already helping technology companies navigate the legal requirements of AI regulation.
Reference:
European Commission – AI Act
European Commission – European approach to artificial intelligence
Europian Union – Artificial Intelligence for Europe
Leave a Reply