Commission guidance on the AI system definition

Article 3(1) of the EU AI Act (Regulation (EU) 2024/1689, with the changes of Regulation (EU) 2026/1744) gives the definition of an AI system. The Commission gives guidance on this definition in its Guidelines on the definition of an AI system, Communication “C(2025) 5053 final” of 29 July 2025. These Guidelines are not binding (paragraph (7)).

For the publication and adoption dates of each Commission guidance text, see Commission guidance.

Scope of the AI Act

Article 3(1) of the EU AI Act gives the test for an AI system. In paragraph (2) of the Commission Guidelines, the AI Act applies “only to those systems that fulfil the definition of an ‘AI system’”.

An AI system is not always in the scope of the AI Act. Article 2 gives the persons to which the AI Act applies, for example providers and deployers, and the conditions on their location. It also gives the systems and models to which the AI Act does not apply. Thus, examine Article 2 and Article 3(1) first. Then examine each obligation with its conditions, exceptions, and application date.

Article 3(1) and the initial prohibitions in Article 5 of the EU AI Act apply from 2 February 2025 (Article 113, point (a)). For all application dates, see Application dates.

For Article 6(1) high-risk AI systems related to Annex I Section B products, Article 2(2) excludes Articles 4, 5, and 50.

Article 2 of the EU AI Act also contains exclusions from its scope. Three of these exclusions are important when you examine the AI-system definition:

• “Military, defence or national security purposes” (Article 2(3)). “This Regulation does not apply to areas outside the scope of Union law”, and it “shall not, in any event, affect the competences of the Member States concerning national security”. It does not apply to AI systems “where and in so far they are placed on the market, put into service, or used with or without modification exclusively for military, defence or national security purposes, regardless of the type of entity carrying out those activities”. It also does not apply to AI systems “which are not placed on the market or put into service in the Union, where the output is used in the Union exclusively for military, defence or national security purposes”.
• Scientific research and development (Article 2(6)). The AI Act does not apply to “AI systems or AI models, including their output, specifically developed and put into service for the sole purpose of scientific research and development”. The exclusion continues to apply when such an AI system is put into service only for scientific research and development. The Commission guidelines on Article 50 give this guidance: “if those systems are put into service or their outputs are being used for other purposes than for the sole purpose of scientific research and development, the relevant transparency obligations in Article 50 AI Act would still need to be complied with” (paragraph (21)). See also Commission guidance on transparency.
• Before placing on the market (Article 2(8)). The AI Act does not apply to “any research, testing or development activity regarding AI systems or AI models prior to their being placed on the market or put into service”. “Such activities shall be conducted in accordance with applicable Union law.” The exclusion does not apply to testing in real-world conditions. The Commission guidelines on Article 50 give this guidance: “once an AI system is placed on the market or put into service as a result of such research and testing activities, it should nonetheless comply with the relevant transparency obligations under Article 50 AI Act” (paragraph (22)).

Article 2 contains more rules on scope. For the adoption of the Commission guidelines on Article 50 and when they apply, see Commission guidance.

The seven elements of Article 3(1)

Article 3(1) of the EU AI Act gives this definition:

‘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;

The Commission Guidelines divide this definition into seven elements (paragraph (9)):

1. “a machine-based system”
2. “designed to operate with varying levels of autonomy”
3. “may exhibit adaptiveness after deployment”
4. “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”

The Commission Guidelines divide the lifecycle of a system into the “pre-deployment or ‘building’ phase” and the “post-deployment or ‘use’ phase” (paragraph (10)). The seven elements “are not required to be present continuously throughout both phases of that lifecycle”. An element can occur in one of these two parts only. Thus, examine the elements across the full lifecycle of the system.

Element 1 — Machine-based system (paragraphs (11)–(13))

The Commission Guidelines give this guidance: “The term ‘machine’ can be understood to include both the hardware and software components that enable the AI system to function.” (paragraph (11)). Hardware components include “processing units, memory, storage devices, networking units, and input/output interfaces”. Software components include “computer code, instructions, programs, operating systems, and applications”. Paragraph (12) adds this condition: AI systems “must be computationally driven and based on machine operations”.

In paragraph (13), “The term ‘machine-based’ covers a wide variety of computational systems.” The Guidelines give two examples of machine-based systems: “the currently most advanced emerging quantum computing systems”, and “biological or organic systems so long as they provide computational capacity”.

Element 2 — Designed to operate with varying levels of autonomy (paragraphs (14)–(21))

In recital 12 of the EU AI Act, AI systems with “varying levels of autonomy” are “designed to operate with ‘some degree of independence of actions from human involvement and of capabilities to operate without human intervention’” (Commission Guidelines, paragraph (14)). In the Commission Guidelines, this recital “excludes systems that are designed to operate solely with full manual human involvement and intervention” (paragraph (17)). The Guidelines add: “Human involvement and human intervention can be either direct, e.g. through manual controls, or indirect, e.g. though automated systems-based controls which allow humans to delegate or supervise system operations.”

An AI system can have autonomy when a person gives the input. In the example of paragraph (18), a system that “requires manually provided inputs to generate an output by itself” has “some degree of independence of action”. The cause is that the system is “designed with the capability to generate an output without this output being manually controlled, or explicitly and exactly specified by a human”. Paragraph (20) gives the result: “All systems that are designed to operate with some reasonable degree of independence of actions fulfil the condition of autonomy in the definition of an AI system.”

Element 3 — “May exhibit adaptiveness after deployment” (paragraphs (22)–(23))

In recital 12 of the EU AI Act, adaptiveness “refers to self-learning capabilities, allowing the behaviour of the system to change while in use” (Commission Guidelines, paragraph (22)). In Article 3(1), the system “may exhibit adaptiveness after deployment”. In the Commission Guidelines, this shows that a system “may, but does not necessarily have to, possess adaptiveness or self-learning capabilities after deployment to constitute an AI system” (paragraph (23)).

Thus, adaptiveness is not a necessary condition. A system that does not change its behavior after deployment can be an AI system if the other elements apply to it. For example, an AI system can use a model with weights that do not change in use. The definition applies to the system, not to the model only (see AI systems and GPAI models).

Element 4 — For explicit or implicit objectives (paragraphs (24)–(25))

In the Commission Guidelines, an AI system operates for one or more objectives (paragraph (24)). Explicit objectives are “clearly stated goals that are directly encoded by the developer into the system”. An example is “the optimisation of some cost function, a probability, or a cumulative reward”. Implicit objectives are “goals that are not explicitly stated but may be deduced from the behaviour or underlying assumptions of the system”. These objectives “may arise from the training data or from the interaction of the AI system with its environment”.

The objectives are different from the intended purpose (paragraph (25)). The objectives “are internal to the system, referring to the goals of the tasks to be performed and their results”. In Article 3(12), the intended purpose is “the use for which an AI system is intended by the provider”. The intended purpose “is externally oriented and includes the context in which the system is designed to be deployed and how it must be operated”.

Element 5 — Inference, from input it receives, “how to generate outputs” (paragraph (26)+)

In the Commission Guidelines, the capacity to infer is “a key, indispensable condition that distinguishes AI systems from other types of systems” (paragraph (26)). Recital 12 of the EU AI Act gives a limit: the definition “should not cover systems that are based on the rules defined solely by natural persons to automatically execute operations”. Inference is about the procedure to get the outputs, and about a capacity “to derive models or algorithms, or both, from inputs or data” (paragraph (27)). The text “infers, how to generate outputs” is also about the “building phase” of the system (paragraph (29)).

The Commission Guidelines give two groups of AI techniques that let a system infer (paragraphs (30)–(31)):

• Machine learning techniques that “learn from data how to achieve certain objectives”. Examples are supervised learning, unsupervised learning, self-supervised learning, and reinforcement learning (paragraphs (32)–(37)). Deep learning is “a subset of machine learning” (paragraph (38)).
• Logic-based and knowledge-based techniques that “infer from encoded knowledge or symbolic representation of the task to be solved” (paragraph (39)). These systems “learn from knowledge including rules, facts and relationships encoded by human experts”. They apply “formal logic, predefined rules or ontologies to new situations”. Examples are “knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning, expert systems and search and optimisation methods”.

Thus, a system can be an AI system without machine learning. Inference from rules “encoded by human experts” is an AI technique (paragraph (39)). But a system that only applies “rules defined solely by natural persons to automatically execute operations” does not infer (paragraphs (40) and (46)).

Element 6 — Outputs (predictions, content, recommendations, decisions) (paragraph (52)+)

Article 3(1) of the EU AI Act gives examples of outputs: “such as predictions, content, recommendations, or decisions”. The Commission Guidelines put the outputs of AI systems into “four broad categories” (paragraph (53)). “Each category differs in its level of human involvement.”

• Predictions: “an estimate about an unknown value (the output) from known values supplied to the system (the input)” (paragraph (54)).
• Content: “the generation of new material by an AI system”, for example “text, images, videos, music and other forms of output” (paragraph (56)). In the Guidelines, recital 12 lists content as “a separate category of output” because of “the prevalence of this output in generative AI systems”.
• Recommendations: “suggestions for specific actions, products, or services to users based on their preferences, behaviours, or other data inputs” (paragraph (57)). “If these recommendations are automatically applied, they become decisions.”
• Decisions: “conclusions or choices made by a system” (paragraph (58)). Such a system “implies a fully automated process whereby a certain outcome is produced in the environment surrounding the system without any human intervention”.

Element 7 — Can influence physical or virtual environments (paragraph (60)+)

In Article 3(1) of the EU AI Act, the outputs “can influence physical or virtual environments”. Thus, this element is about the capacity of the outputs to influence an environment. In the Commission Guidelines, AI systems “are not passive, but actively impact the environments in which they are deployed” (paragraph (60)). The influence can be on physical objects. It can also be on “virtual environments, including digital spaces, data flows, and software ecosystems”.

Systems that are not AI systems

The Commission Guidelines on the EU AI Act give four groups of systems that are possibly not AI systems (paragraphs (40)–(51)). Some of these systems “have the capacity to infer in a narrow manner”. But they can be “outside of the scope of the AI system definition because of their limited capacity to analyse patterns and adjust autonomously their output” (paragraph (41)).

These groups are examples with conditions. They do not remove a technique from the definition in all uses. The Guidelines give this guidance: “each system must be assessed based on its specific characteristics” (paragraph (6)). In paragraph (61), the decision “should be based on the specific architecture and functionality of a given system and should take into consideration the seven elements of the definition”. Paragraph (62) adds: “No automatic determination or exhaustive lists of systems that either fall within or outside the definition of an AI system are possible.”

1. Optimization systems

The Commission Guidelines give this group in paragraph (42): systems “used to improve mathematical optimisation or to accelerate and approximate traditional, well established optimisation methods, such as linear or logistic regression methods”. The Guidelines give the cause: “while those models have the capacity to infer, they do not transcend ‘basic data processing’”. One possible indication is that a system “has been used in consolidated manner for many years”. The group includes “machine learning-based models that approximate functions or parameters in optimization problems while maintaining performance”.

The Commission Guidelines give these examples:

• “physics-based systems may use machine learning techniques to improve computational performance, accelerating traditional physics-based simulations or estimating parameters, that are then fed into the established physics models” (paragraph (43)). In this example, the models “approximate complex atmospheric processes, such as cloud microphysics or turbulence, enabling faster and more computationally efficient forecasts”.
• A “satellite telecommunication system to optimize bandwidth allocation and resource management” (paragraph (44)). Machine learning models predict “network traffic and optimize the allocation of resources like power and bandwidth to satellite transponders, having similar performance to established methods in the field”.

These systems can make automatic adjustments. But in the Guidelines, these adjustments are “addressed at optimising the functioning of the systems by improving its computational performance rather than, for example, at permitting adjustments of their decision making models in an intelligent way”. Paragraph (45) then gives this result: “Under these conditions they may be excluded from the AI system definition.” Thus, a system can use linear or logistic regression and be an AI system. The group applies only on the conditions of paragraphs (42) to (45).

2. Basic data processing (paragraphs (46)–(47))

In the Commission Guidelines, a basic data processing system “follows predefined, explicit instructions or operations” (paragraph (46)). It does tasks “based on manual inputs or rules, without any ‘learning, reasoning or modelling’ at any stage of the system lifecycle”. It operates “based on fixed human-programmed rules, without using AI techniques, such as machine learning or logic-based inference, to generate outputs”. The Guidelines give these examples:

• “database management systems used to sort or filter data based on specific criteria”, for example “find all customers who purchased a specific product in the last month”
• “standard spreadsheet software applications which do not incorporate AI enabled functionalities”
• “software that calculates a population average from a survey that is later exploited in a general context”

Systems “solely intended for descriptive analysis, hypothesis testing, and visualisation” are also not AI systems (paragraph (47)). The Guidelines give these examples:

• In “software for sales report visualisation, statistical methods can be used to create a sales dashboard that shows total sales, average sales per region and sales trends over time”. But “the system does not recommend how to improve sales or which products to promote”.
• “a software system that applies statistical techniques to opinion polls or survey data to determine their validity, reliability, correlation, and statistical significance”

In the Guidelines, these systems “simply present data in an informative way”.

3. Heuristics

In the Commission Guidelines, “Classical heuristics are problem-solving techniques that rely on experience-based methods to find approximate solutions efficiently.” (paragraph (48)). They “typically involve rule-based approaches, pattern recognition, or trial-and-error strategies rather than data-driven learning”. These systems “apply predefined rules or algorithms to derive solutions”. But machine learning systems “adjust their models based on input-output relationships”.

The Guidelines give this example: “a chess program using a minimax algorithm with heuristic evaluation functions can assess board positions without requiring prior learning from data”. The Guidelines add that “heuristic methods may lack adaptability and generalization compared to AI systems that learn from experience”.

A system without “data-driven learning” can be an AI system. Logic-based and knowledge-based techniques that use rules “encoded by human experts” are AI techniques (paragraph (39), see Element 5).

4. Simple prediction systems (paragraphs (49)–(51))

The Commission Guidelines give this group in paragraph (49): “All machine-based systems whose performance can be achieved via a basic statistical learning rule, while technically may be classified as relying on machine learning approaches fall outside the scope of the AI system definition, due to its performance.” The Guidelines give these examples:

• “in financial forecasting (basic benchmarking) such machine-based systems may be used to predict future stock prices by using an estimator with the ’mean‘ strategy to establish a baseline prediction” (paragraph (50)). “Such basic benchmarking methods help to assess whether more advanced machine learning models could add value.”
• “using the average temperature of last week for predicting tomorrow’s temperature” (paragraph (50))
• a “customer support response time system that are based on static estimation to predict the mean resolution time from the past data” (paragraph (51))
• “demand forecasting for a store to predict how many items of a product the store will sell each day” (paragraph (51))

These systems “help to establish a baseline or a benchmark” (paragraph (51)).

AI systems and GPAI models

Article 3(1) of the EU AI Act gives the definition of an AI system. Article 3(63) gives the definition of a general-purpose AI model. Recital 97 of the EU AI Act contains this text:

Although AI models are essential components of AI systems, they do not constitute AI systems on their own. AI models require the addition of further components, such as for example a user interface, to become AI systems.

Article 3(66) gives the definition of a general-purpose AI system: “an AI system which is based on a general-purpose AI model and which has the capability to serve a variety of purposes, both for direct use as well as for integration in other AI systems”.

Chapter V of the EU AI Act gives the rules for general-purpose AI models. These rules are different from the rules for AI systems. In recital 97, they “should apply also when these models are integrated or form part of an AI system”. For the conditions of Article 3(63), see The GPAI model test. Paragraph (64) of the Commission Guidelines contains this text: “The analysis on the differences between AI systems and general-purpose AI models is outside the scope of these Guidelines.” The text above about models and systems comes from recital 97.

The Article 3(1) decision is about “the specific architecture and functionality of a given system” (Commission Guidelines, paragraph (61)). It is also about the interaction of the system with its external environment (paragraph (19)). In recital 12 of the EU AI Act, environments are “the contexts in which the AI systems operate”. Thus, the environment is not a part of the AI system.

In recital 97 of the EU AI Act, general-purpose AI models “may be further modified or fine-tuned into new models”. Thus, fine-tuning can make a new model. But a new model is a model, and a model is not an AI system without more components.

A wrapper or other software that uses a model is an AI system only if all elements of Article 3(1) apply to it. Examine its architecture and functionality (Commission Guidelines, paragraphs (61)–(62)). The use of a model in a system does not automatically make a new AI system. It also does not automatically remove the system from the definition.

In paragraph (59) of the GPAI-scope Guidelines, a downstream modifier becomes “the provider of the modified general-purpose AI model only if the modification leads to a significant change in the model’s generality, capabilities, or systemic risk”. In these Guidelines, fine-tuning is one type of modification (paragraph (57)). For this test, see Commission guidance on GPAI models.

AI system classification

Article 3(1) of the EU AI Act does not tell the reader which obligations apply to an AI system. For Articles 5, 6, and 50, examine the conditions of each Article. Article 2 also applies, for example the Section B rule in Scope of the AI Act.

• Article 5 prohibited practices. Each prohibition has conditions and an application date, but only some have express exceptions. The two prohibitions that Regulation (EU) 2026/1744 added to Article 5(1), and Article 5(1a) and (1b), apply from 2 December 2026. See Commission guidance on prohibited practices.
• Article 6(1) and Annex I. AI systems related to Annex I products are high-risk only if all Article 6(1) conditions apply.
• Article 6(2), Annex III, and Article 6(3). Annex III AI systems are high-risk unless the Article 6(3) exception conditions apply, with no exception for systems that do profiling of natural persons.
• Article 50 transparency. The obligations of providers and deployers in Article 50 have their conditions and exceptions. Article 50 does not apply to an AI system only because Article 5 does not apply to it. Also, Article 50 does not make an Article 5 practice permitted. See Article 50 transparency and Commission guidance on transparency.

Record the Article 3(1) decision

The EU AI Act does not contain a general obligation to record how an organization applied the Article 3(1) test. Modulos recommends that an organization records its Article 3(1) decision, the elements that it examined, and why it reached that result. This is a good procedure, for example when a system is near one of the groups in Systems that are not AI systems. The record is a recommendation of Modulos, not an obligation in the AI Act.

The provider's Article 6(4) documentation duty is a statutory obligation, different from the Article 3(1) record that Modulos recommends.

Use Modulos

For the EU AI Act Frameworks OFF-1 at Organization level and MFF-1 at AI System level, see Requirements and recommended steps.

Sources

• Commission Guidelines on the definition of an AI system: “Commission Guidelines on the definition of an artificial intelligence system established by Regulation (EU) 2024/1689 (AI Act)”, Communication from the Commission “C(2025) 5053 final”, 29 July 2025. Paragraphs (2), (6), (7), and (9) to (64). Commission text.
• EU AI Act: Articles 2, 3(1), 3(12), 3(63), 3(66), 5, 6(1) to 6(4), 49(2), 50, and 113 of Regulation (EU) 2024/1689 of 13 June 2024 (consolidated version of 27 July 2026). EUR-Lex.
• EU AI Act, recitals: Recitals 12 and 97 of Regulation (EU) 2024/1689, published in the Official Journal on 12 July 2024. EUR-Lex.
• Digital Omnibus on AI: Amendments to EU AI Act Articles 2(2), 5, 6, and 113 in Regulation (EU) 2026/1744 of 8 July 2026 (Official Journal, 24 July 2026). EUR-Lex.
• GPAI-scope Guidelines: Paragraphs (57) and (59) of “Commission Guidelines on the scope of the obligations for providers of general-purpose AI models established by Regulation (EU) 2024/1689 (AI Act)”, Communication from the Commission “C(2025) 7719 final”, 19 November 2025. Commission text.
• Commission guidelines on Article 50: “Guidelines on the implementation of the transparency obligations for certain AI systems under Article 50 of Regulation (EU) 2024/1689”, Annex to Communication to the Commission “C(2026) 5054 final”, 20 July 2026. Paragraphs (21) and (22). Commission text.

Disclaimer

This page gives general information. It is not legal advice.