The HR department pre-sorts applications with an assistant system, a chatbot answers the first inquiries in customer service, and marketing produces product texts in seconds. None of these decisions was made as a regulatory decision. Since August 2, 2026, they are regulatory decisions nonetheless, because key provisions of the EU AI Act have been applicable and enforceable since then.<\/p>\n\n\n\n
Uncertainty among SMEs is high, and it is being amplified by contradictory reports. On the one hand, deadlines have been postponed; on the other, Germany has designated its supervisory authority, and fines can reach up to 35 million euros. Both are true. The question is which part of this affects you.<\/p>\n\n\n\n
clience.legal is a digital business law firm for entrepreneurs. We support the legally compliant use of AI<\/a> and assess which obligations actually follow from the business model. This article sorts the rules by role and risk class and shows what a company that uses AI rather than developing it actually needs to do.<\/p>\n\n\n\n This distinction determines ninety percent of your workload. A provider is anyone who develops an AI system or has it developed and places it on the market under their own name or trademark. A deployer is anyone who uses an AI system under their own authority.<\/p>\n\n\n\n Anyone who uses a tool purchased on the market is a deployer. The extensive obligations regarding risk management, technical documentation, data governance and conformity assessment fall on the provider. Deployer obligations are considerably leaner and essentially concern use in accordance with the intended purpose, human oversight and informing the persons affected.<\/p>\n\n\n\n Caution is advised if you substantially modify a third-party system, offer it under your own name or use it for a purpose other than the intended one. In these cases, you may slip into the provider role, with all the obligations attached to it. Check this before you embed a solution in your own product.<\/p>\n\n\n\n In practice, two mistakes occur. The first is the assumption that a mere user is not affected at all. Deployer obligations are leaner, but they exist, and the transparency and AI literacy obligations apply to everyone who uses AI in the company.<\/p>\n\n\n\n The second mistake is the inadvertent shift in roles. Anyone who embeds a purchased model into their own product, offers it under their own name, or uses it for a purpose other than that intended by the manufacturer can be considered a provider. This happens particularly frequently in the case of software companies that integrate an assistance function into their application and then advertise it using their own product name.<\/p>\n\n\n\n It is therefore advisable to answer three questions in writing for every integration: who appears to the customer as the responsible party, is the manufacturer\u2019s intended purpose being changed, and how far do the provider\u2019s contractual assurances extend? The answers belong in the contract with the provider and in your own service description. We describe how AI tools can be integrated sensibly into contract work in AI in contract law<\/a> and, with a view to the limits, in why contracts should not be drafted with AI tools such as ChatGPT<\/a>.<\/p>\n\n\n\n Certain applications are prohibited, such as social scoring with detrimental effects, the targeted exploitation of vulnerabilities of certain groups of persons and emotion recognition in the workplace. The last point is of practical relevance for companies, because sentiment analysis systems in call centers or job interviews may be covered by it. Violations trigger the highest range of fines, up to 35 million euros or seven percent of worldwide annual turnover.<\/p>\n\n\n\n This category covers AI systems that pose a high risk due to their area of use, including recruitment and personnel management, creditworthiness assessment, critical infrastructure and education. For SMEs, the HR side is particularly relevant: systems that pre-screen applications or support promotion decisions typically fall into this category.<\/p>\n\n\n\n The Digital Omnibus has narrowed the definition of high-risk AI. Components that merely serve to support, improve efficiency or control quality are now only considered high-risk if a failure can actually create risks to health or safety. The classification itself therefore becomes more important, because it is no longer made across the board.<\/p>\n\n\n\n This class affects most medium-sized users. Anyone who uses a system that interacts with natural persons must ensure that it is recognizable that they are interacting with a machine. Artificially generated or manipulated content must be labeled accordingly. These obligations have applied since August 2, 2026. The technical labeling of synthetic content by machine-readable markings has been postponed to December 2, 2026, because the standards for it are still maturing.<\/p>\n\n\n\n The vast majority of business applications fall into this category, such as spell checkers, scheduling, translation or forecasting in logistics planning. No special obligations under the AI Act arise here. Data protection, trade secret protection, copyright and liability continue to apply regardless.<\/p>\n\n\n\n The amendment package was published in the Official Journal at the end of July 2026 and has entered into force. Three points are essential for companies.<\/p>\n\n\n\n A postponement, however, is not a repeal. On December 2, 2027, the full catalog of obligations originally planned for August 2026 is expected to apply. Those who do nothing now will have used up the time gained instead of using it.<\/p>\n\n\n\n Since February 2, 2025, Article 4 of the AI Act has required providers and deployers to take measures to ensure that their staff and other persons dealing with the operation of AI systems have a sufficient level of AI literacy. The scope depends on prior knowledge, the context of use and the groups of persons affected.<\/p>\n\n\n\n In practice, this means documented training tailored to the role. Someone who works with a chatbot in customer service needs different knowledge than the specialist department that selects a system. Record participants, content and date, because if in doubt, compliance with this obligation can only be proven through documentation. The content framework for this overlaps considerably with what we describe in AI and data protection<\/a> and Liability, copyright and data protection when using AI<\/a> .<\/p>\n\n\n\n The deployer obligations can be summarized in four points that can be implemented without much effort.<\/p>\n\n\n\n First, use in accordance with the intended purpose. A system may only be used for the purpose for which it is intended. This sounds obvious, but it is regularly violated when a tool is used for new tasks in day-to-day business without anyone checking the intended purpose.<\/p>\n\n\n\n Second, human oversight. Results must not feed unchecked into decisions that affect people. This requires a designated person who is able and authorized to review them and who also has the necessary time to do so. A duty to review without the capacity to do so is a fiction.<\/p>\n\n\n\n Third, informing the persons affected. Anyone who interacts with a system or is affected by a decision supported by it should be able to recognize this. In the employment context, the workforce must also be informed.<\/p>\n\n\n\n Fourth, record-keeping. Where the system generates logs, these must be retained insofar as they fall within your area of responsibility. These records are also your proof that oversight actually took place.<\/p>\n\n\n\n Germany has regulated national implementation with the AI Market Surveillance and Innovation Promotion Act, which the Bundestag passed on June 11, 2026. The Federal Network Agency is the central market surveillance authority. A coordination and competence center has been established there, which pools cooperation with the specialist authorities and operates a service desk for companies that is expressly aimed at small and medium-sized enterprises as well.<\/p>\n\n\n\n Sectoral responsibilities remain in place. BaFin supervises the financial sector, BfArM medical devices, and the data protection supervisory authorities data protection matters. The act does not create any additional substantive obligations; it governs responsibilities, procedures and sanctions. The authorities can request documents, examine systems and require corrective measures.<\/p>\n\n\n\n For companies, the designation of the supervisory authority is more than a formality. It ends the situation in which a regulation applied but no one enforced it. From now on, there is an address for complaints, for example from applicants, customers or competitors, and a procedure that follows. At the same time, this creates for the first time a body to which companies can put questions about classification without triggering proceedings.<\/p>\n\n\n\n For most medium-sized companies, this can be done in a few days. It only becomes complex where high-risk applications are in use, and that is exactly why early classification pays off. We describe how this fits into your wider compliance in Compliance and managing director liability<\/a> and AI: Opportunities and pitfalls<\/a>.<\/p>\n\n\n\n A common misconception is that the matter is settled once the classification under the AI Act has been made. In fact, the Act leaves several areas of law untouched that are more often relevant in day-to-day business.<\/p>\n\n\n\n These issues can be covered in the same usage policy, which significantly reduces the effort. We cover the data protection aspects in Data protection and compliance in the company<\/a>.<\/p>\n\n\n\n Related: Legal certainty in the digital enterprise<\/a>.<\/p>\n\n\n\n Send us a list of the AI tools in use and a brief description of what they are used for. We will determine the role and risk class, identify the obligations that actually apply to you and draft the usage policy your teams can work with. To do so, book a free initial consultation<\/a> of 15 to 20 minutes. For ongoing support, we are available as external legal department<\/a> .<\/p>\n\n\n\nThe most important information at a glance<\/h2>\n\n\n\n
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The first key question: provider or deployer?<\/h2>\n\n\n\n
Why the role question is so often answered incorrectly<\/h2>\n\n\n\n
The four risk categories<\/h2>\n\n\n\n
Prohibited practices<\/h3>\n\n\n\n
High-risk systems<\/h3>\n\n\n\n
Systems subject to transparency obligations<\/h3>\n\n\n\n
Minimal risk<\/h3>\n\n\n\n
What the Digital Omnibus changed<\/h2>\n\n\n\n
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The obligation that already applies: AI literacy<\/h2>\n\n\n\n
What deployers specifically have to do<\/h2>\n\n\n\n
Who supervises in Germany<\/h2>\n\n\n\n
Five steps to an inventory<\/h2>\n\n\n\n
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What the AI Act does not regulate<\/h2>\n\n\n\n
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