With the entry into force of additional provisions of the EU AI Act, many companies are currently asking themselves the same question: Starting August 2, must all AI-generated images be visibly labeled?
The short answer is: No.
The longer and more important answer, however, is this: Companies should not assume that the issue of AI labeling does not apply to them. This is because Article 50 of the EU AI Act goes far beyond the mere labeling of images. In fact, it is a key transparency requirement that covers numerous use cases of artificial intelligence and entails significant organizational requirements.
The real challenge, therefore, is not to flag individual pieces of content. The challenge lies in understanding one’s own use of AI, defining responsibilities, and establishing a structured governance framework.
The Fallacy of a Blanket Labeling Requirement
In recent months, numerous simplified explanations of the EU AI Act have been published. These often give the impression that, in the future, every image generated using artificial intelligence will be required to include a visible warning label.
However, the legal situation is not that simple.
Article 50 of the EU AI Act sets forth transparency requirements for various AI applications. The specific requirements that apply depend, among other things, on:
- What type of AI system is used
- What role a company plays
- What content is created or processed
- The context in which AI is used
- What risks do affected individuals face?
Therefore, there is no one-size-fits-all answer for all AI-generated content.
For companies, this means they must evaluate their AI applications on a case-by-case basis rather than making general assumptions.
Transparency involves much more than just images
Anyone who associates Article 50 exclusively with image generation is overlooking a significant portion of the requirements.
The regulation covers, among other things:
- Chatbots and AI Assistants
- AI Agents
- AI-generated or manipulated audio content
- AI-generated or manipulated video content
- Deepfakes
- Emotion Recognition Systems
- Biometric Categorization Systems
- Certain AI-generated texts on topics of public interest
- Requirements for Machine-Readable Markings
This makes it clear that transparency is not just a marketing issue. It affects numerous business processes, customer interactions, and internal applications.
Companies that use AI in customer service, human resources, marketing, software development, or knowledge management should therefore assess whether transparency requirements apply.
The key question: What role does your company play?
A key aspect of the AI Act is that obligations depend on the specific role.
Many organizations today use AI systems from major providers such as Microsoft, OpenAI, Google, or Anthropic. It is often assumed that all regulatory obligations rest solely with the provider.
This assumption can be dangerous.
Among other things, the AI Act distinguishes between:
- Providers of AI systems
- Operators or Deployers
- Importers
- Retailers
- Authorized Representatives
Depending on the use case, different obligations may arise. In one scenario, a company may simply be a user of an AI solution, while in another, it may provide content itself or configure AI systems in a way that triggers additional requirements.
Without a clear definition of roles, it is therefore difficult to determine what legal obligations actually exist.
Do you even know which AI systems are being used?
Many companies already face a significant transparency problem today.
Employees use AI tools for:
- Copywriting
- Image Generation
- Translations
- Software Development
- Data Analysis
- Research Tasks
- Customer Communication
This often happens on a decentralized basis and without centralized tracking.
As a result, companies are unable to answer fundamental questions:
- What AI systems are used?
- Who uses these systems?
- For what purposes are they used?
- What data is processed?
- What content is published?
- What are the risks?
An essential component of effective AI governance is therefore, first and foremost, to establish transparency regarding one’s own AI landscape.
You can only meet requirements that you are aware of. This is not possible for unknown AI applications.
Who reviews and approves AI-generated content?
Another point that is often overlooked concerns internal responsibilities.
Many companies have approval processes in place for contracts, data protection documents, or marketing campaigns. However, such processes often do not yet exist for AI-generated content.
This raises a number of important questions:
- Who creates the content?
- Who verifies the technical accuracy?
- Who assesses potential transparency requirements?
- Who decides whether a label is required?
- Who documents the decision?
Without clear lines of responsibility, uncertainties and inconsistencies can quickly arise.
While one department consistently tags content, another may not do so at all. This not only increases regulatory risks but also makes audits and compliance verification more difficult.
Documentation Is Becoming a Key Success Factor
An effective compliance strategy does not end with the implementation of a measure.
Companies are increasingly required to demonstrate how decisions were made and on what basis certain assessments were conducted.
Therefore, organizations should document the following:
- Which AI systems are used
- What assessments were conducted
- What transparency requirements were identified
- What measures were implemented
- Who approved the decisions
- When inspections took place
This documentation not only provides regulatory certainty; it also facilitates internal controls, certifications, and external audits.
AI Governance Instead of Isolated Measures
Many companies are currently looking for quick solutions to specific requirements of the AI Act.
In practice, however, it has become apparent that isolated measures are rarely sufficient.
Simply introducing a labeling system for AI-generated images may address only a small portion of the actual requirements.
A more sustainable approach is to establish a structured AI governance framework with defined processes, roles, and responsibilities.
These include, for example:
- AI Guidelines
- Approval Processes
- Risk Assessments
- Training Courses
- Documentation
- Control mechanisms
- Regular Inspections
A systematic approach not only reduces regulatory risks but also builds trust among customers, business partners, and regulatory authorities.
ISO/IEC 42001 as the Foundation for Sustainable AI Compliance
Many organizations now recognize that the long-term implementation of AI regulations is only feasible through a management system approach.
The international standard ISO/IEC 42001 provides a structured framework for this.
It helps companies:
- Identifying AI systems
- Define Responsibilities
- Assessing Risks
- Establishing governance processes
- Implementing compliance requirements
- Provide documentation for audits
This creates a solid foundation for the responsible use of artificial intelligence.
Conclusion
The discussion about labeling AI-generated images falls short. Article 50 of the EU AI Act goes far beyond visual content and imposes comprehensive transparency requirements on companies.
The key question, therefore, is not whether a single image must be labeled. The key question is whether an organization even knows which AI systems are being used, what obligations arise from their use, and how these decisions are documented.
Assumptions are not a compliance strategy.
Companies should take this opportunity to analyze their use of AI, define responsibilities, and establish robust governance structures.
Syngenity® GmbH helps organizations achieve precisely this level of transparency and establish AI governance structures and AI management systems in accordance with ISO/IEC 42001. After all, sustainable AI compliance does not begin with a label, but with a systematic understanding of an organization’s own AI landscape.
