Your blog draft? ChatGPT. Meta title? Claude. The alt text for your hero image? Yep, AI-generated that too.
In 2026, we’ve moved beyond asking if content teams are using AI. With new EU regulations taking effect, the question every organization now needs to answer is: how are we using AI, and what do we need to disclose?
Let's break it down.
What's actually changed:
As of 2 August 2026, new EU AI Act transparency and labelling requirements are in effect for certain AI-generated and AI-manipulated content. Alongside these Article 50 requirements, the European Commission has published a voluntary Code of Practice on Transparency of AI-Generated Content, with around 190 organizations signing up ahead of the deadline.
The Code is not legally binding. Instead, it offers a practical blueprint for how organizations can meet the Act’s requirements. It’s a useful guide to what the Commission considers good practice, but it isn't a rulebook.
First, there’s an important distinction to make. Marking and labeling content are not the same thing:
Marking is machine-readable. Think invisible watermarks or signed metadata connected to a piece of content that a system can use to detect that AI was involved. This is aimed primarily at the companies that build AI systems.
Labeling is what a human sees. A visible note on the page saying content was AI-generated or AI-modified. This is aimed primarily at the organizations that publish content.
The Commission has even proposed standardized icons: “AI GENERATED” and “AI MODIFIED”; with guidance on design and placement. Those are optional. You can meet the transparency goal another way.
The part everyone gets wrong:
The rules are narrower than the panic suggests.
The disclosure obligation for text focuses on two things:
- Deepfakes, which are designed to make it impossible to tell real media from fake
- Text published to inform the public on matters of public interest
New requirements don't require you to stamp an "AI generated" badge on every sentence an LLM has touched.
A meaningful exemption exists.
When content has undergone human editorial review and a named person or organization takes editorial responsibility for it, the text disclosure requirement generally doesn’t apply. For content teams with an established review-and-approve workflow, this may be one of the most important provisions to understand.
The practical takeaway? Human oversight matters. For teams using a headless CMS like Kontent.ai, built-in personalized workflows, permissions, and approval steps can help make that oversight a defined part of the content process, giving teams a clear way to assign responsibility and document editorial review rather than adding another manual compliance task.
That exemption is doing a lot of work, and it’s worth understanding properly rather than labeling everything out of anxiety. Over-labeling has a real cost: if every page carries a disclaimer, the disclaimer stops meaning anything. We’ve collectively been here before with cookie banners.
The honest uncertainty
We’d rather say this plainly than pretend otherwise: nobody has fully settled what the unit of labeling is.
Is it the individual field? The content item? The page? The whole site? A page-level notice and a per-paragraph marking are wildly different amounts of work, and the guidance doesn’t yet resolve that.
The guidelines also don’t detail how to treat content that arrives already touched by AI: translated elsewhere, drafted in another tool, or copied and pasted from a document. Once content is in your system, its history usually isn’t.
Anyone telling you they have this precisely figured out is operating on forecasts, not facts.
What we’re seeing in the wider market:
The direction is becoming clearer, even though there isn't a single roadmap:
Provenance metadata is consolidating around C2PA Content Credentials, backed by Adobe, Microsoft, Google, OpenAI and others. It’s strongest for images, weaker for video and audio, and largely unsolved for plain text.
- Text is the hard modality. There is no reliable, universal watermark for AI-generated text, and the major model providers have said as much. Detection tools produce false positives, which is its own reputational risk.
- Distribution platforms have moved first. Meta shows “AI info” labels while YouTube requires creators to disclose realistic synthetic content. If you publish to those channels, you’re already operating under disclosure rules regardless of what any regulation does next.
What content teams need to do now:
Our advice is simple: build the capability now, and be ready to adapt as requirements change.
Start by finding out what you’re actually publishing. Most teams cannot answer the question, “How much of our content involved AI?” That’s the real gap, and it’s a governance problem before it’s ever a legal one.
- Add a labeling field to your content model. One element on the content types that matter gives you a way to capture that information from the start. It’s simple to implement, easy to change, and gives you the data you need. If you use Kontent.ai, we’ve documented exactly how to set this up, including which element type to choose and how to keep it reliable when AI agents are involved.
- Decide who owns editorial responsibility. If you plan to rely on the human review exemption, that reliance needs to be real: a named owner, an actual review step, and a record that it happened. A workflow step in your CMS can make this part of the publishing process.
- Be careful with autonomous publishing. If your AI agents can publish without human review, the human editorial review exemption may not apply. Keep a human approval step, or ensure required labelling is applied automatically. Agentic CMS platforms like Kontent.ai provide publishing guardrails that keep teams in control of when and how AI-generated content goes live.
- Keep the label separate from the display. Store the fact that content is AI-generated as structured data. Decide separately how (and if) to show it on each channel. Your website, app, and partner feed may each need something different, and you don’t want to redo the modeling every time.
That last point is the one we’d stress. If the requirements tighten, the teams who already hold this as clean, queryable data will adapt. The teams who don’t will be stuck doing a costly content audit.
How Kontent.ai supports your EU AI Act compliance
Personalized workflows: Keep humans in the loop by designing workflows where content is reviewed and approved by an authorized user before it is published.
Audit log + version history: See what was generated or edited by AI agents, track changes, and maintain a clear record of how content was created and reviewed.
Role-based permissions: Control who can create, edit, approve, and publish content, and prevent AI agents from publishing without explicit authorization.
Certifications: Kontent.ai takes AI governance and security seriously, aligning with leading industry frameworks and certifications, including AI ISO standards, the AI Pact, and the CSA AI Trustworthy Pledge 2025. To learn more about our certifications, visit our trust center.
Where we stand
Rather than introduce a one-size-fits-all AI labeling feature that could become outdated with a single regulatory change, we’re taking a more practical approach. Kontent.ai gives teams the flexibility to meet current requirements, while we continue to update the product as governing bodies provide further guidance. We’ll keep pace with regulatory changes, so your team doesn’t have to.