Article 50 has moved from preparation to practice
From 2 August 2026, the EU AI Act's Article 50 transparency obligations apply to specified AI systems and uses. The European Commission published final implementation guidelines in July, alongside a Code of Practice intended to help organisations meet the marking and labelling requirements for AI-generated or manipulated content.
The practical point is simple: organisations now need to know when their AI systems must disclose that a person is interacting with AI, when generated outputs need machine-readable marking and when people must be told that content or analysis involves AI.
This is not only a product-interface task. Compliance depends on accurate system inventories, clear provider and deployer roles, release controls, supplier evidence and records showing why a particular disclosure approach was selected.
Which business activities should be checked first
Start with AI systems that interact directly with people. Providers of interactive AI systems generally need to design them so individuals are informed that they are interacting with AI, unless that fact is obvious to a reasonably informed and observant person in the circumstances. Customer-service assistants, candidate-facing recruitment tools and automated advisory interfaces deserve early attention.
Next, identify systems that generate or manipulate image, audio, video or text. Providers within scope need technical measures that mark outputs in a machine-readable format and make them detectable as artificially generated or manipulated. The Commission says those measures should be effective, interoperable, robust and reliable as far as technically feasible.
Deployers also have duties in specific contexts. These include disclosure when using emotion recognition or biometric categorisation systems, labelling deepfake content, and identifying certain AI-generated or manipulated text published to inform the public on matters of public interest. The exact rule and exceptions depend on the use case, so record the rationale rather than relying on a blanket statement.
A six-step Article 50 readiness check
First, update the AI inventory. Record every system that interacts with people or generates or manipulates content, including embedded third-party features. Second, confirm whether the organisation is acting as provider, deployer, importer or distributor for each system; obligations follow the role, not the label used internally.
Third, map the relevant transparency trigger and any exception. Fourth, capture the technical or user-interface measure used to meet it: notice wording, display timing, content label, metadata standard or machine-readable marker. Fifth, test whether the disclosure remains clear across devices, languages, export formats and downstream sharing.
Sixth, retain evidence. Keep screenshots, test results, product requirements, supplier confirmations, approval records and version history. A disclosure that exists in production but cannot be traced to a controlled decision will be harder to defend and maintain.
Do not treat the voluntary code as a substitute for the law
The Commission describes the Code of Practice as a voluntary framework that can help signatories demonstrate compliance with specified transparency obligations. It does not replace the AI Act or the Commission's Article 50 guidelines.
Organisations that do not follow the code remain responsible for compliance and should be able to explain how their chosen measures satisfy the regulation. That makes a documented gap assessment useful even where a business decides not to sign or follow every measure in the code.
For generative AI systems placed on the market before 2 August 2026, check the applicable transitional position carefully. Do not assume that every existing system or every output has the same treatment.
What good evidence looks like now
A credible Article 50 evidence pack connects each relevant system to its role assessment, transparency trigger, implemented measure, test record, owner and review date. It should also show how supplier changes and product updates trigger reassessment.
This evidence can serve several purposes at once: regulatory readiness, customer assurance, procurement responses and internal release approval. The goal is not a one-off label audit. It is a repeatable process that keeps transparency measures accurate as systems and uses change.
AI Act Ready helps organisations organise this work into a living inventory, role map, control set and buyer-ready evidence pack. For the documents customers are likely to request, read our procurement checklist next.