Tag: Ai act

• 14 Sep, 2026
• • Compliance,
  • Product

AI Act and analytics: when should an AI feature in a dashboard be disclosed?

Analytics products increasingly include AI-assisted features: automatic monthly traffic summaries, anomaly detection, generated reporting comments, conversational dashboard assistants and optimisation suggestions. Since 2 August 2026, part of the AI Act’s transparency obligations has applied. This is no longer only a topic for public chatbots, large platforms or general-purpose AI models. It can also concern software publishers adding AI interfaces to professional products, depending on the system and use case. The useful question is not: “Does every AI-powered feature need a warning?” The useful question is: is the person directly interacting with an AI system, or being exposed to AI-generated or manipulated content in a context covered by Article 50? This article gives product, marketing, analytics and compliance teams a practical way to reason about the issue. What started to apply on 2 August 2026 The European Commission published guidelines on 20 July 2026 to help providers and deployers comply with transparency obligations for certain AI systems. These obligations started applying on 2 August 2026. They are meant to help people recognise when they are interacting with an AI system or when content has been generated or modified by AI. Broadly speaking, providers must design certain systems so people are informed when they directly interact with AI, and must add machine-readable marks to enable detection of certain AI-generated or manipulated content. Deployers also have information duties in some cases, including deepfakes, certain public-interest content without human review, and some emotion recognition or biometric categorisation systems. For a professional analytics product, not every automated feature falls into the same bucket. A statistical calculation, a rule-based alert or a background anomaly model is not the same as a visible conversational assistant. Case 1: a conversational assistant in the dashboard This is the clearest case. If a user can ask a dashboard assistant a question such as “why did traffic drop this week?”, the user is directly interacting with AI. Where the Article 50 conditions are met and the AI character is not obvious, the interface must inform the user clearly, distinctly and accessibly at the start of the first interaction that they are dealing with an AI system, not a human analyst or a purely deterministic rule. A concise label is usually enough:AI assistant: responses are generated automatically from the data available in this workspace. Verify important conclusions before making decisions.Transparency should not become a frightening legal wall. It should be clear, accessible and proportionate. Case 2: an automatically generated analytics report summary An automatic summary can have several forms. If it remains internal and helps a team prepare its monthly analysis, the main issue is governance: who can generate it, which data it uses, whether it is reviewed, and how the system avoids inventing causes that are not supported by the data. If the summary is sent to clients, a board, investors or the public, the analysis changes. The text may become information generated by an AI system. In some cases, especially where it concerns matters of public interest without human review or editorial control, Article 50 requires specific information. For a B2B analytics vendor, a simple operational rule can help:an internal AI-generated draft should be identified as such in the interface; an automatically generated external report should keep a generation trace; reports that influence important decisions should not be published without real human review; for information published to inform the public on matters of public interest, any human review invoked for the Article 50 exception should be genuine and documented.The key word is genuine. A superficial approval step should not be described as editorial control. Case 3: anomaly detection A system that flags an unusual traffic variation is not always a direct AI interaction. It may be a statistical model, a threshold rule or a background automated process. That does not mean there are no obligations. If personal data is involved, GDPR, minimisation, documentation and governance remain relevant. But Article 50 transparency is not triggered simply because an algorithm exists. The product should name the feature honestly. “Anomaly detected” may be more accurate than “AI discovered a problem”. If an explanation is generated by a model, separate the measured signal from the generated interpretation. Example:Measured signal: organic sessions fell by 34% over seven days. Generated hypothesis: the drop may be related to three acquisition pages. Status: hypothesis to verify, not an automatic conclusion.This distinction reduces analytical hallucination. Case 4: automatic recommendations AI recommendations are attractive: “improve this page”, “reduce this channel”, “increase this budget”. They are also risky if they hide data limitations. In analytics, a recommendation should show:the data used; the period analysed; known exclusions; confidence level or at least uncertainty; the difference between observation, hypothesis and recommendation.The AI Act raises the transparency question for individuals. Analytics credibility adds another one: does the user understand why the recommendation exists? Good design does not only say “AI-generated”. It gives enough context to verify or challenge the recommendation. Provider or deployer: why the distinction matters The Commission distinguishes providers, who develop or place an AI system on the market, from deployers, who use an AI system under their authority in a professional or organisational context. A SaaS publisher integrating an AI feature into its product may be the provider of that feature for its customers. A company enabling that feature for its employees may be a deployer. In some scenarios, both roles coexist. This matters for product teams. It is not enough to assume that the provider of the underlying model carries all responsibility. The software publisher designing the experience, choosing the data, displaying the output and defining the use case should document its own choices. Checklist for an AI feature in analytics Before launching an AI feature, check:does the feature involve direct interaction between the user and an AI system? could the user think they are interacting with a person or a human-authored analysis? can the generated content be exported, shared or published? is real human review planned before external publication? does the interface clearly indicate what is generated automatically? does the output separate measured data, hypotheses and recommendations? are the necessary prompts, model versions and data sources logged, with personal data filtered, controlled access and a defined retention period? are known limits understandable by a business user? can support teams explain the feature at a high level? are related personal-data processing operations documented separately?What to avoid Avoid labels that make AI invisible. “Automatic analysis” may be too vague if the user is actually interacting with a generative system. Avoid the opposite excess too. A heavy legal banner on every chart can make the interface less readable without improving user understanding. Most importantly, avoid unsupported causal claims. A model can detect a correlation or propose a hypothesis, but it should not invent a business cause without evidence. In an analytics dashboard, transparency is not only about legal disclosure. It should help the user make better decisions. Conclusion The AI Act does not mean that every algorithmic feature in an analytics product needs a permanent warning. It does require serious treatment of situations where users directly interact with AI or receive generated content in a context covered by Article 50. For SaaS publishers, the right response is not only legal. It is product discipline: name AI when it is used, explain its limits, log the relevant context, and separate measured facts from generated interpretation. It is also a credibility opportunity. Useful analytics should not merely generate more commentary. It should generate commentary that can be checked. FAQ Are all automated analytics features covered? No. A statistical rule, a trend calculation or a threshold alert is not automatically covered by the AI Act transparency obligations. The first step is to qualify the system and determine whether it directly interacts with a person or generates content that must be disclosed. Do AI summaries need a banner every time? Not necessarily. The information should be clear, understandable and visible at the right point in the workflow. A stable notice attached to the summary module may be more useful than a repeated intrusive alert. Does human review remove all labelling questions? It depends on the context, the use of the report and the actual level of editorial review. The safer approach is to document the validation chain rather than assume that any human review removes every obligation. SourcesEuropean Commission - Guidelines on transparency obligations for providers and deployers of AI systems European Commission - Transparency obligations under Article 50 of the AI Act European Commission - Guidelines published on 20 July 2026