{"id":262,"date":"2026-06-30T13:07:06","date_gmt":"2026-06-30T11:07:06","guid":{"rendered":"https:\/\/p-j4su3f.project.space\/2026\/06\/30\/the-same-ai-a-different-risk-depending-on-the-sector\/"},"modified":"2026-07-02T12:42:27","modified_gmt":"2026-07-02T10:42:27","slug":"the-same-ai-a-different-risk-depending-on-the-sector","status":"publish","type":"post","link":"https:\/\/euaiact-academy.com\/en\/2026\/06\/30\/the-same-ai-a-different-risk-depending-on-the-sector\/","title":{"rendered":"The same AI, a different risk depending on the sector"},"content":{"rendered":"\n

Most AI training courses treat everyone the same. Yet the same action \u2013 such as copying a text into an AI tool \u2013 can be completely harmless in one sector and a real problem in another. If you train everyone the same way, you end up getting it wrong for everyone<\/em>. <\/p>\n\n

The same action, completely different implications<\/h2>\n\n

Let\u2019s take a very specific scenario: an employee copies a text into an AI tool to have it summarised. Harmless? That depends on the sector she works in. <\/p>\n\n

In a tax consultancy, that same click could be risky \u2013 it might involve client data, and thus breach professional confidentiality. At the tradesman\u2019s workshop next door, it\u2019s completely uncontroversial if it\u2019s a supplier\u2019s quotation. In a care home, it would be particularly sensitive, because health data is among the most strictly protected information of all. <\/p>\n\n

One action. Three sectors. Three completely different risk scenarios. This is precisely where it becomes clear why training \u2018for everyone\u2019 so often comes to nothing in practice. <\/p>\n\n

Where the sector decides on the actual issues<\/h2>\n\n

In my work with companies from a wide range of sectors, I see time and again that every sector has its own key issue when it comes to AI. Here are a few examples of just how different the priorities are <\/p>\n\n

    \n
  • Law firms, tax consultancy firms, doctors\u2019 practices. <\/strong>Here, everything revolves around professional secrecy. The crucial question is which confidential client or patient data is even permitted to come into contact with an AI tool \u2013 and which is never allowed to. <\/li>\n\n\n\n
  • Marketing and the creative industries. <\/strong>AI-generated text and images have long been part of everyday life here. The dominant issue is therefore labelling: from August 2026, AI-generated content must be recognisable as such. <\/li>\n\n\n\n
  • Recruitment and staffing services. <\/strong>As soon as AI plays a part in the pre-selection of job applications, we enter an area that is actually considered high-risk. This is where training is most demanding \u2013 and most important. <\/li>\n\n\n\n
  • Financial services. <\/strong>The situation becomes similarly sensitive when AI is involved in credit checks. This, too, is a strictly regulated use case, not an everyday tool like any other. <\/li>\n\n\n\n
  • Skilled trades and Production. <\/strong>For quotations, bills of materials or documentation, AI is usually not a critical issue. Here, it is less about bans and more about the sensible handling of data and genuine efficiency gains. <\/li>\n\n\n\n
  • Retail and services.. <\/strong>Where chatbots interact with customers, labelling and data protection are the key issues \u2013 the customer should know that they are chatting with an AI.<\/li>\n<\/ul>\n\n

    The message behind this is simple: training that says the same thing to everyone says too little to most people \u2013 and the wrong thing to some. A roofer isn\u2019t interested in a law firm\u2019s client confidentiality, and an agency\u2019s image labelling is of no help to a care worker. <\/p>\n\n

    Why generic training fails to resonate<\/h2>\n\n

    There is a simple pedagogical reason why industry-specific relevance makes such a difference: people learn from situations they are familiar with. If an example comes from an unfamiliar working environment, people nod politely \u2013 but do not apply it to their own everyday lives. <\/p>\n\n

    \u2018Industry-specific\u2019 therefore means that the examples and borderline cases come from the learners\u2019 own lived experience. The care worker hears a care-related case, the tradesperson one from their own trade, and the tax clerk one from the firm. Only then does the crucial moment arise: \u2018Ah, that\u2019s exactly my kind of case<\/em>.\u2019 And it is precisely this moment that sticks in the mind. <\/p>\n\n

    But the industry is only half the battle<\/h2>\n\n

    As important as the sector is \u2013 it\u2019s not enough on its own. Because even within the same company, not everyone uses AI in the same way. The colleague in Marketing faces completely different questions to the accountant in the next office, even though both work in the same sector. <\/p>\n\n

    Sector and department are therefore intertwined. The sector determines what a company fundamentally deals with. The department determines what the individual actually does in their day-to-day work. Only when both are combined does training become truly effective \u2013 but that\u2019s a topic in its own right. <\/p>\n\n

    Industry-specific focus is not a luxury, but a prerequisite<\/h2>\n\n

    Anyone who takes AI training seriously cannot ignore the industry. It determines whether the content is perceived as an abstract compulsory exercise or as something that is tangibly relevant to one\u2019s own working day. Generic training is quick to deliver \u2013 but rarely understood. <\/p>\n\n

    That is precisely why the content of the EU AI Act Academy is tailored to specific sectors: everyone is given examples and borderline cases that actually occur in their own day-to-day work \u2013 rather than training that is supposed to suit everyone and therefore doesn\u2019t quite suit anyone.<\/em><\/p>\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Most AI training courses treat everyone the same. Yet the same action \u2013 such as copying a text into an AI tool \u2013 can be completely harmless in one sector and a real problem in another. If you train everyone the same way, you end up getting it wrong for everyone. The same action, completely […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"class_list":["post-262","post","type-post","status-publish","format-standard","hentry","category-eu-ai-act"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/posts\/262","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/comments?post=262"}],"version-history":[{"count":5,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/posts\/262\/revisions"}],"predecessor-version":[{"id":267,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/posts\/262\/revisions\/267"}],"wp:attachment":[{"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/media?parent=262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/categories?post=262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/euaiact-academy.com\/en\/wp-json\/wp\/v2\/tags?post=262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}