{"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":"2026-10-02T23:47:28","modified_gmt":"2026-10-02T21:47:28","slug":"the-same-ai-a-different-risk-depending-on-the-sector","status":"publish","type":"post","link":"https:\/\/klangholz-compliance.de\/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, the same click could be sensitive: it may involve client data and professional confidentiality. A supplier’s quotation in the workshop next door might be less sensitive. That does not automatically make it unproblematic: it may contain confidential prices or personal information. The content and the tool approved for it are what matter. In a care setting, the information could be particularly sensitive, as health data is among the most strongly protected information.<\/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 are part of everyday work. Disclosure is therefore an important question. Specific transparency obligations apply to deepfakes and certain AI-generated texts intended to inform the public. Not every AI-assisted draft automatically falls within these rules.<\/li>\n\n\n\n
  • Recruitment and staffing services.<\/strong> AI systems used to evaluate or shortlist applications may be classified as high-risk. Their purpose, operation and the allocation of responsibility need particular care.<\/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> AI can make quotations, materials lists and documentation easier to prepare. Confidential data still needs protection and results must be checked, especially where errors could affect safety.<\/li>\n\n\n\n
  • Retail and services.<\/strong> Where AI chatbots interact with customers, transparency and data protection matter. People should generally be told that they are communicating with AI, unless this is already obvious.<\/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":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/posts\/262","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/comments?post=262"}],"version-history":[{"count":11,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/posts\/262\/revisions"}],"predecessor-version":[{"id":577,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/posts\/262\/revisions\/577"}],"wp:attachment":[{"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/media?parent=262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/categories?post=262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/klangholz-compliance.de\/en\/wp-json\/wp\/v2\/tags?post=262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}