For decision-makers, this means: the crucial question is no longer just, where<\/strong> KI can be used. It reads, how<\/strong> value creation, decision architecture, and operating models need to be further developed if AI is to become an integral part of the organisation.<\/p>\n The transition to AI-nativity is particularly evident in three areas:<\/p>\n These three fields determine whether AI primarily generates efficiency or leads to sustainable differentiation.<\/p>\n\t Many companies start with AI where the benefits are immediately visible: faster analyses, more efficient processes, automated routines, lower costs. This is a plausible entry point. From a strategic perspective, however, it falls short.<\/p>\n Because AI is not only changing the speed of existing value creation. It is changing the way value is generated in the first place. When analyses, research, syntheses, or initial decisions become available in ever shorter periods of time, the bottleneck shifts. No longer will pure information production be the decisive factor, but rather the ability to meaningfully classify, prioritise, and translate information into new offerings, services, and decisions.<\/p>\n For companies, this means that anyone wanting to use AI strategically must re-examine their own value creation.<\/p>\n Decision-makers should ask themselves<\/strong><\/p>\n What specifically changes<\/strong><\/p>\n The third field refers to the Operating Model. This is where the difference between AI utilization and AI nativeness becomes particularly tangible.<\/p>\n As long as AI is primarily used as an additional tool, processes, roles and structures largely remain the same. The organisation continues to operate as before, just more efficiently in certain areas. AI-native organisations go further. They redesign processes around the capabilities of AI.<\/p>\n This doesn't just affect technology, but the way work is organised.<\/p>\n Typical questions therefore are <\/strong><\/p>\n This is particularly evident in knowledge-intensive environments. When AI takes over research, analysis, synthesis, or structuring, human work shifts. The focus moves more towards judgment, context, coordination, responsibility, and execution.<\/p>\n Organisations must rethink their operating models in terms of<\/strong><\/p>\n In addition, there is a structural dimension: AI-native mature organisations do not just operate faster. They operate differently. Processes become more adaptive, roles more fluid, knowledge more collectively organized, and the relationship between central control and decentralised operational competence shifts.<\/p>\n\t Decision-makers should ask themselves<\/strong><\/p>\n For AI-nativity to be sustainable, companies must address three aspects of their decision-making architecture rethink<\/strong><\/p>\n The result<\/strong><\/p>\n Organisations that consciously design this architecture make decisions not only faster, but often also more consistently, resiliently and strategically clearly.<\/p>\n\t Those who consciously develop the operating model create the foundation for scalable, AI-driven operations instead of isolated individual initiatives.<\/p>\n Many companies are currently in a phase that could be described as AI-First.<\/strong> AI is being actively used, new tools are being introduced, processes are being optimised. This is an important step.<\/p>\n However, AI-native maturity only truly begins where organizations take the next step.<\/p>\n\t For decision-makers, this distinction is central. Because it makes clear that the actual degree of maturity cannot be recognised by, whether<\/strong> but rather on how AI is used, how deep<\/strong> it is integrated into value creation, decision-making systems, and organizational logic.<\/p>\n The path to AI-native mature operations does not begin with full speed ahead, but with strategic clarity. Companies do not need to overhaul everything immediately. However, they must start by asking the right questions and consciously shaping the key levers.<\/p>\n Not only optimising existing services, but also examining how business models, services and customer interaction are changing.<\/p>\n\t Consciously design responsibility, traceability, and human judgement in an AI-driven decision-making world.<\/p>\n\t Develop processes, roles, incentive systems, and data logics in such a way that humans and AI can collaborate effectively.<\/p>\n\t Many companies currently find themselves between a desire for innovation and feeling overwhelmed. They are experimenting with AI, seeing initial results, and simultaneously sensing that the real change goes deeper. This is precisely why a framework is needed that extends beyond individual use cases.<\/p>\n AI-native maturity offers such a framework. It helps understand AI not as a collection of new tools, but as a structural driver of a broader transformation. Crucially, this change does not remain abstract. It becomes concrete in the way companies create value, organize decisions, and further develop their operating model.<\/p>\n The next phase of the AI transformation will be led by companies ready to rethink their own organizations. Companies that want to become AI-native must therefore fundamentally redesign three aspects:<\/p>\n That is precisely where the real challenge and equally the great opportunity lie.<\/p>\n AI-native maturity starts with the right questions, about value creation, for instance, the role of human expertise, or about leadership, responsibility, and trust.<\/p>\n If you wish to discuss these questions further for your company, the authors would be happy to hear from you.<\/strong><\/p>\n Stephan Weber, Daniel Ehmann and Stefan Schmautz look forward to exchanging perspectives \u2014 practical, strategic and closely connected to the real challenges organizations are facing.<\/p>\n\t\t\t\n\t\t\t\t\t\tContact\n\t\t\t\t\t<\/a>\n <\/p>","protected":false},"excerpt":{"rendered":" AI is not only changing processes, but also value creation, decisions and operating models. Our latest article shows what companies now need to rethink strategically.<\/p>","protected":false},"author":4,"featured_media":7185,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_title":"Von KI-Nutzung zu KI-Nativit\u00e4t: Was Entscheider jetzt wissen sollten","_yoast_wpseo_metadesc":"Von KI-Nutzung zu KI-Nativit\u00e4t: Erfahren Sie, was Entscheider jetzt zu Value Creation, Decision Making und Operating Model wissen m\u00fcssen \u2013 und warum KI mehr als Effizienz bedeutet.","_pipeline_managed":"","footnotes":""},"categories":[108,41],"tags":[272,274,265,268,275,273,270],"class_list":["post-7278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-adex-partners","category-services","tag-ai-first-vs-ai-native","tag-decision-making-mit-ki","tag-ki-native-organisation","tag-ki-nutzung-vs-ki-nativitaet","tag-ki-transformation-unternehmen","tag-operating-model-ki","tag-value-creation-mit-ki"],"blocksy_meta":[],"acf":[],"_links":{"self":[{"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/posts\/7278","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/comments?post=7278"}],"version-history":[{"count":10,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/posts\/7278\/revisions"}],"predecessor-version":[{"id":7293,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/posts\/7278\/revisions\/7293"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/media\/7185"}],"wp:attachment":[{"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/media?parent=7278"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/categories?post=7278"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.adexpartners.com\/en\/wp-json\/wp\/v2\/tags?post=7278"}],"curies":[{"name":"WP","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Book](\"https:\/\/www.adexpartners.com\/wp-content\/uploads\/AdEx_Partners_Buch_KI-native_Beratung_AdobeStock_1813571367.webp\" "\\"Book")
\n\t<\/figure>\n\tAvailable in shops now<\/u>
\n<\/a><\/h6>\n\tThree fields that decision-makers must now rethink<\/strong><\/h3>\n\t
\n
1<\/h6>\n
Value Creation: AI doesn't just change how quickly value is created, but how value is generated at all<\/strong><\/h4>\n
\n
\n
\nBut How do we redesign value creation when AI becomes part of its fundamental logic?<\/strong>\n\t3<\/h6>\n
Operating Model: AI requires redefined processes, roles, and organisational structures<\/h4>\n
\n
\n
2<\/h6>\n
Decision Making: AI needs a new decision-making architecture<\/h4>\nA second field that is being fundamentally changed by AI is the way in which decisions are made.
\nIn many organisations, decision preparation has evolved historically: information is collected, analysed, condensed, and then brought to decisions along formal hierarchies. AI initially accelerates this logic. However, with increasing maturity, it also changes it.
\nWhen AI creates models, simulates options, assesses risks, or generates recommendations for action, the decision-making architecture shifts. Decisions become more data-rich, faster, and partly more decentralised. At the same time, the demands for traceability, accountability, and judgement increase. This is precisely why AI-nativity is also a leadership issue.
\nThe central challenge then is not only: How do we use AI for better decision preparation?<\/strong> It is: Where and how does human judgement remain consciously effective?<\/strong>\n\n
\n
The result<\/h3>\n
Der eigentliche Shift von \u201eAI-First“ zu \u201eAI-Native“<\/h3>\n
AI-First.<\/strong><\/h5>\n
\n
AI-native maturity<\/strong><\/h5>\n
\n
\n\t\t\tWhat decision-makers should do by way of concrete action\t<\/h2>\n\t
\n\t\t\tThree priorities to get started\t<\/h4>\n\t
1. Questioning Value Creation<\/h5>\n
2. Structured Decision Making<\/h5>\n
3. Resetting the Operating Model<\/h5>\n
Why this is relevant now<\/h3>\n
\n\t\t\tConclusion\t<\/h3>\n\t
\n
\n\t\t\tThinking further through exchange\t<\/h2>\n\t
![\"\"](\"https:\/\/www.adexpartners.com\/wp-content\/uploads\/AdEx_Partners_Stephan_Weber-652x800-Kopie-2-e1768209209484.webp\")
\n Stephan Weber \n Partner\n <\/a>\n <\/a>\n ![\"\"](\"https:\/\/www.adexpartners.com\/wp-content\/uploads\/AdEx_Partners_Daniel_Ehmann-652x800-Kopie-3-e1768224897266.webp\")
\n Daniel Ehmann \n Partner\n <\/a>\n <\/a>\n ![\"Stefan](\"https:\/\/www.adexpartners.com\/wp-content\/uploads\/AdEx_Intelligence_Stefan_Schmautz-652x800.webp\")
\n Stefan Schmautz \n Partner & Head of AI\n <\/a>\n <\/a>\n\n\n\n