For small and medium-sized enterprises (SMEs), AI offers enormous potential for scaling and increasing efficiency, but for these companies in particular there is no alternative to using GAFA services because they themselves lack, for example, machine learning knowledge or the necessary data to train algorithms. An analytics marketplace could solve these challenges.
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The discussion on the use of artificial intelligence (AI) often revolves around the optimization of processes or the recognition of patterns in huge amounts of […]
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In order to position itself in the FinTech market with an AI-based application, a company needs a deep understanding of the AI FinTech market, i.e. both the existing applications and the business models that have developed around these applications. In the first part of this blog post, we used the AI Application Taxonomy, a tool to describe the properties of an AI application in more detail, to analyze a total of 79 AI applications from 75 FinTechs and to identify four overarching application archetypes. In the second part, we now focus on the business models within which these applications are used and the resulting positioning options for financial institutions.
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Assessing the strategic opportunities that the use of Artificial Intelligence (AI) provides a company with requires a sound understanding of AI-based applications. Such an understanding includes not only knowledge of the possible functionalities of AI-based applications (Dietzmann & Alt, 2020), but also knowledge of the characteristics that describe these functionalities in more detail (e.g. the speech input for the speech recognition functionality can be received as an acoustic signal or be available digitally in the form of a video). Combined, these two aspects enable a detailed analysis of existing AI-based offerings, on the basis of which positioning opportunities vis-à-vis competitors in the target market can be identified. The Competence Center Ecosystems has therefore not only developed the periodic table of artificial intelligence but also the “AI Application Taxonomy”, which contains the currently observable characteristics of AI-based applications. In the current and first part of the blog post we introduce the taxonomy and then analyze a sample of European AI FinTechs.
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Scientists are people who work theoretically, are very correct and often withdrawn – so the widespread impression. Far from it, as I was able to experience at the 53rd Hawaii International Conference on System Sciences (HICSS): From a keynote on “bullshit” in the scientific literature to findings on artificial intelligence (AI) in game development and the future of work, exciting, very diverse and above all very practice-oriented topics were addressed. In the following I give a short summary.
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