Auflistung nach Autor:in "Wyrtki, Katrin"
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- ZeitschriftenartikelA Maturity Model for Assessing the Digitalization of Public Health Agencies(Business & Information Systems Engineering: Vol. 65, No. 5, 2023) Doctor, Eileen; Eymann, Torsten; Fürstenau, Daniel; Gersch, Martin; Hall, Kristina; Kauffmann, Anna Lina; Schulte-Althoff, Matthias; Schlieter, Hannes; Stark, Jeannette; Wyrtki, KatrinRequests for a coordinated response during the COVID-19 pandemic revealed the limitations of locally-operating public health agencies (PHAs) and have resulted in a growing interest in their digitalization. However, digitalizing PHAs – i.e., transforming them technically and organizationally – toward the needs of both employees and citizens is challenging, especially in federally-managed local government settings. This paper reports on a project that develops and evaluates a continuous (vs. a staged) maturity model, the PHAMM, for digitalizing PHAs as a cornerstone of a digitally resilient public health system in the future. The model supports a coordinated approach to formulating a vision and structuring the steps toward it, engaging employees along the transformation journey necessary for a federally-managed field. Further, it is now being used to allocate substantial national funds to foster digitalization. By developing the model in a coordinated approach and using it for distributing federal resources, this work expands the potential usage cases for maturity models. The authors conclude with lessons learned and discuss how the model can incentivize local digitalization in federal fields.
- ZeitschriftenartikelReady or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors(Business & Information Systems Engineering: Vol. 63, No. 1, 2021) Jöhnk, Jan; Weißert, Malte; Wyrtki, KatrinArtificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necessities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adoption and is important to successfully leverage AI’s business value. Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. The paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, the paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice.