ENTWICKLUNG EINES IMPLEMENTIERUNGSFRAMEWORKS FÜR DEN UNTERNEHMENSWEITEN EINSATZ GENERATIVER KÜNSTLICHER INTELLIGENZ IM DEUTSCHEN MITTELSTAND

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Absztrakt (kivonat)

This dissertation develops a process-oriented and empirically validated framework for the systematic implementation of generative Artificial Intelligence in small and medium-sized enterprises. The framework's practical applicability is demonstrated through a marketing use case. In doing so, this work answers the central research question of how the implementation process can be designed for the German Mittelstand and which strategic approaches and underlying technologies can be utilized. A three-stage qualitative approach provided the methodological foundation: First, an initial framework draft was developed from 39 expert interviews and a document analysis using qualitative content analysis. This draft was subsequently reviewed and finalized through validation interviews with the experts (member checking). The result of this process is a validated implementation framework that divides the implementation process into five phases: (1) Identification of Problems and Opportunities, (2) Conceptualization and Planning, (3) Prototyping or Piloting, (4) Integration and Rollout, and (5) Evaluation and Further Development. The framework offers more than purely technical guidance by integrating strategic, technological, organizational, and cultural dimensions. Implementation success depends on three strategic prerequisites: a symbiotic governance logic, a clear technology and data strategy, and the continuous enablement of employees. The primary scientific contribution of this work lies in the development of one of the first empirically validated frameworks that structures the implementation of generative Artificial Intelligence specifically for small and medium-sized enterprises. It bridges the gap between established adoption theories and the concrete need for action in practice by providing a directly applicable, phase-based process model. For companies, the framework serves as a practical guide that allows for an approach tailored to their individual circumstances. Future research can build on this by developing industry-specific adaptations or by quantitatively testing the identified success factors.

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