Betrieb für InformationsTechnologie

Artificial Intelligence (AI) adoption poses complex, multidimensional challenges for organizations that extend beyond technical implementation. While strategic interest in AI is rising, many initiatives struggle to scale sustainably. This paper addresses the fragmented state of AI adoption research by offering a theory-driven synthesis of the key management challenges organizations face. Based on a systematic literature review and grounded in the socio-technical systems (STS) perspective, the study identifies challenges across technological, organizational, and social domains - such as data governance, organizational inertia, skill shortages, and ethical ambiguity - as mutually reinforcing. The resulting framework highlights the systemic entanglement of these barriers, underscoring the limits of isolated interventions. This study contributes to Information Systems (IS) research by conceptualizing AI adoption as a socio-technical transformation and providing an integrative typology of adoption challenges. The findings offer a foundation for future empirical research and guide strategic decision-makers in navigating the organizational complexity of AI integration.

Durch die Moglichkeit, rekombinante Antikorper herzustellen, anstatt auf die Produktion von Antikorpern in einem Versuchstier oder in Hybridomen angewiesen zu sein, wird der molekulare Aufbau von Antikorpern steuerbar. Das kann beispielsweise genutzt werden, um Antikorper mit verschiedenen Molekulgrosen und Formaten herzustellen (Frenzel et al. 2013).