The increasing importance of artificial intelligence on manufacturing competitiveness is a prevalent topic in contemporary studies. Thereby, the potential of AI extends across companies of all sizes. However, small and medium-sized enterprises in particular see major challenges in AI implementation. As a result, our study determined the variations in the perceived barriers to AI realization among different company sizes and the obstacles that notably impede the willingness to implement AI. To achieve this, a factor analysis and structural equation modeling were carried out. Our findings highlight that the different challenges for AI implementation can be divided into four groups economic, technological, social, and political. Thereby the perception of economic challenges varies the most among distinct enterprise sizes, whereas the social challenges correlate extremely weakly. Regarding the willingness to implement AI, political challenges show the strongest negative correlation, while technological challenges, contrary to prior assumptions, lack significant influence.

Despite significant advancements in medical artificial intelligence (AI) systems, these technologies are prone to mistake in their predictions. These mis- takes can significantly affect medical experts’ willingness to continue using these systems. To mitigate potential discontinuation, existing research indicates that providing additional information alongside predictions, can lessen negative out- comes like discontinuation. Given the potential impact on users’ information pro- cessing, we hypothesize that AI explanations, detailing the system's decision- making process, can also influence the likelihood of discontinuing use after an AI mistake. Through an online experiment with medical experts (n=227), we demonstrate that such explanations can influence medical experts’ information processing and, consequently, mitigate the adverse effects on the actual discon- tinuation of AI systems following a mistake.

Small and medium-sized enterprises (SME) thrive on knowledge-intensive operations, making effective knowledge management critical to their success. Contemporary developments, such as semantic search applications (SSA) leveraging artificial intelligence (AI), promise significant benefits for knowledge management. However, the adoption of such AI-based applications in the context of SME remains still notably limited. Building upon the groundwork laid by previous research on the socio-technical dimensions of AI adoption, we thus investigate the adoption of SSA in a multiple case study within the German manufacturing sector. Hence, contextualizing the adoption of SSA in SMEs using a grounded theoretical framework. Our findings highlight the intricate interplay of organizational readiness, external support, and user satisfaction in facilitating SSA adoption. We believe our framework holds significant potential to guide the adoption of SSA and thus offers valuable insights for navigating the complexities of harnessing the potential of AI-based applications for effective knowledge management in SMEs.