Influence of company size and AI implementation challenges in manufacturing companies

Abstract The increasing importance of artificial intelligence (AI) in manufacturing competitiveness is widely recognized. However, many firms face diverse challenges in their implementation. A nuanced understanding of these hurdles is therefore needed. To identify the main challenges associated with AI implementation and to show differences in assessment across various company sizes, a survey and an analysis of variance (ANOVA) were conducted. The research highlighted that the lack of knowledge, the duration, application selection, and limited time resources pose the greatest challenges for manufacturing companies. In this context, the study reveals a shift in how companies perceive AI knowledge gaps: instead of a general lack of AI understanding, they now prioritize evaluating the cost–benefit effect as the main knowledge gap. Moreover, the duration and time resource limitations emerge as a substantial impediment to implementation, which has so far received too little attention in research work. Entrepreneurs should be aware of these issues and initially start with simple, low-cost applications, instead of spending a long time searching for optimized evaluation methods. Thereby, smaller companies, in particular, should avoid overestimating the implementation costs. Our study also underscores the importance of consistently distinguishing between small and medium-sized enterprises (SMEs) and large companies in future research, regarding AI implementation challenges. When addressing issues related to economic and information technology (IT) infrastructure, even this conventional division proves inadequate, necessitating a more detailed differentiation of company sizes. The findings also show that entrepreneurs need to engage in experience exchange regarding AI with enterprises of similar size.