# Artificial Intelligence Software Adoption in Manufacturing Companies
## 5 Discussion  
The results of our study show that the use of AI in the manufacturing companies is still quite low. As AI is an emerging technology, this was one of the initial expectations. About 18% of the respondents in our study can be classified as AI users, as they use at least one AI software solution in their production processes. Originally, it was assumed that larger companies were more likely to use some kind of AI-powered software. Although the results of the descriptive statistics in Table 3 indicate that larger companies more frequently use AI software, the logistic regression revealed that company size is not a significant predictor of AI usage. An important finding of our study is, therefore, that the use of AI software in manufacturing companies appears not to be statistically significantly related to the company size. This finding suggests that small, medium and large companies are equally likely to adopt AI technologies, which is in contrast to some previous studies that indicated a potential advantage for larger companies due to their greater resources and capabilities [53; 54; 55]. Our findings reject the main findings of [PERSON] et al. [54], who argue that company size positively influences AI implementation. Instead of directly using company size as a factor, the authors measured this by the number of employees, which was further transformed using natural logarithms. In their study, they first carried out a multiple linear regression to model the relationship between different company characteristics, technological context, environmental context and the dependent variable \"AI Index\", which was calculated as a mean of values of AI intensity for three different tasks. In the first and second multiple linear regression models, it was shown that AI index increases with the number of employees. However, in this same study, [PERSON] et al. used the logistic regression to model the relationship between companies that implemented AI only at their domestic location and not at their foreign location. In this case, company size turned out to not be statistically significant. [PERSON] [69] argues that companies of all sizes are adopting AI technologies and emphasizes the role of cloud-based AI services in making these technologies accessible to smaller companies. One explanation for this finding is the increasing availability of scalable AI solutions. Cloud-based AI services, for example, offer flexible and cost-effective options for companies of all sizes. These services eliminate the need for significant upfront investment in hardware and infrastructure, making AI accessible to smaller companies. The practical implications of this finding are significant. It suggests that small manufacturing companies should not be  
Figure 1: Probability of AI usage for every I4.0 readiness level. deferred by their size when considering the use of AI. On the contrary, they can use AI to compete more effectively with larger companies by driving innovation and efficiency in their operations. As already mentioned, the emergence of cloud-based services and AI-as-a-service business models enables manufacturing companies to implement AI capabilities without the need for in-house experts or extensive infrastructure. Therefore, small and medium-sized manufacturing companies have access to cost-effective solutions that do not require significant upfront investment in necessary infrastructure. These solutions can be then tailored to specific needs while improving operational efficiency by automating routine tasks and enabling human experts to work on more complex activities. Based on our findings, we reject H2 and conclude that company size is not statistically significantly related to the use of AI. No currently available study has addressed a direct relationship between the different roles of manufacturing companies in the supply chain [21, 60] and the likelihood of AI adoption. Therefore, we investigated the relationship between the role of the manufacturer in the supply chain and the adoption of AI.