# Artificial Intelligence Software Adoption in Manufacturing Companies
## 5 Discussion
An initial analysis revealed that the proportion of AI users in the three categories is quite similar. Further tests with logistic regression confirmed that the role of the manufacturer does not contribute to a higher likelihood of AI use. Contrary to what might be expected, the use of AI appears to be robust across both types of companies, but the type and focus of AI applications may differ. Suppliers are more likely to focus their AI efforts on improving production processes, quality control and supply chain management, while OEMs are using AI to manage the complexity of large-scale manufacturing and extensive supply chains. For suppliers, AI provides a competitive advantage by improving production quality and operational efficiency. For OEMs, AI's ability to streamline complex manufacturing processes and optimize supply chains means higher productivity and cost savings. Nevertheless, based on our findings, we can reject H3 and conclude that companies that are solely suppliers of parts or components to other companies are no more likely to use AI than OEM manufacturers or companies that are both suppliers and OEMs. We originally hypothesized that the role of the manufacturer plays a role in the adoption of AI, but the regression results prove otherwise. Therefore, we reject this hypothesis and conclude that the role of a company is not statistically significantly related to the AI usage and that there are other, more important factors that influence it. Regarding the technology intensity of manufacturing companies, it was hypothesized that the higher the technology intensity of a company, the more likely it is to use AI. It is assumed that high-tech industries such as electronics will adopt AI technologies faster, as they are inherently familiar with advanced technological tools and allocate more financial resources to research and development. Conversely, low-tech industries, such as textiles and basic materials, are assumed to be slower to adopt AI. While the results of our descriptive statistics show that manufacturing companies in the high-tech group have the highest proportion of AI users (30% of companies in this group use AI), the logistic regression rejects the hypothesized relationship. The introduction of AI obviously brings advantages for high-tech and low-tech companies. For manufacturing companies in low-tech industries, AI offers the opportunity to optimize existing processes and improve quality control, thereby increasing competitiveness. The implementation of AI solutions can help these industries achieve greater efficiency and cost savings, and make them more resilient in a rapidly evolving market environment. In addition, the use of AI in high-tech industries can drive significant innovation, streamline complex manufacturing processes and improve product design. Finally, the relationship between the level of I4.0 readiness and AI usage was examined. For the purposes of our study, an I4.0 readiness model was used that was developed based on previous EMS studies. This model is based only on technologies that are seen as enablers of I4.0. Since AI is part of a broader concept of I4.0, we were interested in whether there is a relationship between a higher level of readiness (and thus more enabling technologies) and the use of AI. Our analysis confirms a positive relationship and shows that the higher the level of readiness, the greater the likelihood of using AIin at least one production area. Therefore, we can accept the hypothesis that the level of I4.0 readiness is statistically significantly related to the use of AI. This means that the use of AI solutions in manufacturing companies is closely related to the readiness for Industry 4.0 (I4.0), especially with the use of other (enabling) digital technologies used in the company. In other words, manufacturing companies that were better prepared for I4.0 were more likely to adopt AI solutions that can increase their efficiency, productivity and competitiveness.