# Artificial Intelligence Software Adoption in Manufacturing Companies
## 6 Conclusions  
The aim of our study was to investigate the diffusion of AI tools in manufacturing companies in the three selected countries and the potential influence of company size, role, technological sector and I4.0 readiness on the likelihood of AI implementation. The following main conclusions can be made:  
* Company size does not contribute to a higher likelihood of AI adoption. * Technology intensity does not contribute to a higher likelihood of AI adoption. * The role of the manufacturer in the supply chain does not contribute to a higher likelihood of AI adoption. * Higher I4.0 readiness contributes to the likelihood of AI adoption. Our study has several contributions. First, it contributes to the current state of knowledge about the use of AI in manufacturing. It shows that the share of manufacturing companies using AI is quite low. It also confirms some of the established findings of [PERSON] et al. [54]. Although it may initially appear that larger companies have more resources and knowledge in terms of adopting new technologies, this is not the case with AI. Even companies that fall into the high technology intensity category do not use AI more frequently than companies in other categories. This study also makes a small contribution to the area of supply chain management. It shows that the role of the company in the supply chain in relation to the adoption of AI is not statistically significant. The final contribution of this study looks at the relationship between the readiness of companies for I4.0 and the likelihood of AI adoption. It confirms that companies that have reached a higher level of I4.0 readiness (in our case, they use more I4.0-enabling technologies) are more likely to use AI. This study offers some limited managerial implications. Based on previous findings, it is still assumed that companies with more know-how and more resources are more likely to adopt new technologies. These findings challenge the previous assumption and suggest that pure organizational characteristics do not matter in the context of AI adoption. Therefore, managers or consultants should not rely solely on company resources when adopting AI. However, the positive relationship between AI use and the selected I4.0 readiness model shows that a higher level leads to a higher likelihood of AI use. Since the selected I4.0 model consists only of technologies that are believed to be enablers of I4.0, our results imply that certain technologies have a greater impact on the likelihood of AI use. This is an important managerial implication since it motivates managers to analyze their current processes and identify missing technologies that would enable easier implementation of AI. However, for a more detailed overview of AI-enabling technologies and factors, further studies need to be conducted. Our results suggest that a manufacturing company still has about a 14% chance of using AI even without adopting enabling technologies. This suggests that there are other, non-technological factors contributing to AI adoption that merit further investigation. However, the more of these technologies and combinations of them that are implemented, the higher the likelihood of AI adoption. A common denominator of these technologies is their role in data generation. ERP systems consolidate different business processes into a unified system and generate data from different departments such as finance, supply chain, manufacturing and human resources. PLM systems monitor data throughout a product's lifecycle, including specifications, design changes, manufacturing processes and performance metrics. Digital visualization technologies generate data by capturing real-time information from various sources and transforming it into visual formats such as dashboards, 3D models and simulations. Mobile devices facilitate real-time access to information and improve communication and collaboration between employees, generating data.