# Artificial Intelligence Software Adoption in Manufacturing Companies
## 2 Literature Review
### AI Adoption in the Organizational and Technological Classification Context  
In the past, it has been shown that there is a positive correlation between the size of a company and the likelihood of introducing new technologies [50; 51; 52]. Since AI is regarded as a new technology, it is logical to assume that larger companies will lead in the adoption of AI. This assumption is supported by recent studies that show that larger companies are more likely to not only prepare strategies for AI implementation, but also to follow through and implement AI [53; 54; 55]. It is assumed that greater financial resources and more human capital due to better implemented databases for AI training are mainly responsible for greater adoption. Although [PERSON] et al. suggest that company size is a poor predictor of AI implementation [54], other studies suggest otherwise. Therefore, we develop the following hypothesis:  
**H2.**: _Company size is related to the adoption of AI in manufacturing companies._The industrial sector in which a company operates often determines the pace of technology adoption and reflects the different product characteristics, production conditions and technological contexts within similar groups of firms [56]. Previous research has shown that there are industry-specific differences in the adoption of AI technology. The high-tech and automotive industries, known for their early adoption of digital technologies and advanced robotics, are leading the adoption of AI [57]. Industry size also appears to correlate positively with the level of AI adoption, suggesting that technology intensity within sectors influences their readiness and ability to adopt AI technologies [57]. In contrast to previous assumptions, [PERSON] et al. found that individual industrial sectors do not contribute significantly to the adoption of AI technologies [54]. They believe that this is partly due to the design of their study and argue that direct measurement of product and production characteristics is a better approach to determine AI adoption. However, since it is generally assumed that industry sectors that fall into higher technology intensity categories are more likely to adopt new technologies, we develop the following hypothesis:  
**H3**: _Technology intensity is related to the adoption of AI in manufacturing companies._  
The supply chain is a larger network of companies that collaborate in adding value to the final product or service [58]. It is therefore subject to constant decision-making in terms of supplier selection, inventory management, logistics and other complex challenges [59; 60]. Some researchers believe that AI will be able to address these challenges and autonomously perform various tasks throughout the entire supply chain [61]. Most of the research focuses on the potential applications and benefits of AI in supply chain management [62]; however, some have attempted to define the driving forces and critical success factors that determine the adoption of AI in organizations [63; 64]. Although there is limited research exploring the role of companies in the supply chain (e.g., whether it is an OEM producer or a pure supplier) in AI implementation outcomes, [PERSON] et al. conducted semi-structured interviews with ten German OEM manufacturers to examine the elements of successful AI implementation [65]. Since no clear relationship between the role of the company and the implementation of AI could be proven, we develop the following hypothesis:  
**H4**: _The company's role in the supply chain as a supplier or OEM producer is related to the adoption of AI in manufacturing companies._