# Artificial Intelligence Software Adoption in Manufacturing Companies
## 2 Literature Review
### Industry 4.0 and AI Adoption  
Industry 4.0 (I4.0) is a complex and important concept for modern and future manufacturing. At its core, it is a collection of current concepts that cannot be classified as such. Some of these concepts include fully autonomous smart factories, cyber-physical systems (CPSs) that merge the physical and digital worlds, self-organizing manufacturing systems, new distribution and procurement systems, new systems for individualizing products and services, human-centric manufacturing systems, sustainability and resource efficiency [38]. Although it is clear what I4.0 encompasses or should encompass, there is still no common and generally accepted definition [39]. This could possibly be due to the fact that research has focused on specific, individual technologies and domain-specific applications, while other aspects, such as the role and challenges of management, have been less explored [40; 41]. Due to this uncertainty, empirical evidence has shown that companies are struggling with the concept of I4.0 and its implementation. Due to the limited understanding of the importance and benefits of I4.0, companies need support in identifying relevant strategic actions, programs and projects that enable the transition to I4.0 [42]. While high investment, cost, complexity and required knowledge are the main barriers to I4.0 adoption, the existing company-specific IT infrastructure (hardware, software, networks and connectivity of devices) and technologies (including machines and business processes) hinder the successful adoption of advanced technologies [43]. As there is no guideline for the implementation of I4.0 concepts, many different readiness and maturity models have been developed. Their main goal is to help companies successfully transition into the new era of manufacturing. These models are important for several reasons. The first reason is that they enable the assessment of current capabilities, i.e., the level of technological and digital maturity within the organization. This can help companies analyze the gaps between their current state and the desired state of I4.0 integration. The second reason is that they provide a guide for implementation. With clear steps to improve systems, processes and capabilities, companies can create strategic plans and prioritize investments in technology and innovation, which in turn ensures that resources for digital transformation are allocated efficiently. The third reason is that these models can contribute to benchmarking against industry standards due to clear criteria and maturity levels. This can lead to a better understanding of the competitive position and areas that need to be improved in order to increase operational efficiency and innovation capabilities. While the models differ in scope, focus and complexity, technology remains a key component in all frameworks. Some provide comprehensive insights into operational, people, strategic and technology implications, while others focus more on digital technologies or process automation. The level of detail varies widely, ranging from in-depth, detailed analysis to broader overviews. Each model can focus on different aspects such as technology, people, processes or strategy. However, technology forms the basis for each model as it enables detailed assessments and facilitates adaptation to evolving industry trends. The methods of the models, which come from academia, industry or consulting firms, range from rigorous empirical research to expert consensus. Some models are static, while others can be adapted to specific user inputs and changing conditions. Through integration with other standards, such as ISO, they are further adapted to broader industry practice [10; 44; 45; 46; 47]. It can be concluded that this new industrial paradigm is largely due to digitalization and the increasing connectivity of devices and machines [48]. Both concepts have a common component that they either generate or exchange: data.