Background: Digitalized operations have become praxis for organizations of all shapes and sizes and while the digital tools keep developing, certain aspects make it difficult for certain organizations to keep up. One of the most modern, efficient, and sought-after digital tools is artificial intelligence (AI). With increased efficiency and decreased human errors, it has become the foundation for operations within organizations all over the world. One of these types of operations is the human resource management (HRM) process found in each organization. And while some industries have had a much easier task in adopting AI into their HRM function, others have had more difficulty. Purpose: While there are a few theories of what might affect the process of AI adoption, these are quite old and often known to managers. Yet, certain industries have a hard time adopting AI tools within the HRM function while other industries have fully implemented automated systems that have revolutionized the way they operate. The purpose of this study is to understand why and how AI adoption differs between these industries when it comes to similar operations such as the HRM function. Method:        The methods of this study were based on the grounded theory (GT) as a basis to analyze eight different organizations within the financial industry and telecom industry. Through semi-structured interviews, different aspects could be illustrated as crucial when it comes to the possibility to adopt AI within existing operations.  Conclusion: The results of this study show that the AI-maturity of the organization and industry alike play a crucial part in successfully adopting AI. But the institutional pressures and the available resources are equally important to understand to be able to successfully adopt AI. These two aspects form the outcome of AI adoption and the number of complex combinations that can be formed highlights why AI adoption differs between organizations and industries alike.

Det finns en växande trend och ett ökat intresse för implementeringen av AI i olika branscher. Potentialen som AI har är stor med möjligheten att hantera stora mängder digital information och effektivisera processer. Det ökade intresset att implementera AI inkluderar byggbranschen som är i behov av ökad produktivitet. Branschen befinner sig i ett tidigt skede med implementeringen av AI, således finns det ett behov att studera forskningsområdet vidare. Denna kvalitativa fallstudie syftar till att öka kunskapen om AI-mognad i byggproduktion genom att utforska mognaden och dess påverkande faktorer. Studien görs i samarbete med ett stort entreprenadföretag där fyra fokusgruppsintervjuer och sex enskilda semistrukturerade intervjuer genomfördes. Alla intervjudeltagare är anställda på företaget och har en koppling till byggproduktionen. Analysen av empirin görs utifrån de teoretiska ramverken Diffusion of Innovation (DOI) och Technology- Organization- Environment (TOE). Studiens resultat visar att det finns en begränsning i kunskapen och användningen av AI. Generellt finns intresse att använda AI i större utsträckning i byggproduktionen och förväntningar att AI kommer bidra till positiva förändringar. Fokus i dagsläget ligger på att utforska nyttan av AI och uppmuntra anställda att använda teknologin. Det finns en övergripande positiv attityd gentemot AI, däremot är kunskapen och användningen begränsade vilket indikerar att AI-mognaden är låg i byggproduktionen. De faktorer som har identifierats påverka AI-mognaden är: outvecklad teknologi, säkerhet, storlek, kännedom, kultur, produktionsskedet, IT-leverantörer, marknadsläget, konkurrenter, kunder, underentreprenörer och byggbranschen. Denna studie är ett tidigt bidrag inom det relativt nya forskningsområdet och ökar förståelsen för utvecklingen av AI i byggbranschen. / There is a growing trend and increasing interest in the implementation of AI in various industries. The potential of AI is vast, with an ability to manage large amounts of digital information and streamline processes. The rising interest in implementing AI includes the construction industry, which needs increased productivity. The industry is in an early stage of AI implementation, hence, there is a need to further study this research area. This qualitative case study aims to increase the knowledge about AI maturity in production by exploring the maturity and its influencing factors. The study is conducted in collaboration with a large contractor company in which four focus group interviews and six individual semi-structured interviews were conducted. All interviewees are employed at the company and are connected to the production. The empirical analysis is based on the theoretical frameworks Diffusion of Innovation (DOI) and Technology- Organization- Environment (TOE).  The study’s results show that there is a limitation in the knowledge and the use of AI. Generally, there is an interest in using AI to a greater extent in the production and expectations that AI will contribute to positive changes. Currently, the focus is on exploring the benefits of AI and encouraging employees to use the technology. There is an overall positive attitude towards AI, however, knowledge and usage are limited, indicating that the AI maturity is low in the production. The factors identified as influencing the AI maturity are: Underdeveloped Technology, Security, Size, Awareness, Culture, The Production Phase, IT Suppliers, The Market Condition, Competitors, Customers, Subcontractors, and The Construction Industry. This study is an early contribution to the relatively new research area and increases the understanding of the development of AI in the construction industry.