The crucial industry-based aspects of AI adoption : An empirical analysis of AI adoption to understand how and why it differs between different industries with focus on the HRM function

Abstract: 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.