Exploring the process of AI integration seen through a resource and capability perspective: A qualitative study of international SMEs in the manufacturing industry

Abstract

This thesis investigates the necessary resources and capabilities to integrate Artificial Intelligence (AI) in International Small and Medium-sized Enterprises (ISMEs) in the manufacturing industry. By applying theories as the Resource-Based View, Dynamic Capabilities, Absorptive Capacity, and the Technology-Organization-Environment framework, we construct our own Conceptual Framework to research the necessary internal capabilities in conjunction with external factors for ISMEs to integrate AI. This study uses a qualitative methodology and adopts an abductive approach, complemented by a multiple-case study design, where we spoke to eight ISMEs using semi-structured interviews. The research reveals that successful AI integration in ISMEs depends on a combination of firm-specific resources, such as financial resources, human resources, and data availability, alongside organizational capabilities like an innovative culture and strong absorptive capacity, this in turn makes it possible to leverage both internal and external resources. The study highlights the importance of strategic alignment between resources and capabilities, effective communication between management and board, and an environment conducive to technological adoption. As the study provides insight of facilitators and barriers of AI integration in ISMEs, this thesis offers practical implications for managers aiming to integrate AI.