Unpacking AI Implementation : A study of organizational dynamics in digital transformation

Sammanfattning

Background: Artificial intelligence (AI) is rapidly becoming a key component of digital transformation across industries. While large enterprises often have the resources and expertise to navigate this transition, small and medium-sized enterprises (SMEs) frequently face structural, financial, and knowledge-related barriers. As a result, AI implementation in SMEs tends to be fragmented and underexplored, especially in the Swedish context. Purpose: This study aims to explore how AI is currently being implemented in Swedish SMEs, with a specific focus on the socio-technical dynamics involved. Using an extended version of Leavitt’s Diamond Model—which incorporates People, Processes, Technology, and Data—the study investigates socio-technical aspects such as organizational readiness, internal motivations, and practical challenges related to AI adoption through the research question:How does socio-technical factors influence the implementation of AI in Swedish SMEs? Method: A qualitative research design was employed, combining semi-structured interviews with professionals experienced in AI projects and a structured analysis of purposively sampled LinkedIn posts. Data was coded abductively using the extended socio-technical framework, allowing for both theory-driven and emergent insights. Conclusion: The findings reveal that most AI initiatives in SMEs are focused on process optimization rather than radical transformation. Successful adoption is driven by motivated individuals, clear AI visions, and strategic alignment across organizational layers. However, challenges such as limited technological infrastructure, fragmented planning, and lack of access to internal data remain significant. The study contributes to theory by expanding socio-technical models of digital transformation and offers practical implications for SMEs seeking to support AI adoption.