Navigating Artificial Intelligence Pre-adoption Phase in Industrial Organization : Case study of a Swedish Automotive Manufacturing Firm

Sammanfattning

Artificial Intelligence (AI) holds great potential to transform industrial operations, yet many manufacturing firms remain in the early stages of AI adoption. Unlike post-implementation studies, this research captures managerial perceptions and actions during the pre-adoption phase in a Swedish automotive manufacturing firm. The study employs an inductive, qualitative single-case design to explore how managers make sense of an emerging technology under uncertainty, and how they intervene through change management initiatives to prepare the organization before any formal adoption decisions. Empirical data were gathered through in-depth interviews, a focus group, and workplace observations, and analyzed using the Gioia method. The analysis yielded second-order themes and ultimately three aggregate dimensions: (1) Strategic Vision and Ambidextrous Change Strategy, (2) Socio-Technical Readiness Alignment, and (3) Contextual Rigidity and Risk Governance. Collectively, these dimensions illustrate how managers develop a strategic vision while balancing exploration and efficiency, align technological and organizational readiness, and address contextual constraints (e.g., limited expertise and regulatory uncertainty). Based on these insights, the study proposes an integrated conceptual model for navigating the AI pre-adoption phase. This model highlights how effective managerial practices translate initial interest in AI into structured preparatory activities around three aggregate dimensions. It underscores that this early stage requires active leadership engagement, cross-functional coordination, and early capability-building to shape organizational readiness for AI. Consequently, the findings offer practical implications for managers in manufacturing firms, suggesting that proactive leadership and proactive change management during the pre-adoption phase can lay a strong foundation for successful AI implementation. By concentrating on the pre-adoption stage, this study addresses a key gap in the literature and provides real-world insights into how manufacturing firms prepare for AI. Overall, the findings portray the pre-adoption phase as an active process of organizational alignment driven by managerial initiatives, rather than a passive waiting period.