Developing microfoundations for AI Readiness in Industrial Firms : Insights from Production Planning Practices

Industrial firms increasingly recognize the strategic importance of Artificial Intelligence (AI), but many struggle with the practical and organizational challenges of preparing to adopt it. This study explores how industrial firms develop microfoundations for AI readiness, the specific routines, practices, and structures that enable capability development. The study uses production planning as an empirical setting. While production planning offers a focused context, the findings can be applicable to broader organizational functions where AI adoption is emerging. This thesis uses a qualitative, theory-driven multiple-case study approach, guided by the frameworks of Dynamic Capabilities (DC) and Organizational Learning (OL). Data was collected through semi-structured interviews at two Swedish industrial firms and analyzed thematically to identify recurring patterns and practices. The findings show that firms do not progress toward AI readiness through formalized processes as the literature claims, but instead through dynamic and informal development of microfoundations. Key microfoundations enabling AI readiness include structured experimentation, readiness evaluations, trust-building communication, experiential learning, and role-specific training. OL was found to act as a foundational learning structure that indirectly supports DC development. The study contributes by clarifying the recursive relationship between organizational learning and dynamic capabilities, and by showing how AI readiness is developed in practice as a dynamic condition. The study also highlights how emotional, cultural, and informal elements shape early AI efforts, areas often overlooked in the current literature.