Artificial intelligence driven dynamic capabilities: unpacking micro-foundational mechanisms

Purpose As artificial intelligence (AI) becomes more integrated into organisational processes, small and medium-sized enterprises (SMEs) face unique challenges and opportunities in developing dynamic capabilities for digital transformation. Despite increasing scholarly attention, most existing research focuses on large firms, leaving the microfoundations of AI-enabled transformation in resource-constrained SMEs underexplored. Addressing this gap, this study draws on a multiple-case qualitative analysis of Finnish SMEs leading in AI adoption. Design/methodology/approach This study draws on a multiple-case qualitative analysis involving 51 in-depth interviews with CEOs/founders, employees, and IT experts across 28 Finnish SMEs at the forefront of AI adoption. Findings Our findings reveal that strategic leadership framing, informal team learning routines, and trust-based human–AI interaction norms actively shape the development of sensing, seizing, and reconfiguring capabilities essential for digital adaptation. By articulating and visually mapping four empirically grounded propositions, we demonstrate that dynamic capabilities in SMEs emerge from recursive cycles of experimentation and collective learning rather than linear planning. Originality/value The study advances dynamic capabilities theory by highlighting the interplay between context, leadership, and social routines in digitally progressive but resource-limited environments. Practical implications are offered for SME managers and policymakers aiming to foster organizational agility and resilience in the evolving digital landscape.