Analyzing the Interaction between Learning, Artificial Intelligence, and Capabilities: A Study of the Sources of Organizational Dynamism
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Abstract
This study examines how the adoption of artificial intelligence (AI) reshapes learning processes in small and medium-sized enterprises (SMEs) and its implications for the evolution of path dependence and dynamic capabilities. Using a qualitative multiple case study design, the research draws on data from AI-adopting SMEs in Yogyakarta, collected through semi-structured interviews, observations, and organizational documents. The analysis combines within-case analysis and cross-case comparison to identify underlying mechanisms and recurring patterns. The findings reveal that AI plays a dual and paradoxical role in organizational evolution. On one hand, AI enhances efficiency, data-driven decision-making, and sensing capabilities, thereby reinforcing existing learning patterns and self-reinforcing mechanisms. On the other hand, AI acts as an exogenous technological shock that can disrupt established trajectories, expand interpretive capacity, and enable path-breaking processes. However, this transformation is not uniformly beneficial. While AI strengthens analytical capabilities, it may also deepen cognitive rigidity and reinforce new forms of path dependence if not critically managed. These findings suggest that AI should not be viewed solely as a tool for operational efficiency, but as a mechanism for cognitive and strategic transformation. This study contributes by conceptualizing AI as both a path-reinforcing and path-breaking force, integrating insights from path dependence and dynamic capabilities literature.
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