Abstract

This study investigates how barriers related to skilled employees, technological infrastructure, and data quality interact to shape artificial intelligence maturity in organizations operating in the Netherlands. Drawing on the AI Capability Maturity Model, data were collected through a cross-sectional survey of 108 professionals across diverse sectors and analyzed using Partial Least Squares Structural Equation Modeling. The results indicate that barriers related to skilled employees exert the strongest influence: they directly hinder AI maturity and intensify perceived barriers in technological infrastructure and data quality. While technological infrastructure barriers were positively associated with higher levels of data quality barriers, neither infrastructure nor data quality barriers showed a significant direct effect on AI maturity. Mediation analyses suggest that barrier effects are predominantly direct rather than indirect. Taken together, these findings support a sociotechnical perspective on AI maturity, highlighting the pivotal role of workforce capabilities in AI implementation and underscoring the need for organizations to align technical investments with sustained employee development in order to advance AI maturity.

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