AI as a human complement in the labour market: comparing employees’ AI competence in finance, healthcare, and public services

Abstract

The rapid expansion of artificial intelligence (AI) is reshaping labour markets and transforming professional tasks across sectors. While AI adoption is accelerating, little is known about employees’ AI competence outside the IT domain. This study examines self-assessed AI competencies among employees in three sectors undergoing significant digital transformation, finance, healthcare, and public services, using a cross-sectional survey (N = 560). Respondents assessed their proficiency across ten AI sub-competences based on an adapted Bloom’s Taxonomy scale. Descriptive statistics, Kruskal–Wallis tests, and exploratory factor analysis were applied to identify underlying structures of AI sub-competence. Results show that overall AI competence levels are low: half of employees report no use of AI or only simple task use. Public service employees rated their competencies highest, but differences between sectors were statistically significant for 8 of the 10 sub-competences. Across sectors, the most developed skills were information retrieval, text generation, and translation, while advanced competences, such as data analysis, automation, and multimodal AI functions, were rated lowest. Factor analysis revealed two competence clusters, reflecting a gradient from widely used text-based functions to more complex or specialized AI skills. These findings indicate early-stage AI competence development across all three sectors and highlight the need for targeted training, especially in higher-complexity AI skills. The results provide an empirical baseline for monitoring competence development and support sector-specific strategies to prepare the workforce for increasingly AI-enabled environments.

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