Proceedings of the 70th International Academic Conference, Madrid

MEASURING ORGANIZATIONAL AI MATURITY: DEVELOPMENT AND VALIDATION OF A MULTIDIMENSIONAL MATURITY FRAMEWORK

FILIP STOVÍČEK, FEDERICO BANDA, ANDRIANA BAZIUK

Abstract:

Artificial intelligence (AI) has become a key driver of digital transformation, enabling organizations to improve decision-making, operational efficiency, innovation, and competitiveness. Despite the growing adoption of AI technologies, organizations differ substantially in their ability to successfully implement, govern, and scale AI initiatives. Existing studies emphasize the importance of technological, organizational, strategic, and governance-related capabilities; however, there is still no universally accepted and empirically validated framework for measuring organizational AI maturity. This study aims to develop and empirically validate a multidimensional Organizational AI Maturity Model that captures the readiness of enterprises for artificial intelligence implementation. The proposed framework conceptualizes AI maturity through seven dimensions: AI culture, AI strategy, AI organization, AI technology, AI data readiness, AI ethics, and AI privacy. The empirical analysis is based on data collected within the national AI-ImpactSK project, comprising a sample of 816 enterprises operating in Slovakia. To validate the proposed model, reliability analysis, exploratory factor analysis, and hierarchical regression analysis were employed. The results demonstrate excellent internal consistency of the measurement instrument (McDonald’s ω = 0.91). Exploratory factor analysis identified a single dominant latent factor explaining 59.36% of total variance, supporting the conceptualization of organizational AI maturity as a coherent multidimensional construct. All seven dimensions exhibited substantial factor loadings ranging from 0.66 to 0.81, confirming their significant contribution to the overall maturity framework. Hierarchical regression analysis revealed that organizational size characteristics, represented by turnover and employee count, are statistically significant predictors of AI maturity; however, their explanatory power remains limited (R² = 0.03), suggesting that internal organizational capabilities play a more important role than structural characteristics alone. The study contributes to the literature by providing one of the first empirically validated AI maturity frameworks based on large-scale enterprise data from a Central and Eastern European context. The proposed model offers both researchers and practitioners a structured instrument for assessing organizational preparedness for artificial intelligence and identifying areas requiring further development.

Keywords: Artificial intelligence, AI maturity, Organizational AI maturity, Digital transformation

DOI: 10.20472/IAC.2026.070.017

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