Speaker
Description
The present paper aims to assess the efficiency with which European Union Member States transform the adoption of artificial intelligence at the enterprise level, under ESG constraints, into sustainable economic, social and institutional outcomes. In the current context, characterized by the acceleration of digital transformation processes, artificial intelligence is widely recognized as a key driver of economic competitiveness. However, its contribution to sustainable performance is not uniform, as it is conditioned by structural and institutional factors specific to each economy.
From a methodological perspective, the research employs the Data Envelopment Analysis (DEA) method, applying a basic radial model, output-oriented and characterized by variable returns to scale. The analysis focuses on the 27 European Union Member States, considered as comparable decision-making units, and is based on data for the year 2024, given their availability at the time of the analysis. The set of variables is designed to capture both the digital dimension and sustainability-related constraints. Accordingly, the input variables include the level of artificial intelligence adoption in enterprises, greenhouse gas emissions and energy intensity, while the output variables reflect sustainable performance through the share of renewable energy in final energy consumption, the employment rate and a composite governance indicator, constructed as the average of control of corruption, government effectiveness and rule of law.
The results highlight significant differences across the analyzed countries in terms of the efficiency of transforming inputs into sustainable outputs. A total of 16 countries are positioned on the efficiency frontier, while the remaining economies exhibit varying degrees of inefficiency. Nevertheless, the relative proximity of efficiency scores to unity suggests that most inefficiencies are moderate and could be reduced through improved policy coordination and more effective use of available resources.
Furthermore, the findings indicate that a higher level of artificial intelligence adoption in the business sector does not automatically translate into superior sustainable performance. Efficiency is significantly influenced by the quality of the institutional environment and the ability of economies to integrate digital technologies within coherent development strategies. In this respect, the results underline the importance of complementarity between digitalization and governance, suggesting that the benefits of digital transformation are maximized in the presence of effective institutions and well-articulated public policies.
Given the aforementioned aspects, the study emphasizes that digital transformation, particularly through the adoption of artificial intelligence at the enterprise level, can contribute to sustainable performance only if supported by an adequate institutional framework and strategic policy integration.
