Europe faces a critical choice in the race for artificial intelligence (AI). As the technology promises to reshape economies worldwide, policymakers are caught between two competing narratives. Optimists envision AI as the catalyst for a new productivity boom, potentially adding several percentage points to annual growth (Baily et al. 2023). Sceptics warn that adoption barriers, skill gaps, and uneven diffusion may limit gains and exacerbate inequality (Acemoglu 2024, Filippucci et al. 2024, Gambacorta and Shreeti, 2025). For Europe, the stakes are particularly high: while the continent boasts world-leading AI researchers and industrial capacity, it lags behind the US and China in developing new AI technologies (Cornelli et al. 2023). Recent studies suggest that AI could widen cross-country income gaps, with benefits concentrating in advanced economies that are better prepared to adopt and integrate these technologies (Cazzaniga et al. 2024, Gambacorta et al. 2025, Hennig and Khan 2025).
Yet robust firm-level evidence on AI’s actual effects in Europe remains scarce. Do European firms that adopt AI genuinely become more productive? Does AI destroy jobs or augment workers? Are the benefits shared broadly, or do they concentrate among larger, better-resourced companies? In a recent study (Aldasoro et al. 2026), we provide the first causal evidence on how AI adoption affects productivity and employment across more than 12,000 European firms.
Europe’s AI paradox
Europe’s position in the global AI landscape is paradoxical. On various innovation metrics, the continent falls behind. The EU trails the US not only in the absolute number of AI-related patents but also in AI specialisation – the share of AI patents relative to total patents. This innovation gap translates into differences in firms’ readiness to adopt AI, as measured by the IMF’s AI preparedness index, which assesses countries based on digital infrastructure, human capital, innovation capacity, and regulatory frameworks (Cazzaniga et al. 2024).
However, when it comes to actual deployment, the picture is more nuanced. Drawing on the European Investment Bank Investment Survey (EIBIS), we find that on average, AI adoption levels are similar in the EU and the US. Notably, important heterogeneity emerges beneath the surface. Financially developed EU countries – such as Sweden and the Netherlands – match US adoption rates, with around 36% of firms using big data analytics and AI in 2024. In contrast, firms in less financially developed EU economies, such as Romania and Bulgaria, lag substantially behind, with adoption rates around 28% in 2024. Figure 1 illustrates this divide, showing how the gap has persisted and even widened in recent years.
Figure 1 Use of big data analytics and AI by country groups
Notes: Average share of firms reporting that they use AI by country groups, controlling for firms’ sector. The error bars represent 95% confidence intervals. EU countries are grouped based on an index of financial development using financial market data from 2015 to 2023 and consisting of two composite indicators: (i) financial market size and integration, and (ii) financial market depth (see Betz et al., 2026). Source: EIBIS 2019-2024.
Adoption also varies dramatically by firm size. Among large firms (more than 250 employees), 45% have deployed AI, compared with only 24% of small firms (10 to 49 employees). This echoes classic patterns in technology diffusion (Comin and Hobijn 2010): larger firms possess the resources, technical expertise, and economies of scale needed to absorb integration costs. AI-adopting firms are also systematically different – they invest more, are more innovative, and face tighter constraints in finding skilled workers. These patterns suggest that simply observing which firms adopt AI and comparing their performance could yield misleading results, as adoption itself is endogenous to firm characteristics.
Isolating AI’s causal effect
To credibly identify the causal effect of AI on productivity, we develop a novel instrumental variable strategy, inspired by Rajan and Zingales’ (1998) seminal work on financial dependence and growth. Their key insight was that industry characteristics measured in one economy – where they are arguably less affected by local distortions – can serve as an exogenous source of variation when applied to other countries.
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