Artificial intelligence promises to reshape economies worldwide, but firm-level evidence on its effects in Europe remains scarce. This column uses survey data to examine how AI adoption affects productivity and employment across more than 12,000 European firms. The authors find that AI adoption increases labour productivity levels by 4% on average in the EU, with no evidence of reduced employment in the short run. The productivity benefits, however, are unevenly distributed. Medium and large firms, as well as firms that have the capacity to integrate AI through investments in intangible assets and human capital, experience substantially stronger productivity gains.
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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.
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.
We extend this logic to the firm level. For each EU firm in our sample, we identify comparable US firms – matched on sector, size, investment intensity, innovation activity, financing structure and management practices. We then assign the AI adoption rate of these matched US firms as a proxy for the EU firm’s exogenous exposure to AI. Because US firms operate under different institutional, regulatory and policy environments, their adoption patterns capture technological drivers that are plausibly independent of EU-specific factors. Rigorous propensity-score balancing tests confirm that our matched US and EU firms are virtually identical across key observable characteristics, validating the identification strategy. Our analysis draws on survey data from EIBIS combined with balance sheet data from Moody’s Orbis.
Productivity gains without job losses
Our results reveal three key findings. First, AI adoption causally increases labour productivity levels by 4% on average in the EU. This effect is statistically robust and economically meaningful
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Second, and crucially, we find no evidence that AI reduces employment in the short run. While naïve comparisons suggest AI-adopting firms employ more workers, this relationship disappears once we account for selection effects through our instrumental variable approach. The absence of negative employment effects, combined with significant productivity gains, points to a specific mechanism: capital deepening. AI augments worker output – enabling employees to complete tasks faster and make better decisions – without displacing labour
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Third, AI’s productivity benefits are far from evenly distributed. Breaking down our results by firm size reveals that medium and large companies experience substantially stronger productivity gains than their smaller counterparts (see Figure 2). This differential effect reflects the role of scale in absorbing AI integration costs and accessing complementary assets – data infrastructure, technical talent, and organisational capacity to redesign workflows. The finding raises concerns about widening productivity gaps between firms and regions, particularly given Europe’s industrial structure, which is dominated by small and medium-sized enterprises.
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Source: How AI is affecting productivity and jobs in Europe | CEPR
Robin Edgar
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