Predictive AI and productivity growth dynamics: evidence from French firms

Luca Fontanelli, Mattia Guerini, Raffaele Miniaci and Angelo Secchi

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: While artificial intelligence (AI) adoption holds the potential to enhance business operations through improved forecasting and automation, its relation with average productivity growth remain highly heterogeneous across firms. This paper shifts the focus and investigates the impact of predictive artificial intelligence (AI) on the volatility of firms' productivity growth rates. Using firm-level data from the 2019 French ICT survey, we provide robust evidence that AI use is associated with increased volatility. This relationship persists across multiple robustness checks, including analyses addressing causality concerns. To propose a possible mechanisms underlying this effect, we compare firms that purchase AI from external providers ("AI buyers") and those that develop AI in-house ("AI developers"). Our results show that heightened volatility is concentrated among AI buyers, whereas firms that develop AI internally experience no such effect. Finally, we find that AI-induced volatility among "AI buyers" is mitigated in firms with a higher share of ICT engineers and technicians, suggesting that AI's successful integration requires complementary human capital.

Keywords: Artificial intelligence; productivity growth volatility; coarsened exact matching (search for similar items in EconPapers)
Date: 2025-04-07
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Persistent link: https://EconPapers.repec.org/RePEc:ssa:lemwps:2025/12

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