IDEAS home Printed from https://ideas.repec.org/p/ags/feemwp/355806.html
   My bibliography  Save this paper

Predictive AI and productivity growth dynamics: evidence from French firms

Author

Listed:
  • Fontanelli, Luca
  • Guerini, Mattia
  • Miniaci, Raffaele
  • Secchi, Angelo

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.

Suggested Citation

  • Fontanelli, Luca & Guerini, Mattia & Miniaci, Raffaele & Secchi, Angelo, 2025. "Predictive AI and productivity growth dynamics: evidence from French firms," FEEM Working Papers 355806, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemwp:355806
    DOI: 10.22004/ag.econ.355806
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/355806/files/NDL2025-11-1.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.355806?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:feemwp:355806. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.