The AI workforce and firm maturity: old firms, new tech
Pattanaporn Chatjuthamard, Pornsit Jiraporn, Pandej Chintrakarn, Woraphon Wattanatorn · Journal of Business Economics · 11 Jun 2026
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Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
Using resume and job-posting data on U.S. firms (Babina et al. 2024), the authors find that older firms employ a smaller share of AI workers: a one-standard-deviation increase in firm age lowers the AI-worker share by 5.2%. R&D and infrastructure investment mitigate this, board composition matters, and firms that integrate AI talent show higher market valuation and operational efficiency.
Abstract
Abstract We examine how firm age affects the adoption of artificial intelligence (AI) within the workforce. Drawing on a novel dataset developed by Babina et al. (2024) combining resume and job posting data for U.S. firms, we find that older firms often encounter difficulties integrating AI talent, possibly due to entrenched practices, outdated systems, and resistance to change. Specifically, a rise in firm age by one standard deviation reduces the share of AI workers by 5.2%. However, investments in R&D and infrastructure can mitigate these challenges, enabling modernization and attracting skilled AI professionals. Importantly, our analysis reveals that board composition, particularly the presence of female and minority directors, impacts AI adoption. In older firms, risk-averse decision-making and slower consensus-building among diverse boards may hinder the integration of AI talent. Firms that successfully integrate AI talent achieve significant improvements in market valuation and operational efficiency. Our study provides practical insights for managers and policymakers aiming to guide mature organizations through the challenges of technological transformation. Our findings relate to internal AI capability development and may not fully capture firms’ reliance on external AI solutions.
About this publication
Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
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- 2026 (4 months ago)
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