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Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives

Headshot of Salome Baslandze
Salomé Baslandze Research Economist and Associate Adviser
Photo portrait of Zach Edwards
Zach Edwards Richmond Fed
Photo portrait of John Graham
John Graham Duke University's Fuqua School of Business
Photo portrait of Ty McClure
Ty McClure Economic Research Analyst
Photo portrait of Michael Sparks
Michael Sparks Economic Research Analyst
Headshot of Brent Meyer
Brent Meyer Vice President and Senior Economist
Photo portrait of Sonya Ravindranath Waddell
Sonya Ravindranath Waddell Federal Reserve Bank of Richmond
Photo portrait of Daniel Weitz
Daniel Weitz Survey Director

Summary

Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.

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Working Paper 2026-4

Abstract: We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI.

JEL classification: O33, D22, J24

Key words: artificial intelligence, productivity, technological change, labor markets, occupations

https://doi.org/10.29338/wp2026-04


The authors appreciate the excellent research assistance by Kathleen Barrow, Akshara Bassi, Mac Gilliam, and Ayush Vats. They thank participants in the Federal Reserve Bank of Atlanta brown bag seminar and the Duke University brown bag seminar for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Atlanta, Federal Reserve Bank of Richmond, and the Federal Reserve System.

Salomé Baslandze is with the Federal Reserve Bank of Atlanta and CEPR. Ty McClure, Brent Meyer, Michael Sparks, and Daniel Weitz are with the Federal Reserve Bank of Atlanta. Zachary Edwards and Sonya Ravindranath Waddell are with the Federal Reserve Bank of Richmond. John R. Graham is with the Fuqua School of Business at Duke University and NBER.

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