Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912567824220160 |
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| author | Resh, William G. Ming, Yi Xia, Xinyao Overton, Michael Gürbüz, Gul Nisa De Bruhl, Brandon |
| author_facet | Resh, William G. Ming, Yi Xia, Xinyao Overton, Michael Gürbüz, Gul Nisa De Bruhl, Brandon |
| contents | This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data on occupational requirements across the federal government from both centralized and decentralized human resource sources, including from the U.S. Office of Personnel Management (OPM) and various federal agencies. While our preliminary findings suggest some significant shifts in required competencies and potential vulnerability of certain roles to AI-driven changes, we provide nuanced insights that support arguments against abrupt or generic approaches to strategic human capital planning around the development of generative AI. The study aims to inform strategic workforce planning and policy development within federal agencies and demonstrates how this approach can be replicated across other large employment institutions and labor markets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce Resh, William G. Ming, Yi Xia, Xinyao Overton, Michael Gürbüz, Gul Nisa De Bruhl, Brandon Computers and Society General Economics Economics I.2.7; I.2.11; I.2.1; I.2.3; I.7 This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data on occupational requirements across the federal government from both centralized and decentralized human resource sources, including from the U.S. Office of Personnel Management (OPM) and various federal agencies. While our preliminary findings suggest some significant shifts in required competencies and potential vulnerability of certain roles to AI-driven changes, we provide nuanced insights that support arguments against abrupt or generic approaches to strategic human capital planning around the development of generative AI. The study aims to inform strategic workforce planning and policy development within federal agencies and demonstrates how this approach can be replicated across other large employment institutions and labor markets. |
| title | Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce |
| topic | Computers and Society General Economics Economics I.2.7; I.2.11; I.2.1; I.2.3; I.7 |
| url | https://arxiv.org/abs/2503.09637 |