PREPRINT
Assessing the Impact of Generative Artificial Intelligence on the U.S. Federal Workforce through a Competency-Based Approach Item Info
- Title:
- Assessing the Impact of Generative Artificial Intelligence on the U.S. Federal Workforce through a Competency-Based Approach
- Authors:
- Resh, Bill Ming, Yi Xia, Xinyao Overton, Michael Gurbuz, Gul Nisa Breuhl, Brandon De
- Year:
- 2025
- Output Type:
- Preprint
- Venue:
- arXiv
- Venue Type:
- Preprint
- Status:
- Under review
- Theme:
- Data Science and AI
- Sub-theme:
- AI
- Project:
- AI and The Future of Government Work
- Abstract:
- 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.
- Topics:
- Generative AI Federal workforce Competency
- DOI:
- https://doi.org/10.48550/arXiv.2503.09637
- arXiv ID:
- 2503.09637
- Citations (OpenAlex):
- 1
- Open Access Copy:
- https://arxiv.org/pdf/2503.09637
- OpenAlex ID:
- W4416032042
- Full Citation:
- Resh, Bill, Yi Ming, Xinyao Xia, Michael Overton, Gul Nisa Gurbuz, and Brandon De Breuhl. "Assessing the Impact of Generative Artificial Intelligence on the U.S. Federal Workforce through a Competency-Based Approach." arXiv preprint arXiv:2503.09637. https://doi.org/10.48550/arXiv.2503.09637 (Under Review)
Cite this work
Under review
Reference
Resh, Bill; Ming, Yi; Xia, Xinyao; Overton, Michael; Gurbuz, Gul Nisa; Breuhl, Brandon De (2025). Assessing the Impact of Generative Artificial Intelligence on the U.S. Federal Workforce through a Competency-Based Approach. arXiv. [Under review].
View DOI record arXiv preprint
@misc{resh2025_assessing_the_impact_of_,
title = {Assessing the Impact of Generative Artificial Intelligence on the U.S. Federal Workforce through a Competency-Based Approach},
author = { Resh, Bill and Ming, Yi and Xia, Xinyao and Overton, Michael and Gurbuz, Gul Nisa and Breuhl, Brandon De },
year = {2025},
howpublished = {arXiv},
doi = {10.48550/arXiv.2503.09637},
eprint = {2503.09637},
archivePrefix = {arXiv},
url = {https://www.michaeloverton.net/research/items/assessing_the_impact_of_generative_artificial_intelligence_o.html}
}