Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce

William G. Resh, Yi Ming, Xinyao Xia, Michael Overton, Gul Nisa Gurbuz and Brandon De Bruhl

Papers from arXiv.org

Abstract: This study applies a multi-stage Retrieval-Augmented Generation system, which grounds large language model assessments in curated evidence, to evaluate generative AI's near-term impact on knowledge, skills, and abilities across several hundred federal occupational series. Each occupation is scored along three dimensions of complementarity (AI enhances existing competencies), augmentation (AI requires workers to transform competencies), and substitutivity (AI replaces human tasks). Results reveal that generative AI primarily complements and augments federal roles, with limited substitution potential. Even occupations scoring highest on substitutivity reflect partial automation of routine subtasks rather than position elimination. These findings caution against blunt workforce reductions premised on displacement and instead support targeted reskilling, competency-based planning, and human-AI collaboration. The framework is adaptable to other organizations with structured competency models and job documentation.

Date: 2025-03, Revised 2026-10
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