Optimizing Workforce Efficiency in the United States (U.S.) Federal Sector: The Role of Predictive Analytics and AI in Human Resource (HR) Decision-Making

Kemisola Modinat Kasali
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Kemisola Modinat Kasali: College of Business, Health, and Human Services, School of Business, Department of Management, Marketing, and Technology, University of Arkansas at Little Rock, USA.

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 3, 1062-1068

Abstract: The U.S. federal government is undergoing a critical workforce transformation, driven by initiatives under the Department of Government Efficiency (DOGE) to optimize resource allocation and reduce bureaucratic redundancies. This shift poses significant challenges in maintaining service quality amid workforce reductions but offers opportunities to enhance operational effectiveness and public service delivery through technological innovation. Predictive analytics and artificial intelligence (AI) offer strategic, data-driven solutions to enhance human resource (HR) decision-making that enables federal agencies to proactively forecast attrition, enhance workforce planning, and improve operational efficiency. This article examines the integration of AI in federal HR operations, assesses its potential to streamline hiring, retention, and performance management. By evaluating current policy frameworks and presenting innovative AI-driven workforce strategies, this article contributes to the advancement of government modernization. The recommendations provide concrete steps for implementing AI governance frameworks, establishing public-private partnerships, and developing inclusive workforce analytics systems to ensure AI adoption enhances rather than disrupts public service delivery. This article also proposed the introduction of novel approaches to AI integration in federal HR which includes a hybrid human-AI decision support system and an innovative cross-agency AI knowledge sharing platform. In addition, this paper addresses critical challenges that include data privacy concerns, algorithmic bias risks, and the ethical implications of AI-driven decision-making in the federal workforce context that provides a balanced perspective on both opportunities and limitations of these technologies.

Date: 2025
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