International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Artificial Intelligence Chatbots in Action: Optimizing Benefits Enrollment in Public Administration

Author(s) Venkatarama Reddy Kommidi, Sudheer Chennuri
Country United States
Abstract This article examines the implementation of artificial intelligence-powered chatbots in public sector benefits enrollment processes, focusing on their potential to streamline operations, enhance user experience, and reduce administrative burdens. Through a comprehensive analysis of case studies in
healthcare and social security benefits programs, we demonstrate that chatbots can significantly improve efficiency, accuracy, and accessibility in enrollment procedures. Our findings indicate a 30-40% reduction in processing times and a marked increase in application completion rates. However, challenges related to
data privacy, system integration, and user acceptance persist. This article contributes to the growing body of literature on digital transformation in public administration by providing empirical evidence of chatbots' effectiveness in benefits enrollment. We conclude that while chatbots offer promising solutions
to longstanding issues in public sector service delivery, their successful implementation requires careful consideration of technical, ethical, and user-centric factors. Our study has important implications for policymakers and public administrators seeking to leverage AI technologies to enhance public service
efficiency and accessibility
Keywords Keywords: Chatbots, Public Sector Benefits, Enrollment Optimization, Artificial Intelligence, Administrative Efficiency
Field Computer
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-31
Cite This Artificial Intelligence Chatbots in Action: Optimizing Benefits Enrollment in Public Administration - Venkatarama Reddy Kommidi, Sudheer Chennuri - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29630
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.29630
Short DOI https://doi.org/g8p2tb
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