Atribuição de tarefas no serviço público: uma solução a partir da inteligência artificial generativa

• Ano de defesa: 2024
• Autor(a) principal: Perlingeiro, João Kaian dos Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Gestão Organizacional (Mestrado Profissional)
• Palavras-chave em Português: atribuição de tarefas; gestão pública; eficiência; automatização; task assignment; public management; efficiency; automation; administração; eficiência (serviço público); CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::ADMINISTRACAO PUBLICA::ADMINISTRACAO DE PESSOAL; Administração; Serviços públicos - Brasil; Inteligência artificial; Serviço público - Produtividade do trabalho - Brasil; ODS::ODS 9. Indústria, Inovação e infraestrutura - Construir infraestrutura resiliente, promover a industrialização inclusiva e sustentável, e fomentar a inovação.; ODS::ODS 11. Cidades e comunidades sustentáveis - Tornar as cidades e os assentamentos humanos inclusivos, seguros, resilientes e sustentáveis.
• Link de acesso: https://repositorio.ufu.br/handle/123456789/44800; https://doi.org/10.14393/ufu.di.2025.5525

This research addresses the assignment of tasks in the Municipal Department of Works (SMO) of Uberlândia, proposing a solution to optimize this process using artificial intelligence and human-machine interaction. The research is set within the context of public management, highlighting the importance of efficient allocation of human resources, especially in a scenario where resources are limited. The Brazilian public administration, historically marked by patrimonialist practices, has evolved into more bureaucratic and efficient models, such as New Public Management (NPM), which emphasizes efficiency, competitiveness, and decentralization. The SMO, responsible for overseeing public constructions, faces challenges related to task allocation, which directly impacts productivity and service quality, demonstrating the need for a more effective approach to distributing responsibilities. The study identifies problems in task assignment that can lead to delays, increased costs, and stress among employees. The lack of a structured system for this assignment results in asymmetries in workload and difficulties in monitoring task progress. The research suggests that automation can mitigate these issues, promoting a fairer and more efficient allocation of tasks based on criteria. It is understood that the research is relevant not only for the SMO but also for other public institutions facing similar challenges. And as tecnichal-technological product, there is the proposition of an automated task assignment process, which can serve as a model for improving processes in various spheres of public administration, contributing to the efficiency of resource management and the timely delivery of quality services to the population.