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

Abstract

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.