Agilidade Potencializada: integração da inteligência artificial em projetos acadêmicos de pesquisa e extensão

• Ano de defesa: 2024
• Autor(a) principal: Bruno Mateus Dias Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; Programa de Pós-Graduação em Inovação Tecnológica e Biofarmacêutica; UFMG
• Palavras-chave em Português: Metodologias ágeis; Gestão de projetos; Pesquisa científica; Extensão universitária; Rrede neural; Métricas de fluxo; Inovação; Agilidade; Inovações tecnológicas; Empreendedorismo; Propriedade intelectual; Desenvolvimento organizacional; Inteligência artificial; Aplicações educacionais
• Link de acesso: http://hdl.handle.net/1843/77608

This dissertation addresses the application of agility and artificial intelligence (AI) tools in academic research and extension projects, with the main objective of improving management processes and enhancing value delivery. The primary justification for this research is based on the need to improve the management models currently applied in universities, aiming for greater efficiency, predictability, and quality in projects. The research follows an explanatory and exploratory approach, analyzing data collected from two partner initiatives: the "1000 Future Scientists" project and the Laboratory of Inverse Problems and Chemical Kinetics (PINCQ). The research results, referred to as the As-Is phase, revealed significant insights into the application of agile methodologies and AI in these projects. It was identified that combining these approaches can offer a new value delivery model, assisting teams throughout the entire process. Additionally, maturity metrics were proposed to assess the effectiveness of agile practices and the adoption of a neural network. These metrics are highly recommended for university extension and research projects, as they provide accurate information and facilitate informed decision-making. Key project outcomes include the creation of a model for measuring agile and technological maturity, the implementation of a Kanban flow and board with flow metrics, the adoption of agile ceremonies, and the introduction of an OKR system through workshops. Regarding the AI component, a neural network model was developed to analyze the project environments, achieving 77% accuracy on test data and 100% on training data. Based on these findings, a To-Be action plan was created, guiding academic teams in transitioning to a more agile and efficient management approach. This plan incorporates the best practices identified during the research and sets specific goals and actions to improve collaboration, continuous value delivery, and process optimization. In addition, automated spreadsheets were developed to monitor and analyze the proposed metrics. The practical results offer clear directions for improving the efficiency, quality, and predictability of these projects, contributing significantly to the advancement of knowledge and innovation in the academic environment.