Aplicação da inteligência artificial no gerenciamento de projetos: orientações para as práticas

• Ano de defesa: 2024
• Autor(a) principal: Saturno, Alexander Guilherme
• Orientador(a): Rabechini Junior, Roque
• Banca de defesa: Rabechini Junior, Roque , Vils, Leonardo , Sátyro, Walter Cardoso , Zawadzki, Patrick , Teston, Sayonara de Fátima
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação em Gestão de Projetos
• Departamento: Administração
• País: Brasil
• Palavras-chave em Português: gerenciamento de projetos; inteligência artificial; framework
• Palavras-chave em Inglês: project management; artificial intelligence; framework
• Área do conhecimento CNPq: CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/3586

The adoption of new technology as a business strategy has always been a significant factor for organizations, requiring their executives to make assertive decisions. This is because adopting new technology can boost an organization’s business. When companies began to consider artificial intelligence (AI) as a strategic business opportunity, a period of significant changes in organizational strategies emerged. As with any innovation, the initial stage of adoption lacks sufficient research to propose further advancements. It is believed that this technology will be widely disseminated. In the context of project management, AI gains prominence as its potential evolves. In order to advance this research area, this thesis proposes practical guidelines for applying AI in project management through a framework. The scope of this thesis comprises four studies that form a sequential line of investigation. In the first study, the contributions of AI to project management were examined through a systematic literature review and bibliometric analysis. The literature highlights the greatest effectiveness of AI in the following areas: cost management, schedule management, risk management, earned value management, performance evaluation and forecasting, and project success. The second study explored the factors that condition the adoption of AI in project management, leading to the identification of nine conditioning factors: strategic alignment between AI and project objectives; adequate technological infrastructure; training and skill development; visionary leadership; an organizational culture that supports innovation; risk management and compliance; effective communication and stakeholder engagement; continuous feedback and learning; and performance measurement and analysis. Subsequently, these factors were validated with experts and practitioners through interviews. The third study investigated the behavior of project professionals concerning the adoption of AI in project management by means of a survey focused on the areas identified in the first study. After understanding which areas of project management benefit most from this technology and identifying and validating the factors that condition the adoption of AI in project management, the fourth study proposed a framework to guide the application of AI in project management.