Gestão do Conhecimento, Propriedade Intelectual e Transferência de Tecnologia
Autores
Nome
1 - Amanda Rafaela Stein UNIVERSIDADE FEEVALE (FEEVALE) - Novo Hamburgo
2 - Moema Pereira Nunes UNIVERSIDADE FEEVALE (FEEVALE) - Programa de Pós-Graduação em Administração
3 - Cristine Hermann Nodari UNIVERSIDADE FEEVALE (FEEVALE) - Mestrado Acadêmico em Administração
4 - Cristiane Froehlich UNIVERSIDADE FEEVALE (FEEVALE) - Programa de Pós_Graduação Mestrado em Administração
Reumo
The digital era requires shorter response times and quick decision-making for companies to remain competitive in the market, which involves using new technologies such as Artificial Intelligence. With the help of AI systems, knowledge can be stored, analyzed, created, and shared more effectively across companies. At the same time, KM presents a perspective for implementing AI strategies in organizations. Empirical studies on the use of AI for KM are still scarce.
Empirical studies on AI and KM, such as Nadan et al. (2024), Gupta et al. (2022), and Mittal et al. (2023), allow an understanding of fragmented elements of GC. Empirical gaps remain to identify whether and/or how AI can contribute to KM in its different stages. This study aims to fill this gap by analyzing how the use of AI contributes to KM processes in an organization. Therefore, the question arises: How can the use of AI contribute to KM processes in an organization?
The theoretical foundation of this research is divided into three themes. The first is Artificial Intelligence, which exposes this technology and its evolution over time. Next, the rationale for Knowledge Management and its operation is presented. The third part comprises Artificial Intelligence and Knowledge Management and presents the evolution of studies that associate the two themes.
The development of this article included carrying out a single case study, which is descriptive research with a qualitative approach. The institution uses traditional AI and generative AI in different channels that serve its employees and members. Among the differentiators of its AI is the reinforcement of service efficiency, given the continuous growth in the number of members. Data collection occurred through in-depth interviews, documentary research, and participant observation. All information was tabulated based on the stages of the GC process, supported by the Microsoft Excel tool.
Data analysis is divided into the four phases of the Knowledge Management process - Creation, storage and retrieval, sharing, and application of knowledge. For each one, evidence of AI in the KM process of the organization under study was identified, establishing a comparison with the potential identified by Jarrahi et al. (2023).
The study allowed us to identify that Artificial Intelligence can effectively contribute to all stages of the Knowledge Management process, confirming the propositions of Jarrahi et al. (2023). Thus, potential applications were verified in the organization, even though its managers had not previously considered AI as a KM tool.
Borges, A. F. S. et al. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.
Jarrahi, M. H. et al. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99.
Mittal, A. et al. (2023). Role of artificial intelligence in knowledge management: an empirical study of industry experts using stepwise multiple regression. International Journal of Electronic Finance, 12(4), 403-422.