Inteligência Artificial no Brasil: análise da utilização e das barreiras empresariais pós-2022

Abstract

This study aims to analyze the dynamics of Artificial Intelligence (AI) adoption in the Brazilian business landscape, as well as to identify some of the barriers that have hindered its implementation since 2022. The study is structured on the theoretical framework of the Schumpeterian and Neo-Schumpeterian approaches, which contextualize innovation as a driver of economic growth and a determinant of competitiveness. Furthermore, the literature review also includes the origin, concepts, paradigms, and diffusion of AI, positioning it as a key element in the Information Age. The research methodology combines a literature review on innovation and technological diffusion with documentary research from sources such as the TIC Empresas 2023 survey from the Regional Center for Studies for the Development of the Information Society (CETIC) and the PINTEC Semestral 2022 from the Brazilian Institute of Geography and Statistics (IBGE). From these, a secondary data analysis is conducted to outline the Brazilian and global AI scenarios, thereby examining the use of the technology with a focus on the manufacturing industry. Among the results, the study reveals that AI adoption in Brazil is incipient and heterogeneous. The general application of the technology remains limited, even in sectors with greater technological incorporation, which indicates a gap in relation to more advanced economies. This gap is attributed to barriers and challenges similar to those studied globally, such as high implementation costs and a shortage of qualified human capital. However, overcoming these obstacles in Brazil is more complex, given the fragility of its National Innovation System and a historical trajectory of low technological investment. Given this scenario, this research highlights the importance of strategic actions to bridge this gap, emphasizing the need for innovation incentives, specialized labor training, and the strengthening of public-private partnerships.