Tereos Brazil Deepens AI Application, Empowering Operations with Data Quality Focus
Tereos Brazil is leveraging artificial intelligence as a 'copilot' for its business operations, with a strong focus on data quality and operational support. The company's IT executive reveals that Brazil is leading in AI application, particularly in the agricultural sector, even exporting services to other parts of the group. A data committee has been established, with the Brazilian team spearheading AI development and implementing a tiered data classification system to ensure security and governance.
Tereos do Brasil is moving beyond the conventional use of artificial intelligence for data collection or as an embedded feature in market solutions. Its Information Technology (IT) department is actively advancing the use of AI as a 'copilot' for the company's operational journey. The primary focus is on supporting various business areas, particularly through enhancing data quality and providing operational assistance.
According to Rodrigo Audi, the executive IT manager for the sugar and energy company, this journey, which began in 2017, has seen Brazilian operations, especially in the agricultural sector, surpass those in European countries in terms of advancement. Audi states, "We are exporting services. Tereos' technology teams in Brazil are also providing services to the group." He further adds that the company has an internal data committee, and the artificial intelligence wing is led by the Brazilian team. He explains, "I represent the information technology team, but we operate within an internal committee comprising industrial, agricultural, and information technologies. We also have a dedicated data team."
Given the sensitive nature of much of the information, Tereos classifies all materials into three distinct levels. Level A data is the most critical, essential for executive decisions made by committees. Level B encompasses managerial or departmental information, while Level C is reserved for smaller, individual analyses. Audi emphasizes, "We separate the data, and the higher the data's maturity, the more governance and security we apply to it."