Trust in AI: Perspectives of C-Level Executives in Brazilian Organizations
Resumo
Context: With the advancement of Artificial Intelligence (AI) and its increasing integration into business processes, the trust of top management in Brazilian companies has become a crucial issue. Business leaders must be aware of the challenges and opportunities associated with adopting AI in their operations. A lack of understanding and knowledge about the capabilities and limitations of AI can lead to hesitations and concerns from top management regarding its use. Goal: This work aims to identify the main challenges preventing C-level executives from fully trusting AI and its applications within their organizations in the Brazilian context. Additionally, a reference guide is proposed to help top management better understand how AI can be effectively and ethically integrated into their business strategies. Method: We conducted a survey with 30 business leaders from various sectors to understand their perceptions of trust in AI and their concerns regarding its implementation. Results: The results revealed that the main obstacles faced by top management in Brazilian companies were the lack of understanding about AI’s capabilities and its ethical implications. Therefore, it is imperative for business leaders to invest in education and awareness about AI, seeking to understand its benefits and challenges. Only then will they be able to make informed decisions and fully trust AI solutions to drive innovation and sustainable growth in their organizations and the improvement of organizational processes.
Palavras-chave:
Organizational Leaders, C-Level Executives, Ethical Implications in AI, Limitations of AI, Governance
Publicado
05/11/2024
Como Citar
GONÇALVES, Clendson Domingos; MENESCAL, Eduardo de Paoli; MENDONÇA, Fábio Lúcio Lopes de; CANEDO, Edna Dias.
Trust in AI: Perspectives of C-Level Executives in Brazilian Organizations. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 23. , 2024, Bahia/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 147–157.