How do the barriers that prevent or hinder the applicability of artificial intelligence impact its use in an insurance company

Edval da Silva Tavares, Michel Leardini and Marcelo Schneck de Paula Pessoa

Innovation & Management Review, 2024, vol. 22, issue 1, 13-46

Abstract: Purpose - The purpose of this paper is to report on how the barriers that prevent or hinder the applicability of artificial intelligence (AI) impact its use in an insurance company. Design/methodology/approach - Through bibliographic research, this paper maps the literature to identify the barriers to the use of AI by companies. After this, a qualitative single-case study, with descriptive characteristics, is conducted in an insurance company to verify if it is ready to adopt AI. Secondary data based on surveys of information technology consultancies were also used to provide greater consistency to the research. Findings - This analysis showed that investments in these kinds of projects encounter organizational, financial and social barriers, such as a culture not focused on innovation, a lack of strategic IT alignment, a lack of knowledge of the potential or even the ability to carry out an adequate feasibility analysis. Based on the barriers presented by the case and AI initiatives, the researched company is using AI for automation and execution rather than using its transformative potential to be an agent of change across the organization. Most of the barriers to the adoption of AI are organizational rather than technical. Research limitations/implications - The limitation of this study is it presents a single-case study, but this involves local cultural problems because companies are not open to supplying internal information to external researchers. Originality/value - The value of this article lies in examining a real case in Latin America, raising the barriers to the adoption of AI, which has a different environment from Europe and the USA, presenting a company that has the freedom to choose local or international technologies.

Keywords: Artificial intelligence; Alignment; Model; Applicability; Insurance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:inmrpp:inmr-03-2022-0042

DOI: 10.1108/INMR-03-2022-0042

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