Exploring the Impact of Artificial Intelligence (AI) on Business Decision-Making: A Case Study of Insurance Companies in Kitwe
TL;DR
The findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage, and AI has an overwhelmingly positive impact on decision quality and speed.
Abstract
This study investigates the impact of Artificial Intelligence (AI) on business decision-making within selected insurance companies in Kitwe District, Zambia. Employing a quantitative research design, the study collected data from 50 respondents across three insurance companies. The findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage. AI application is highly concentrated in core operational areas such as risk assessment (80%), claims processing (70%), and regulatory compliance (70%). Conversely, AI is entirely absent in customer-facing and strategic functions, including customer service, marketing, investment and finance, and product development. Individual AI tool usage is notably low, with 70% of respondents indicating infrequent or no personal use, highlighting a disconnect between company-level adoption and individual utilisation. Despite limited adoption, AI has an overwhelmingly positive impact on decision quality (80% reporting improvement) and speed (70% reporting acceleration), with no respondents indicating any negative impact. While a large majority (80%) express equal confidence in AI-supported decisions compared to traditional methods, the perceived impact on error reduction is limited. The most significant implementation challenges are unclear return on investment (98%), integration issues with legacy systems (95%), data privacy and security concerns (90%), and a lack of skilled personnel (80%). Other challenges include high implementation costs (60%), employee resistance (60%), and lack of strategic direction (50%). The study recommends that insurance organisations develop comprehensive AI strategies aligned with business objectives, invest in data infrastructure and governance, address skills gaps through training and development, and adopt phased implementation approaches.