Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry
Issued Date
2025-01-01
Resource Type
eISSN
20461402
Scopus ID
2-s2.0-105009538504
Journal Title
F1000research
Volume
14
Rights Holder(s)
SCOPUS
Bibliographic Citation
F1000research Vol.14 (2025)
Suggested Citation
Ramachandaran S., Mahalley Z., Nuraini R., Dhar B.K. Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry. F1000research Vol.14 (2025). doi:10.12688/f1000research.163354.1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111143
Title
Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry
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Author's Affiliation
Corresponding Author(s)
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Abstract
Background: Integrating Artificial Intelligence (AI) with Business Intelligence (BI) systems in the insurance industry holds the potential for enhanced operational efficiency, strategic decision-making, and improved customer experiences. However, the Malaysian insurance sector faces numerous challenges in realizing this potential, including organizational resistance, skill shortages, regulatory complexities, and financial constraints. This study explores the specific challenges encountered in the adoption of AI-driven BI systems within the Malaysian insurance industry. Methods: Using an integrated framework that combines the Technology-Organization-Environment (TOE) model and Resource-Based View (RBV), this research examines the internal and external factors that impact AI adoption. A qualitative case study approach was employed, involving in-depth interviews with technical experts, middle management, and senior leaders from key industry players. Thematic analysis of the data identified significant barriers to AI adoption, such as organizational resistance, lack of skilled personnel, and the complexities of navigating regulatory frameworks. Results: The findings provide a deep understanding of the key challenges faced by Malaysian insurers and highlight areas that require attention, such as leadership commitment, workforce upskilling, technological infrastructure improvements, and policy advocacy. Conclusion: This study adds to the limited academic literature on AI-driven BI adoption in emerging markets and offers practical insights to insurers for overcoming these challenges. By addressing these obstacles, this research contributes to the broader discourse on digital transformation in the insurance sector, offering valuable recommendations for overcoming hurdles in AI adoption while maintaining compliance and ensuring customer-centric approaches.