Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved]
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 challeng...
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| Language: | English |
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F1000 Research Ltd
2025-04-01
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| Online Access: | https://f1000research.com/articles/14-452/v1 |
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| author | Sharmila Ramachandaran Bablu Kumar Dhar Zubaidi Mahalley Riska Nuraini |
| author_facet | Sharmila Ramachandaran Bablu Kumar Dhar Zubaidi Mahalley Riska Nuraini |
| author_sort | Sharmila Ramachandaran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cd1e30ece3454b5da9654da511020afd |
| institution | DOAJ |
| issn | 2046-1402 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj-art-cd1e30ece3454b5da9654da511020afd2025-08-20T03:23:59ZengF1000 Research LtdF1000Research2046-14022025-04-011410.12688/f1000research.163354.1179687Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved]Sharmila Ramachandaran0https://orcid.org/0000-0002-4569-8321Bablu Kumar Dhar1https://orcid.org/0000-0001-8768-8634Zubaidi Mahalley2Riska Nuraini3https://orcid.org/0009-0008-4346-459XFaculty of Business and Communication, INTI International University & Colleges, Nilai, Negeri Sembilan, MalaysiaBusiness Admistration Division, Mahidol University International College, Mahidol University, Salaya, Nakhon Pathom, ThailandFaculty of Business and Communication, INTI International University & Colleges, Nilai, Negeri Sembilan, MalaysiaFaculty of Business and Communication, INTI International University & Colleges, Nilai, Negeri Sembilan, MalaysiaBackground 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.https://f1000research.com/articles/14-452/v1Artificial Intelligence Business Intelligence Malaysian Insurance Industry Technology-Organization-Environment Framework Resource-Based View Digital Transformationeng |
| spellingShingle | Sharmila Ramachandaran Bablu Kumar Dhar Zubaidi Mahalley Riska Nuraini Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] F1000Research Artificial Intelligence Business Intelligence Malaysian Insurance Industry Technology-Organization-Environment Framework Resource-Based View Digital Transformation eng |
| title | Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] |
| title_full | Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] |
| title_fullStr | Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] |
| title_full_unstemmed | Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] |
| title_short | Exploring the challenges of AI-driven business intelligence systems in the Malaysian insurance industry [version 1; peer review: 2 approved] |
| title_sort | exploring the challenges of ai driven business intelligence systems in the malaysian insurance industry version 1 peer review 2 approved |
| topic | Artificial Intelligence Business Intelligence Malaysian Insurance Industry Technology-Organization-Environment Framework Resource-Based View Digital Transformation eng |
| url | https://f1000research.com/articles/14-452/v1 |
| work_keys_str_mv | AT sharmilaramachandaran exploringthechallengesofaidrivenbusinessintelligencesystemsinthemalaysianinsuranceindustryversion1peerreview2approved AT bablukumardhar exploringthechallengesofaidrivenbusinessintelligencesystemsinthemalaysianinsuranceindustryversion1peerreview2approved AT zubaidimahalley exploringthechallengesofaidrivenbusinessintelligencesystemsinthemalaysianinsuranceindustryversion1peerreview2approved AT riskanuraini exploringthechallengesofaidrivenbusinessintelligencesystemsinthemalaysianinsuranceindustryversion1peerreview2approved |