Perceived AI Capabilities: AI and Human Powered Risk Management and Strategic Risk Management in the Insurance Industry
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Universiti Malaysia Sarawak (UNIMAS)
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This study investigates the integration of Artificial Intelligence (AI) into strategic risk management within Malaysia’s insurance industry, focusing on perceived AI capabilities and AI-human collaboration. As the sector undergoes rapid digital transformation, understanding how AI is perceived and deployed in high-stakes decision-making becomes crucial. This study addresses a research gap concerning the limited exploration of AI-human collaboration in strategic risk contexts. While traditional models such as TAM, TAM2, and TAM3 emphasize perceived usefulness and ease of use, they often neglect critical elements such as trust, explainability, and contextual intelligence—factors essential in regulated industries. Empirical research from Malaysia’s insurance sector also remains scarce, particularly regarding professionals’ views on AI’s strategic role. The objective is to evaluate whether perceived AI capabilities and AI-human collaboration contribute meaningfully to strategic risk management. Adopting a quantitative research design, data were collected from 132 risk management professionals using a structured questionnaire. SPSS was used to conduct reliability testing, normality testing, and multiple regression analysis. The findings reveal that neither perceived AI capabilities nor AI-human collaboration had a statistically significant effect on strategic risk management, challenging traditional assumptions in TAM, TAM2, and TAM3. Theoretically, the study questions the predictive power of perceived usefulness and ease of use in complex strategic environments, while highlighting the importance of trust and contextual intelligence. Practically, it informs insurers and policymakers that AI tools must be supported by human expertise, organizational change, and leadership. Future research should consider moderating variables such as organizational culture, regulatory pressures, and AI maturity to better understand AI adoption in the insurance sector. These findings offer a foundation for refining adoption models and promoting ethical, collaborative AI deployment in high-risk industries.