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Antecedents, Components, and Consequences of Applying Artificial Intelligence in the Insurance Industry

Document Type : Original Research Paper

Authors

1 Associate Professor, Department of Business Management, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

2 PhD Student, Department of Business Management, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

3 Professor, Department of Business Management, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

Abstract

BACKGROUND AND OBJECTIVES: The integration of artificial intelligence (AI) into the insurance industry represents a major technological shift potentially capable of transforming traditional business models, operational processes, and customer engagement strategies. Despite its initial promise, AI adoption in insurance markets remains limited and uneven across regions and sub-sectors. Many insurers are still evaluating the feasibility and implications of AI technologies, while others have initiated pilot projects or partial automation systems. Scientific research on the strategic, organizational, and technological impacts of AI in insurance is still emerging, leaving a gap in understanding how AI can be effectively leveraged in this context. This study aims to address that gap by identifying the antecedents, core components, and consequences of AI utilization in the insurance industry. It also proposes a conceptual framework to guide future research and practical implementation efforts.
METHODS: This research employed a qualitative methodology grounded in an interpretive paradigm and inductive reasoning. The study was designed to address a practical problem in the insurance sector while contributing to theoretical development through the creation of a new conceptual model. Data collection was conducted using purposive and snowball sampling techniques to recruit experts from both the insurance and the information technology domains. After achieving theoretical saturation, 40 semi-structured interviews were conducted with a diverse group of participants, including senior managers, insurance company specialists, and university faculty members. The interviews were transcribed and analyzed using thematic analysis, which was carried out in three stages: open coding, axial coding, and selective coding. MAXQDA 2020 software was used to facilitate the organization and synthesis of qualitative data, leading to the extraction of key themes and the development of the study’s conceptual framework.
FINDINGS: The analysis resulted in the extraction of 566 initial codes, which were later refined and consolidated into 63 final codes. These codes were organized into three major categories: antecedents, components, and outcomes. Each category was further subdivided based on the Technology-Organization-Environment (TOE) framework. Antecedents included organizational factors such as perceived usefulness, competitive advantage, cost and time efficiency, self-efficacy, trust, fraud concerns, organizational size, and the growth of insurance claims. Technological antecedents encompassed system complexity and advancements in computational power, while environmental antecedents involved competitive pressure, market dynamics, and evolving customer expectations. The components of AI adoption were also classified into organizational (e.g., infrastructure readiness, top management support, stakeholder trust-building, employee training, organizational ethos, managerial skills, and budget allocation), technological (e.g., technological competencies, access to adoption guidelines, data quality and availability), and environmental (e.g., government support, financial assistance from banks) dimensions. The outcomes of AI adoption included both positive and negative consequences. Positive outcomes were particularly prominent and included service personalization, dynamic risk assessment, fraud detection, improved claims processing, enhanced customer service, and data-driven decision-making. These benefits contributed to greater operational agility, reduced administrative burden, and more accurate underwriting practices. Negative outcomes, although less emphasized, involved concerns related to data privacy, ethical implications of automated decision-making, potential job displacement due to automation, and the risk of over-reliance on AI systems without adequate human oversight. Overall, the findings underscore the complex interplay of internal and external factors that shape the trajectory of AI adoption in insurance. They highlight the need for a balanced approach integrating technological innovation with organizational preparedness and environmental alignment. The conceptual model developed in this study serves as a strategic tool for guiding insurers through the multifaceted process of AI implementation, ensuring that benefits are maximized while risks are proactively managed.
CONCLUSION: This study developed a comprehensive conceptual model that captures the multifaceted nature of AI adoption in the insurance industry. By systematically identifying and categorizing the antecedents, components, and consequences, the model offers a valuable tool for researchers, practitioners, and policymakers seeking to understand and navigate the complexities of AI implementation. The findings emphasize the importance of aligning technological capabilities with organizational readiness and environmental support to ensure successful and sustainable AI integration. The study recommends that future research adopt quantitative approaches to validate and prioritize the proposed model across different insurance sub-sectors, such as health, auto, and life insurance. Such efforts will enable stakeholders to tailor AI strategies to specific contexts, optimize resource allocation, and mitigate potential risks. Ultimately, the research highlights the transformative potential of AI in enhancing operational efficiency, customer satisfaction, and strategic agility within the insurance sector, while also calling for thoughtful governance and ethical oversight to ensure responsible innovation.

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Iranian Journal of Insurance Research
Volume 15, Issue 2 - Serial Number 56
April 2026
Pages 95-118
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