Generative AI in InsurTech: Revolutionizing Personalized Advertising Content, Automated CRM Interactions, and HRM Training Simulations

Abstract

This research investigates the complex, conjunctural pathways leading to high behavioral intention to adopt integrated Generative AI (GenAI) solutions in the Indian InsurTech sector. Moving beyond variance-based methods, we employ a configurational approach grounded in the Technology-Organization-Environment (TOE) framework and the principle of equifinality. Using a mixed-methods design, we first calibrate conditions through qualitative case studies of five InsurTech firms. Subsequently, we apply Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to survey data from 35 InsurTech entities to identify sufficient causal recipes. Findings reveal three distinct, equally effective pathways to high adoption intention: (1) The Capability-Pushed Pathway, driven by strong AI talent and competitive pressure; (2) The Resource-Enabled Pathway, where abundant financial resources overcome regulatory complexity; and (3) The Efficiency-Seeking Pathway, where strong task-technology fit for CRM and advertising motivates adoption even with moderate resources. Notably, no single condition is necessary, but the presence of AI-skilled talent appears in two of the three core solutions. The absence of a strong innovation culture, however, can be compensated by other factors. This study contributes by shifting the analytical lens from 'net effects' to 'causal configurations,' providing managers with tailored strategic archetypes for GenAI adoption across marketing, service, and human capital functions.