This study empirically assesses the readiness for Artificial Intelligence (AI) adoption within the Tunisian insurance sector by applying the Technology Acceptance Model (TAM). The research aims to identify priority application domains, examine structural barriers, and evaluate the alignment of local practices with global technological trends. Using a mixed-methods approach, a survey was conducted among 56 key professionals, including actuaries, managers, and IT specialists, utilizing TAM constructs and Technology-Organization-Environment (TOE) framework. The findings reveal a high level of Perceived Usefulness (mean 3.81/5) and Perceived Ease of Use (mean 3.96/5) among respondents. Priority areas for AI implementation are identified as fraud detection (74%) and claims management or pricing (65%), while significant inhibitors include budgetary constraints (56%) and data quality issues (48%). Interestingly, 68% of professionals favor the implementation of pilot projects as a strategic starting point. This research contributes to the literature on digital transformation in emerging markets by providing one of the first empirical assessments of AI acceptance specifically tailored to the Tunisian insurance ecosystem, offering actionable insights for decision-makers to bridge the gap between technological potential and organizational integration.
Keywords: Artificial Intelligence, TAM Model, Tunisian Insurance, Digital Transformation, Actuarial Science, Emerging Markets
Mehrez Ben Nasr
Certified Actuary FTUSA
Doctor of Professional Practice, University of Digital and AI Management
mehrezbennasr@gmail.com
Mehrez Ben Nasr is a certified actuary with over 10 years of experience in the insurance sector. Holding a Doctor of Professional Practice in AI Management from UNIDAIM, a Master’s degree in Actuarial Science (Université Paris-Dauphine), and a Master’s degree in Mathematics (Université de Carthage), he has authored several scientific publications and specialized books. His research focuses on applying artificial intelligence, machine learning algorithms, and advanced actuarial models.
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