In the insurance industry, artificial intelligence (AI) is becoming more common, which brings with it both opportunities and obstacles, yet its impact on operational efficiency remains unclear. While AI-powered automation has reduced claims processing time by 40% and fraud detection costs by 20%, the effectiveness of AI adoption varies across firms due to differences in market share, digital readiness, and regulatory frameworks. This study examines AI's role in optimizing insurance companies’ operational efficiency. Employing Tobit regression models, Grey Relational Analysis, and Data Envelopment Analysis (DEA), this study evaluates how AI-driven innovations enhance resource allocation, cost efficiency, and financial performance. DEA measures operational efficiency based on labor, financial capital, and physical assets as inputs, and premium income, claims expenditures, and investment income as outputs. Grey Relational Analysis examines external factors such as AI penetration, insurance assets, and economic conditions, while Tobit regression identifies key determinants of efficiency, including AI adoption, market competition, and digital transformation. Findings indicate that AI significantly enhances efficiency, particularly in firms with high digital integration, yet smaller firms struggle due to limited AI infrastructure. These insights align with China’s AI Ethics Governance Policy (Ping An Group, 2023), emphasizing fair, transparent, and responsible AI use. This study empirically assesses AI's impact on insurance efficiency using DEA, Grey Relational Analysis, and Tobit regression, a multi-method approach rarely applied in insurtech research. It aligns with SDG 9, advancing Insurtech, digital transformation, and Industry 5.0, while incorporating China’s AI policies into its analysis to offer policy-driven insights for emerging economies.