Enhancing digital transformation with intelligent power, listed insurance companies' AI competitions enter the deep water zone

Ask AI · How do insurance companies achieve human-machine collaborative efficiency in AI transformation?

The insurance industry is undergoing a “smart reconstruction” led by AI. Recently, leading insurers such as China Life, Ping An, China Pacific Insurance, PICC, New China Life, and China Taiping have successively reported their 2025 performance. On April 1, Beijing Business Daily summarized that “digital and intelligent transformation” has shifted from a strategic slogan to tangible operational investments by listed insurers. Amid the rapid development of artificial intelligence, many insurers discussed the construction of financial technology capabilities, including AI technology development and application, at annual reports, earnings releases, and other occasions. Unlike previous digitalization of individual business points, today’s “AI+” now covers the entire business process, including both customer-facing and internal staff.

AI Penetrates the Entire Business Process

Data from annual reports show that many top insurers have increased the strategic importance of AI in their top-level design. Meanwhile, AI technology is breaking through traditional process bottlenecks, shifting from “manual-led” to “intelligent-driven,” significantly improving service efficiency and customer experience, becoming the core engine for “efficiency gains.”

Since 2025, AI applications in the domestic insurance industry have entered a new phase of large-scale implementation. Listed insurers are increasingly positioning AI as a core strategic tool and increasing resource investment. According to Ping An, the group adheres to the principle of “AI in ALL,” customer demand-oriented, empowering core businesses, continuously investing in R&D, and building leading technological capabilities based on the four elements of artificial intelligence (algorithms, data, scenarios, and computing power). In 2025, over 230k employees at Ping An used the internal intelligent agent platform to develop more than 70k intelligent applications, with model calls totaling 3.65 billion times throughout the year.

China Life also uses AI to improve quality and efficiency. Its annual report mentions actively aligning with the national “Artificial Intelligence+” initiative, building an AI capability system covering all aspects of company management; establishing a data space of “hundreds of millions of data points, thousands of features, and hundreds of labels.”

As AI becomes “infrastructure,” its benefits go beyond cost reduction to “efficiency + quality improvement,” penetrating all business processes. For example, China Life mentions that large models enable agents to specialize and personalize their sales efforts, improving customer outreach efficiency, with annual customer visits increasing by over 15%. ZhongAn Insurance states that AI technology has deeply integrated into product design, marketing, underwriting, service, claims, and quality control across the entire chain. In private domain scenarios, AI customer service helps a single agent serve over 100k end users. Automated review of health insurance cases exceeds 45%, with cases closed within 15 seconds at the fastest, and over 76% of customers receive claims within one working day. In the automotive ecosystem, over 50% of cases are handled with video “instant connection, instant viewing, instant claims,” with AI damage assessment times shortened to as little as 116 seconds.

As Wang Peng, deputy researcher at Beijing Academy of Social Sciences, analyzes, in the context of fluctuating industry manpower, AI can significantly improve total factor productivity. Through tools like intelligent underwriting and instant claims, insurers achieve case closures in seconds and high automation rates, greatly reducing operational costs and enhancing customer experience.

From Auxiliary Tools to Strategic Engines

Looking ahead, many listed insurers have clearly defined AI as a long-term strategic direction. At a critical point in industry digital transformation, AI is no longer just an auxiliary tool for efficiency but a core strategic engine driving business growth and reshaping competitive landscapes.

“AI is not a multiple-choice question but a must-answer,” said Guo Xiaotao, co-CEO of Ping An, at the company’s earnings release. Ping An is advancing the “Comprehensive Finance Nine-Point Convergence” plan, aiming to integrate over 700 million internet registered users into a unified super portal through AI-driven efforts, achieving comprehensive aggregation of traffic, entry points, and backend data, enabling customers to complete medical, pension, and comprehensive financial services in a one-stop experience.

Regarding expanding AI applications, Qin Hongbo, Vice President of New China Life, states that the goal is to “let robots do what robots should do, and let employees do more valuable work.” With the advent of the AI era, technological empowerment at New China Life has penetrated all aspects of business and management, becoming the core engine for high-quality development. The company will continue to maintain strategic focus, investing in both people and infrastructure, aiming under the guidance of the “14th Five-Year” new technology plan to enable AI to generate greater efficiency at New China Life.

Ding Xiangqun, Chairman of PICC, clearly stated at the earnings conference that technology should be positioned as an “accelerator,” emphasizing the need to “more proactively seize AI development opportunities, deepen technological system reforms and digitalization, accelerate the release of technological productivity, and occupy the commanding heights of digital and intelligent transformation.”

Implementing strategies requires scientific methodology guidance. Fu Yifu, a special researcher at Shangshang Bank, suggests that insurers should focus on three collaborative dimensions in advancing AI capabilities. First, build an integrated data and computing power foundation. The effectiveness of AI depends on data quality; insurers need to break down internal “data silos,” and simultaneously develop compliant hybrid cloud and private computing infrastructure to ensure centralized utilization of data assets within regulatory boundaries. Second, balance efficiency gains with risk control. The financial industry demands high accuracy and interpretability; AI applications should establish model governance systems, including algorithm audits, human oversight mechanisms, and ethical standards to prevent “black box” operations from causing compliance risks. Third, reshape organizational capabilities for human-machine collaboration. Deep technological penetration requires redefining job responsibilities, focusing on cultivating frontline employees’ ability to work with AI tools, rather than simple replacement, and continuously upgrading organizational cognition through skill reshaping.

Beijing Business Daily reporter Li Xiumei