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Tax Agent Industry: AI Empowerment Requires Substance Beyond the Hype

China Taxation News reports that the China Certified Tax Agents Association launched a national AI empowerment skills competition for tax professional services, promoting deep integration of AI technology. CCTAA Vice President Zheng Jiangping emphasized that AI+ implementation is key to high-quality industry development, while cautioning practitioners to maintain professional judgment, avoid over-reliance on AI, and stay vigilant against AI hallucination risks.

Industry News中国税务报Source
2026-07-31

The China Certified Tax Agents Association recently launched a national AI empowerment skills competition for tax professional services. Vice President Zheng Jiangping stated the association is promoting deep integration of AI technology to elevate tax professional services up the value chain.

In August 2025, the State Council issued guidelines emphasizing the evolution of service industries toward intelligence-driven models. In March 2026, the State Taxation Administration called for exploring tax AI large models leveraging massive tax data and policy texts. The CCTAA issued guidance on promoting AI application development in the tax agent industry, deploying 16 application scenarios.

On June 18, 2026, Renmin University held an academic seminar on AI applications in tax administration. Wang Juan, director of Yongcheng Jiayi Tax Agent Firm, noted that firms should focus on high-frequency, high-risk matters such as equity transfers and related-party transaction pricing, using AI tools to simulate tax screening logic and help enterprises establish normalized internal audit mechanisms for proactive risk prevention.

Industry insiders believe basic accounting, reconciliation, and filing work will eventually be automated, but compliance judgment based on business logic and inter-departmental communication remain barriers AI cannot cross. Zheng Jiangping emphasized that practitioners must integrate AI effectively while maintaining professional judgment, avoiding over-reliance, and building data security protection systems to ensure practice data remains safe, controllable, and reliable.