Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.

本研究解决了生成性人工智能（GenAI）在软件工程中对生产力影响的实证证据不足的问题。通过对电信和金融科技领域中GenAI编码助手的调查与访谈，发现其在例行编码任务中提升了生产力，但在复杂任务中面临挑战。研究强调了有效使用GenAI的新范式，包括迭代提示优化、沉浸式开发环境和自动代码评估。

利用生成性人工智能（GenAI）改变编码生产力的范式