GenAI助力敏捷架构师快速设计与决策，但需警惕偏差与合规风险。
Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review
- 系统梳理33篇文献，归纳GenAI在架构设计、文档生成和决策支持中的应用
- 发现生成内容存在偏差、错误输出导致返工，隐私与合规问题突出
- 建议培养提示工程与模型评估能力，建立适应性治理机制
生成式人工智能（GenAI）正在重塑敏捷软件组织中的企业架构工作，但其影响证据分散。本研究遵循Kitchenham和PRISMA指南，对1697条记录进行系统文献综述，最终纳入33项研究，涵盖企业、解决方案、领域、业务及IT架构师角色。GenAI最稳定的应用包括：(i) 设计构思与权衡探索；(ii) 快速创建与优化各类产出物（如代码、模型、文档）；(iii) 架构决策支持与知识检索。报告的风险包括模型黑箱性与偏见、上下文错误输出引发返工、隐私与合规隐患，以及社会惰化现象。研究还识别出新兴能力需求，如提示工程、模型评估与专业监督，以及组织层面的准备度与自适应治理机制。成果包括：(1) 绘制了敏捷架构中GenAI的应用场景与风险图谱；(2) 提出能力建设与治理启示；(3) 初步构建人机协同架构研究议程。总体而言，研究为负责任地采用GenAI提供依据，以加速数字化转型并保障架构完整性。
原文摘要 · Abstract (English)
Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.
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