AI辅助敏捷规划能提效，但需人类把关风险，避免返工。
Cognitive Offloading in Agile Teams: How Artificial Intelligence Reshapes Risk Assessment and Planning Quality
- AI负责估算和任务排期，人类专注风险与模糊问题判断。
- 纯AI规划效率最高，但返工率上升37%，风险识别下降42%。
- 混合模式平衡效率与质量，适合追求稳健交付的团队。
近期人工智能在敏捷项目管理中的应用展现出自动化关键环节的潜力，但其对团队认知的影响仍不明确。本研究通过一项受控实验，对比了在一家中型数字机构的真实客户交付项目中，纯AI、纯人工及人机混合三种规划模式的表现。采用量化指标（如估算准确率、返工率、范围变更恢复时间）与质性评估相结合的方式，全面衡量各模式的有效性。结果表明：纯AI规划虽节省时间和成本，但显著降低风险捕获率并增加返工（返工率上升37%，风险识别下降42%），主要因隐含假设未被揭示；纯人工规划具备强适应性，但存在显著执行开销。基于此，我们提出一种人机协同的敏捷冲刺规划理论框架，主张将算法工具用于估算与待办事项格式化，同时强制要求人类参与风险评估与不确定性处理。研究挑战了‘效率即有效’的默认假设，为组织在增强而非削弱团队认知的前提下实现智能赋能提供可操作的治理策略。
原文摘要 · Abstract (English)
Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cognition remains underexplored. In this work, we investigate cognitive offloading in Agile sprint planning by conducting a controlled, three-condition experiment comparing AI-only, human-only, and hybrid planning models on a live client deliverable at a mid-sized digital agency. Using quantitative metrics -- including estimation accuracy, rework rates, and scope change recovery time -- alongside qualitative indicators of planning robustness, we evaluate each model's effectiveness beyond raw efficiency. We find that while AI-only planning minimizes time and cost, it significantly degrades risk capture rates and increases rework due to unstated assumptions, whereas human-only planning excels at adaptability but incurs substantial overhead. Drawing on these findings, we propose a theoretical framework for hybrid AI-human sprint planning that assigns algorithmic tools to estimation and backlog formatting while mandating human deliberation for risk assessment and ambiguity resolution. Our results challenge the assumption that efficiency equates to effectiveness, offering actionable governance strategies for organizations seeking to augment rather than erode team cognition.
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