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.

翻译：人工智能（AI）的最新进展在自动化敏捷项目管理的核心环节中展现出潜力，但其对团队认知的影响仍未得到充分探索。本研究通过一项受控的三条件实验，在中型数字机构的实时客户交付物上对比了纯AI、纯人工及混合规划模型，研究了敏捷冲刺规划中的认知卸载现象。我们采用包括估算准确性、返工率、范围变更恢复时间在内的量化指标，并结合规划稳健性的定性指标，评估了每种模型超越原始效率的实际效果。研究发现：纯AI规划虽能最小化时间与成本，但因未言明的假设导致风险捕获率显著下降、返工增加；而纯人工规划虽在适应性方面表现优异，但需承担大量管理开销。基于这些发现，我们提出了混合AI-人工冲刺规划的理论框架，将估算与待办事项格式化任务分配给算法工具，同时将风险评估与歧义消除环节保留给人类决策。本研究挑战了"效率即效力"的假设，为组织在增强而非削弱团队认知的过程中提供了可操作的管理策略。