Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation
研究了机器学习系统因模型漂移而需要反复人工修复，反而可能加速组织知识折旧的过程，对管理者理解自动化与知识保护的关系有启示。
Organizational knowledge is essential for sustained competitive advantage, yet it naturally depreciates over time. Traditional rule-based technologies help counter this erosion by serving as stable repositories of knowledge. In contrast, machine learning (ML) systems—an increasingly prevalent and relied-upon technology—introduce new risks. Because their predictive models depend on historical training data, ML systems are vulnerable to model drift: a gradual misalignment with evolving operational realities that creates recurring needs for human-led repair. We develop a multilevel process model showing how and when repeated cycles of ML use and repair can unintentionally accelerate organizational knowledge depreciation. In doing so, we highlight the distinct vulnerabilities of ML systems, challenge the conventional view of technologies as stable repositories of knowledge, and emphasize the importance of deliberate human engagement alongside automation to sustain organizational knowledge over time.