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Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation

Jin Gerlach & Donald Lange

Academy of Management Review2026https://doi.org/10.5465/amr.2024.0408article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

Abstract

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.

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https://doi.org/https://doi.org/10.5465/amr.2024.0408

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@article{jin2026,
  title        = {{Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation}},
  author       = {Jin Gerlach & Donald Lange},
  journal      = {Academy of Management Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.5465/amr.2024.0408},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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