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Successfully Mitigating AI Management Risks to Scale AI Globally

Thomas Hutzschenreuter et al.

MIS Quarterly Executive2025https://doi.org/10.17705/2msqe.00117article
AJG 2ABDC A
Weight
0.50

Abstract

Many firms struggle to scale today’s generative and predictive AI systems effectively because their machine learning-based working mechanisms amplify general technology management challenges and create entirely new ones. Based on an in-depth case study of industrial AI pioneer Siemens AG, we describe how to successfully mitigate five critical technology management risks to scale AI globally, and provide recommendations for creating company-wide business impacts with machine learning-based AI systems.

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https://doi.org/https://doi.org/10.17705/2msqe.00117

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@article{thomas2025,
  title        = {{Successfully Mitigating AI Management Risks to Scale AI Globally}},
  author       = {Thomas Hutzschenreuter et al.},
  journal      = {MIS Quarterly Executive},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/2msqe.00117},
}

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Successfully Mitigating AI Management Risks to Scale AI Globally

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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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