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Organizing for AI Innovation: Insights From an Empirical Exploration of U.S. Patents

Yu-Kai Lin & Likoebe M. Maruping

MIS Quarterly2025https://doi.org/10.25300/misq/2025/18765article
FT50UTD24AJG 4*ABDC A*
Weight
0.52

Abstract

Although the prevalence of artificial intelligence (AI) innovations is on the rise, firms frequently report failures and setbacks in their development and implementation of AI innovation efforts. One common issue behind many failing AI initiatives is that they are organized just like other information technology (IT) innovation efforts. To elucidate why and how the production of AI and IT innovations may need to be managed differently, this study juxtaposes these two types of innovations based on two key dimensions of the Schumpeterian framework: the form (product vs. process) and magnitude (radical vs. incremental) of innovations. By analyzing a matched sample of AI and IT patents, we found robust evidence that AI innovations are less radical and more process oriented than comparable IT innovations. Drawing upon our empirical discovery, we developed a conceptual framework to suggest a new way to think about organizing AI innovation. Our research contributes to the literature and practice on AI innovation by illuminating the comparative differences between AI innovations and other IT innovations and advancing a set of empirically derived propositions on how firms may be able to better manage their AI innovation activities.

7 citations

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https://doi.org/https://doi.org/10.25300/misq/2025/18765

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@article{yu-kai2025,
  title        = {{Organizing for AI Innovation: Insights From an Empirical Exploration of U.S. Patents}},
  author       = {Yu-Kai Lin & Likoebe M. Maruping},
  journal      = {MIS Quarterly},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.25300/misq/2025/18765},
}

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Organizing for AI Innovation: Insights From an Empirical Exploration of U.S. Patents

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Evidence weight

0.52

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

F · citation impact0.47 × 0.4 = 0.19
M · momentum0.68 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.