Paper Type
Complete
Paper Number
PACIS2025-1713
Description
Generative artificial intelligence (genAI) holds significant potential in enhancing the productivity of knowledge workers. As competition intensifies and markets become more complex, companies face a growing need for tailored business strategies. Given that time is one of the scarcest resources of well-paid knowledge workers, it matters where they allocate it during strategy development. We investigated which parts of the strategy development process are likely to be augmented with genAI by conducting a systematic literature review and survey involving 36 strategy professionals. We identified and prioritized 13 use cases (UCs) based on their potential productivity impact and potential technical complexity. The results reveal that UCs with higher technical complexity yield greater productivity improvements. Higher productivity impact can be achieved in strategy formulation, whereas lower technical complexity is found in strategic analysis. We categorized UCs into low-hanging fruits and moonshots. Practitioners can use these insights to prioritize and manage their genAI pipeline.
Recommended Citation
Wunder, Andreas; Fränzl, Jonas; Au, Christian; Paul, Herbert; and Winkler, Till J., "Mapping the Landscape of Generative AI Use Cases in Strategic Management: A Systematic Literature Review and Survey Assessment" (2025). PACIS 2025 Proceedings. 4.
https://aisel.aisnet.org/pacis2025/is_praction/is_praction/4
Mapping the Landscape of Generative AI Use Cases in Strategic Management: A Systematic Literature Review and Survey Assessment
Generative artificial intelligence (genAI) holds significant potential in enhancing the productivity of knowledge workers. As competition intensifies and markets become more complex, companies face a growing need for tailored business strategies. Given that time is one of the scarcest resources of well-paid knowledge workers, it matters where they allocate it during strategy development. We investigated which parts of the strategy development process are likely to be augmented with genAI by conducting a systematic literature review and survey involving 36 strategy professionals. We identified and prioritized 13 use cases (UCs) based on their potential productivity impact and potential technical complexity. The results reveal that UCs with higher technical complexity yield greater productivity improvements. Higher productivity impact can be achieved in strategy formulation, whereas lower technical complexity is found in strategic analysis. We categorized UCs into low-hanging fruits and moonshots. Practitioners can use these insights to prioritize and manage their genAI pipeline.
Comments
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