How Discriminative Artificial Intelligence Drives Scenario Planning: A Systematic Literature Review and Research Agenda
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2025
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| author | Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle |
| author_facet | Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle |
| collection | Wiley Open Access |
| contents | How Discriminative Artificial Intelligence Drives Scenario Planning: A Systematic Literature Review and Research Agenda Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle FUTURES & FORESIGHT SCIENCE ABSTRACT This article explores the potential of discriminative Artificial Intelligence (AI) to enhance scenario planning, a widely used methodology in strategic planning. Like others, scenario planning also faces the challenge of efficiently integrating available information. We address this challenge by investigating two research questions: First, how is discriminative AI currently applied in scenario planning? Second, how could discriminative AI techniques additionally be used to support the stakeholders of scenario planning? A systematic literature review identifies 58 relevant documents that illustrate the application of discriminative AI in several stages of the scenario process. We present six key findings in relation to the purpose of discriminative AI, the data used and the spectrum of topics. We then formulate seven research propositions that serve as a research agenda and highlight further potential for the utilization of discriminative AI. Our contribution to science is that we show how the roles of stakeholders are going to change. For management, we demonstrate the numerous opportunities offered by discriminative AI to improve the quality of scenario planning. 10.1002/ffo2.70021 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/ffo2.70021 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ffo2_70021 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | How Discriminative Artificial Intelligence Drives Scenario Planning: A Systematic Literature Review and Research Agenda Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle FUTURES & FORESIGHT SCIENCE How Discriminative Artificial Intelligence Drives Scenario Planning: A Systematic Literature Review and Research Agenda Laura M. Berensmeier Valentin J. Schmitt Martin G. Moehrle FUTURES & FORESIGHT SCIENCE ABSTRACT This article explores the potential of discriminative Artificial Intelligence (AI) to enhance scenario planning, a widely used methodology in strategic planning. Like others, scenario planning also faces the challenge of efficiently integrating available information. We address this challenge by investigating two research questions: First, how is discriminative AI currently applied in scenario planning? Second, how could discriminative AI techniques additionally be used to support the stakeholders of scenario planning? A systematic literature review identifies 58 relevant documents that illustrate the application of discriminative AI in several stages of the scenario process. We present six key findings in relation to the purpose of discriminative AI, the data used and the spectrum of topics. We then formulate seven research propositions that serve as a research agenda and highlight further potential for the utilization of discriminative AI. Our contribution to science is that we show how the roles of stakeholders are going to change. For management, we demonstrate the numerous opportunities offered by discriminative AI to improve the quality of scenario planning. 10.1002/ffo2.70021 http://creativecommons.org/licenses/by/4.0/ |
| title | How Discriminative Artificial Intelligence Drives Scenario Planning: A Systematic Literature Review and Research Agenda |
| topic | FUTURES & FORESIGHT SCIENCE |
| url | https://onlinelibrary.wiley.com/doi/10.1002/ffo2.70021 |