Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors

Felipe A. Csaszar (), Harsh Ketkar () and Hyunjin Kim ()
Additional contact information
Felipe A. Csaszar: Strategy Department, Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Harsh Ketkar: McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
Hyunjin Kim: Strategy Area, INSEAD, Singapore 138676

Strategy Science, 2024, vol. 9, issue 4, 322-345

Abstract: This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a start-up competition that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for the key cognitive processes underlying SDM—search, representation, and aggregation. Our analysis suggests that AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches, like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy.

Keywords: artificial intelligence; strategic decision-making; experiments; search; representation; aggregation; theory-based view (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/stsc.2024.0190 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orstsc:v:9:y:2024:i:4:p:322-345

Access Statistics for this article

More articles in Strategy Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().