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

TL;DR

This paper investigates whether AI, particularly Large Language Models, can augment strategic decision-making (SDM) by generating and evaluating forward-looking strategies in realistic entrepreneurial contexts. It develops AI-augmented SDM vignettes of scenario planning, Porter's Five Forces, the Devil's Advocate, and Wisdom of the Crowd, and provides two empirical studies comparing AI-generated/evaluated strategies to human benchmarks. The results show LLM-generated plans receive higher evaluator interest and that AI-based evaluations correlate with expert judgments, suggesting AI can scale and sharpen SDM. The authors propose a framework linking AI capabilities to SDM processes (search, representation, aggregation) and outcomes, discuss implications for firm performance and the theory-based view of strategy, and outline future research on AI-enabled strategy science.

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 startup competition that current Large Language Models (LLMs) can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for key cognitive processes underlying SDM -- search, representation, and aggregation. Our analysis suggests 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.

Figures (5)

• Figure 1: Comparison of entrepreneur- vs. LLM-generated business plans.
• Figure 2: Investor vs. LLM evaluations of business plans.
• Figure 3: Framework connecting the use of AI in SDM to firm performance.
• Figure B.1: Rubric used by evaluators.
• Figure C.1: Correlations between investor and LLM evaluations by training window.