Can AI Do Strategy?
by Felipe A. Csaszar, Gwendolyn Lee, Peter Zemsky, and Todd Zenger
Can AI do strategy? This introductory essay presents a dual ladders framework: a causal ladder that maps the cognitive hierarchy of strategy tasks and a delegation ladder that specifies when organizations will grant AI autonomy over those tasks. A core insight emerges: AI will not enter strategy where required reasoning is deepest but where its performance is most measurable. The essay highlights how AI may reshape strategic management and previews the Special Issue's key debates and contributions.
Can AI Do Strategy? A Dialogue and Debate
by Aaron Chatterji, Felipe A. Csaszar, James Evans, Teppo Felin, Jessica Hullman, Karim R. Lakhani, Mari Sako, and Todd Zenger
This paper captures a debate and dialogue about AI's role in the future of strategic decision-making. What makes answering the "can AI do strategy" question so fascinating is that it first demands expressing a belief about what strategy is. This debate and dialogue captures a remarkable divergence of positions and perspectives both on what strategy is and on the potential role that AI can play.
Mean Articulation Machines
by Russ McBride
Where do LLMs fail at strategy? Where do they succeed? And where is the boundary? Their "mean articulation" architecture determines the answer to these questions.
The Role of Predictions in Acquisition Decision Making: The Strategic Value of AI-Driven Foresight
by Xinying Qu, M.V. Shyam Kumar, and Tony W. Tong
Using machine-learning–based measures of predicted stock market reactions to acquisition announcements, the study shows that AI-driven foresight improves deal selection and target identification, and that deviations between predicted and realized reactions are associated with greater learning as reflected in deal completion time. The paper highlights that predictions and AI-driven foresight have both ex-ante informational effects and ex-post learning effects for strategic decision making.
AI-Augmented Strategic Decision-Making Under Time Constraints: An Experimental Study on Mental Representations and Strategic Foresight
by Tim Kanis, Justus Emanuel Mann, and Jutta Stumpf-Wollersheim
AI changed how managers evaluated strategic options under time constraints. They considered more factors but felt more information overload and less psychological ownership-without improving strategic foresight.
How Well Can AI Do Strategy? Empirical Benchmarking Using Strategy Simulations
by Ryan T. Allen and Rory M. McDonald
How well can AI do strategy? This has historically been difficult to measure, but in this paper we benchmark LLMs in a strategy simulation that captures uncertainty, complexity, and delayed feedback. We find strong gains through early 2025, but also surprising declines in frontier models' strategic decision-making, partly driven by a tendency to favor legacy businesses over new opportunities in strategic resource allocation.
Can LLMs Aid Analogical Reasoning for Strategic Decisions? A Comparative Study
by Prothit Sen, Maciej Workiewicz, and Phanish Puranam
Finding analogies is valuable for strategists, but only if they match the problem. Today's LLMs outperform humans in finding candidate analogies but not in matching the problem.
Beyond Black Boxes: Designing and Testing Agentic AI Systems for Strategy
by Arnaldo Camuffo, Alfonso Gambardella, Saeid Kazemi, and Abhinav Pandey
Grounded in Simon's architecture of complexity, the authors propose that purposeful AI system design-not mere access to generic models-can be a source of competitive advantage, and they design, build, and experimentally test a multi-agent AI system for strategy.
When Artificial Intelligence Does Strategy: Learning, Good Times, Lock-in, and Human-Driven Strategic Renewal
by Nataliia Neshenko and Michael D. Ryall
We show AI-driven strategies can converge to tacitly collusive equilibria. There, in the comfortable embrace of a profitable status quo, strategic renewal dies-humans could restart a cycle of innovation, but may rationally choose to enjoy the comfort instead.