Citation: Csaszar, F. A., Lee, G., Zemsky, P., & Zenger, T. (2026). Can AI do strategy? Strategy Science 11(1) 1–15. doi:10.1287/stsc.2026.intro.v11.n1
Paper highlights
This is the introductory essay to the Strategy Science special issue “Can AI Do Strategy?” We present our answer to the issue’s question, develop a framework for evaluating AI’s strategic role, and introduce the papers in the issue.
The answer depends on two separate questions: Can AI perform the task? Should an organization let it act? A system may produce strong results without human-like causal reasoning, yet still receive little autonomy when outcomes are hard to verify or mistakes are irreversible.
The core prediction is that AI will enter strategy first where performance can be measured, downside can be bounded, objectives can be specified, and accountability can be assigned—not necessarily where the reasoning looks simplest to people.
The two ladders
The Causal Ladder distinguishes four increasingly demanding forms of strategic reasoning:
- predicting within an existing model;
- estimating the effects of familiar interventions;
- transporting causal ideas to new contexts through counterfactuals and analogies; and
- constructing new causal models and problem frames.
The Delegation Ladder runs from informational support to recommendations, bounded autonomy, and executive authority. Movement up it depends on demonstrated performance and governance rather than on the system resembling a human strategist.
Why the distinction matters
- Capability is measured performance on a task.
- Delegation is a grant of authority and depends on stakes, verifiability, reversibility, liability, and accountability.
- A capable system may remain advisory, while a more limited system may receive narrow autonomy on a well-scored task.
The framework turns a yes-or-no debate into a research program. Scholars can locate tasks on the Causal Ladder and define observable performance. Organizations can separately decide what evidence, monitoring, and fallback procedures justify movement up the Delegation Ladder. Measurability connects the two: AI is likely to enter first where feedback is timely, errors are bounded, and performance can be compared with a human or organizational baseline.
How to use this paper
Cite this for
- The dual-ladder framework separating AI capability from delegation of authority.
- The claim that AI will enter strategy first where performance is measurable, feedback is available, and downside is bounded.
- An introduction to the Strategy Science special issue on AI and strategy.
Useful for teaching
- Why “Can AI do strategy?” should be decomposed into task capability and organizational delegation.
- How to place strategic tasks on a causal-reasoning ladder and a governance ladder.
- Why measured performance on a task does not by itself justify broad executive authority.
Careful claim
The paper argues that AI capability and AI delegation are separate questions; a system may perform well on a bounded strategic task while remaining advisory because stakes, verifiability, reversibility, and accountability differ.
Abstract
Can artificial intelligence (AI) do strategy? This question is both urgent and foundational: urgent because AI is already reshaping strategic practice and foundational because answering it forces us to articulate what strategy actually is.
In this introductory essay to the Strategy Science Special Issue on AI and Strategy, we propose a dual-ladder framework: a causal ladder that maps the cognitive hierarchy of strategic 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.
We organize the Special Issue contributions around what AI can do today, could do as capabilities develop, and should do given the imperatives of accountability and human judgment. We close with a challenge and an invitation: if strategy scholars do not define good strategizing precisely enough to be encoded, tested, and refined, other disciplines will, embedding thinner conceptions of strategy into the tools managers use. Teaching machines to strategize and support strategizing is ultimately a method for rediscovering what strategy is.
Special issue contents
This paper introduces the Strategy Science special issue on “Can AI do strategy?” The issue contains the following articles:
- Can AI do strategy? — Felipe A. Csaszar, Gwendolyn Lee, Peter Zemsky, and Todd Zenger
- Can AI do strategy? A dialogue and debate — Aaron Chatterji, Felipe A. Csaszar, James Evans, Teppo Felin, Jessica Hullman, Karim R. Lakhani, Mari Sako, and Todd Zenger
- Mean articulation machines — Russ McBride
- The role of predictions in acquisition decision making: The strategic value of AI-driven foresight — Xinying Qu, M. V. Shyam Kumar, and Tony W. Tong
- AI-augmented strategic decision-making under time constraints: An experimental study on mental representations and strategic foresight — Tim Kanis, Justus Emanuel Mann, and Jutta Stumpf-Wollersheim
- How well can AI do strategy? Empirical benchmarking using strategy simulations — Ryan T. Allen and Rory M. McDonald
- Can LLMs aid analogical reasoning for strategic decisions? A comparative study — Prothit Sen, Maciej Workiewicz, and Phanish Puranam
- Beyond black boxes: Designing and testing agentic AI systems for strategy — Arnaldo Camuffo, Alfonso Gambardella, Saeid Kazemi, and Abhinav Pandey
- When artificial intelligence does strategy: Learning, good times, lock-in, and human-driven strategic renewal — Nataliia Neshenko and Michael D. Ryall