Handbook of Artificial Intelligence and Strategy

Paper highlights

The Handbook of Artificial Intelligence and Strategy is an edited research handbook for scholars, students, educators, and managers who need a structured view of how AI is changing strategic management. Its 20 chapters bring together more than 40 contributors from strategy, organization, innovation, education, public policy, and AI.

AI affects strategy at several levels at once: it changes assumptions about cognition, supplies new tools for strategic work, alters what schools should teach, and reshapes industries and regulation. The handbook maps this emerging field rather than treating AI as an isolated technology topic or offering one forecast.

The organizing device is the “4 T’s”: theories, tools, teaching, and terrains. Together they connect foundational questions—such as what happens when cognitive limits move—with decision systems, education, competition, governance, and ethics.

A map of the volume

• Theories revisit capabilities, organizational design, competition, and other foundations of strategy.
• Tools examine how AI changes analysis and strategic action.
• Teaching considers curricula, simulations, human skills, and the role of management education.
• Terrains covers the competitive, institutional, ethical, and regulatory environments in which AI strategy unfolds.

The chapters do not supply a settled doctrine. They offer a structured research agenda and competing perspectives in a field where capabilities move faster than conventional research cycles.

Why this handbook matters

Most discussions begin at one level: a manager using a chatbot, a firm adopting a tool, or an industry facing disruption. The handbook places those levels beside one another. Some chapters study prediction, innovation, and decision-making; others examine resistance, AI platforms, low-resource languages, public governance, and ethical failure.

For researchers, the book identifies constructs, mechanisms, and open questions across literatures. For educators, it connects curriculum design to changes in managerial work. For executives, it separates tool adoption from the organizational capabilities and institutional conditions that determine whether adoption produces an advantage. The editors’ introduction explains the “4 T’s” structure and frames the volume as a curated conversation rather than a set of settled answers.

How to use this volume

Cite this for

• A structured map of the emerging AI-and-strategy research field.
• The 4 T’s organization of theories, tools, teaching, and terrains.
• A curated set of perspectives on AI’s effects on strategy, organization, education, competition, governance, and ethics.

Useful for teaching

• Designing a syllabus or module that moves beyond tool adoption.
• Showing how AI strategy questions appear at individual, organizational, industry, and institutional levels.
• Introducing students to several research conversations rather than one settled doctrine.

Careful claim

The handbook is a field map and research agenda; it should be used as a curated conversation about AI and strategy, not as a unified forecast or settled theory.

Chapters and authors

Introduction

1. Editors’ introduction: artificial intelligence and strategy—charting new frontiers — Felipe Csaszar and Nan Jia

Part I: Theories

2. Strategic upskilling of knowledge workers in the generative AI era — Maryam Alavi
3. Will artificial intelligence ‘democratize’ corporate political activities? Amplifying the role of ‘know who’ in the age of enhanced ‘know what’ — Nan Jia, Maria D. Perez, Jinyuan Song, Yifan Wei, and Bo Yang
4. Artificial intelligence as a platform technology: Strategic implications of competing on top of an AI platform — Kevin J. Boudreau, Lars Bo Jeppesen, and Milan Miric
5. Algorithmic ambidexterity: Rethinking exploration and exploitation in the age of AI — Shuang Liu
6. GenAI and the future of creativity in science and art — Sandra Barbosu and Pooyan Khashabi
7. We rise to resist: The crisis of relevance and the movement against generative AI in creative industries — Saheli Nath

Part II: Tools

8. Toward a human–AI task tensor: A taxonomy for organizing work in the age of generative AI — Anil R. Doshi and Alastair P. Moore
9. Generative artificial intelligence for advancing knowledge-based technological innovation and strategy: The ‘fruit tree’ model — Jiaming Ding and Kenneth G. Huang
10. Strategic decisions and AI: The inferential trilemma — Lu Hong, Anusha Kallapur, and Scott E. Page
11. AI-augmented strategic tools for strategy formulation and implementation: Revisiting traditional strategy tools and frameworks — David Gurzick, Maheshkumar P. Joshi, and Martha Gurzick

Part III: Teaching

12. Business schools as learning organizations in the age of AI — Peter Cardon and Ramandeep S. Randhawa
13. Towards an AI-infused MBA: A head, heart, and hands perspective — Alfredo Enrione
14. Why and how to teach foundational AI ideas to business school students — Tom Steinberger
15. Space for AI-supported learning: Expanding and endangered — Henning Piezunka
16. Teaching the role of AI in strategic decision-making within strategic factor markets using a word game — Ipek Koparan and Gorkem Aksaray

Part IV: Terrains

17. The business of AI-producing startups: Evidence from a worldwide survey — James Bessen, Stephen M. Impink, and Robert Seamans
18. Beyond English-centric AI: Strategic frameworks for developing low-resource language models — Prithwiraj Choudhury, Do Yoon Kim, and Sana Kang
19. Governance of artificial intelligence: Public policy and self-regulatory frameworks for consequential decision-making — Gwendolyn Kuo-fang Lee
20. AI and ethical concerns in managing decision-making processes: A case study of Leonardo S.p.A. — Ksenija Milosevic, Saverio Barabuffi, and Giulio Ferrigno

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