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Artificial intelligence and the changing sources of competitive advantage

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  • Sebastian Krakowski
  • Johannes Luger
  • Sebastian Raisch

Abstract

Research Summary We apply a resource‐based view to investigate how the adoption of Artificial Intelligence (AI) affects competitive capabilities and performance. Following prior work on using chess as a controlled setting for studying competitive interactions, we compare the same players’ capabilities and performance across conventional, centaur, and engine chess tournaments. Our analysis shows that AI adoption triggers interrelated substitution and complementation dynamics, which make humans’ traditional competitive capabilities obsolete, while creating new sources of persistent heterogeneity when humans interact with chess engines. These novel human‐machine capabilities are unrelated, or even negatively related, to traditional capabilities. We contribute an integrated view of substitution and complementation, which identifies AI as the driver of these dynamics and explains how they jointly shift the sources of competitive advantage. Managerial Summary AI‐based technologies increasingly substitute and complement humans in managerial tasks such as decision making. We investigate how such change affects the sources of competitive advantage. AI‐based engines’ adoption in chess allows us to investigate competitive capabilities and performance in human, AI, and hybrid settings. We find that neither humans nor AI in isolation explain performance differences in the AI and hybrid settings. Instead, a new decision‐making resource emerges at the human‐AI intersection, which drives performance but is unrelated or even negatively related to humans’ original capability. Our results document how AI adoption changes the sources of competitive advantage and, in turn, requires managers to develop new capabilities to stay relevant in an AI‐based competitive landscape.

Suggested Citation

  • Sebastian Krakowski & Johannes Luger & Sebastian Raisch, 2023. "Artificial intelligence and the changing sources of competitive advantage," Strategic Management Journal, Wiley Blackwell, vol. 44(6), pages 1425-1452, June.
    • Handle: RePEc:bla:stratm:v:44:y:2023:i:6:p:1425-1452
    DOI: 10.1002/smj.3387
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    4. Jiaqi Xu & Xiaofei Tang & En-Chung Chang & Haoyu Peng, 2024. "Working with AI: the impact of organizational intelligent service strategy on employees’ perception of career achievement," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-20, December.
    5. Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024. "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series 01/2024, WU Vienna University of Economics and Business.
    6. Evangelos Katsamakas & J. Manuel Sanchez-Cartas, 2024. "Generative Artificial Intelligence, Content Creation, and Platforms," Journal of Industry, Competition and Trade, Springer, vol. 24(1), pages 1-20, December.
    7. Ali Nawaz Khan & Khalid Mehmood & Ahsan Ali, 2024. "Maximizing CSR impact: Leveraging artificial intelligence and process optimization for sustainability performance management," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4849-4861, September.
    8. Constance E. Helfat & Aseem Kaul & David J. Ketchen & Jay B. Barney & Olivier Chatain & Harbir Singh, 2023. "Renewing the resource‐based view: New contexts, new concepts, and new methods," Strategic Management Journal, Wiley Blackwell, vol. 44(6), pages 1357-1390, June.
    9. Liu, Yang & Ying, Zhenzhou & Ying, Ying & Wang, Ding & Chen, Jin, 2024. "Artificial intelligence orientation and internationalization speed: A knowledge management perspective," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    10. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.

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