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The Role of Firm AI Capabilities in Generative AI-pair Coding

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  • Jacques Bughin

Abstract

Generative Artificial Intelligence (genAI) is the latest evidence of the transformative value of AI in organizations. One promising avenue lies in software engineering, where genAI can contribute to coding by pairing with developers. Based on a sample of global firms, two main insights emerge on analyzing the productivity implications of genAI-pair coding. Coding quality is negatively correlated with productivity throughput gains, while quality-adjusted productivity gains depend on the extent to which organizations have deployed AI capabilities in the form of data, skills upgrade, and AI governance. As observed with other digital technologies, the success of using genAI is closely tied to complementary technical skills and organizational resources.

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  • Jacques Bughin, 2024. "The Role of Firm AI Capabilities in Generative AI-pair Coding," Working Papers TIMES² 2024-076, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ict:wpaper:2013/378272
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    1. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    2. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy & Marco Vivarelli, 2023. "AI technologies and employment: micro evidence from the supply side," Applied Economics Letters, Taylor & Francis Journals, vol. 30(6), pages 816-821, March.
    3. Jacques Bughin, 2024. "Doing versus Saying: Responsible AI among Large Firms," Working Papers TIMES² 2024-077, ULB -- Universite Libre de Bruxelles.
    4. Desouza, Kevin C. & Dawson, Gregory S. & Chenok, Daniel, 2020. "Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector," Business Horizons, Elsevier, vol. 63(2), pages 205-213.
    5. Lee, Yong Suk & Kim, Taekyun & Choi, Sukwoong & Kim, Wonjoon, 2022. "When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy," Technovation, Elsevier, vol. 118(C).
    6. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    7. Milliou, Chrysovalantou & Petrakis, Emmanuel, 2011. "Timing of technology adoption and product market competition," International Journal of Industrial Organization, Elsevier, vol. 29(5), pages 513-523, September.
    8. Ameye, Nicolas & Bughin, Jacques & van Zeebroeck, Nicolas, 2023. "How uncertainty shapes herding in the corporate use of artificial intelligence technology," Technovation, Elsevier, vol. 127(C).
    9. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    10. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    11. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    12. Benlian, Alexander & Wiener, Martin & Cram, W. Alec & Krasnova, Hanna & Maedche, Alexander & Möhlmann, Mareike & Recker, Jan & Remus, Ulrich, 2022. "Algorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 133638, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
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    Keywords

    Generative AI; productivity; enterprise RBV; capabilities; machine learning;
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