This paper aims to explore the historical evolution of artificial intelligence (AI) in management and organizational studies and practices, highlighting the cyclical patterns of technological advancements, scholarly contributions and organizational adoptions. This paper proposes a wave metaphor and related framework to capture the dynamic and cyclical nature of AI’s evolution into theory and practice. This paper accessed relevant scholarly sources about AI’s technological and practical development over the decades. This study uncovers the recurring misalignments between technological advancements, scholarly contributions and organizational adoptions by identifying five distinct waves in AI history – symbolic AI, the AI Winter, the machine learning renaissance, the big data era and the emerging phase of human–AI collaboration. Each wave reflects distinct challenges and opportunities, providing insights into how management theory and practices shaped and have been shaped by AI. This framework also highlights the role of theory-practice misalignment – both as a barrier and a driver of progress – in shaping the trajectory of AI’s integration into management and organizational studies. This work challenges linear views of technological progress and emphasizes the interplay (and misalignments) between scholarly contributions and practice. For academics, it offers comprehensive research directions for investigating AI in management and organization studies. For practitioners, it provides guidance on navigating technological adoptions.

Surfing the AI waves: The historical evolution of artificial intelligence in management and organizational studies and practices / Cristofaro, Matteo; Giardino, Pier Luigi. - In: JOURNAL OF MANAGEMENT HISTORY. - ISSN 1751-1348. - 2025:(2025). [10.1108/JMH-01-2025-0002]

Surfing the AI waves: The historical evolution of artificial intelligence in management and organizational studies and practices

Giardino, Pier Luigi
Ultimo
2025-01-01

Abstract

This paper aims to explore the historical evolution of artificial intelligence (AI) in management and organizational studies and practices, highlighting the cyclical patterns of technological advancements, scholarly contributions and organizational adoptions. This paper proposes a wave metaphor and related framework to capture the dynamic and cyclical nature of AI’s evolution into theory and practice. This paper accessed relevant scholarly sources about AI’s technological and practical development over the decades. This study uncovers the recurring misalignments between technological advancements, scholarly contributions and organizational adoptions by identifying five distinct waves in AI history – symbolic AI, the AI Winter, the machine learning renaissance, the big data era and the emerging phase of human–AI collaboration. Each wave reflects distinct challenges and opportunities, providing insights into how management theory and practices shaped and have been shaped by AI. This framework also highlights the role of theory-practice misalignment – both as a barrier and a driver of progress – in shaping the trajectory of AI’s integration into management and organizational studies. This work challenges linear views of technological progress and emphasizes the interplay (and misalignments) between scholarly contributions and practice. For academics, it offers comprehensive research directions for investigating AI in management and organization studies. For practitioners, it provides guidance on navigating technological adoptions.
2025
Settore SECS-P/08 - Economia e Gestione delle Imprese
Settore ECON-07/A - Economia e gestione delle imprese
Cristofaro, Matteo; Giardino, Pier Luigi
Surfing the AI waves: The historical evolution of artificial intelligence in management and organizational studies and practices / Cristofaro, Matteo; Giardino, Pier Luigi. - In: JOURNAL OF MANAGEMENT HISTORY. - ISSN 1751-1348. - 2025:(2025). [10.1108/JMH-01-2025-0002]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/449471
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