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Transforming boundaries: how does ChatGPT change knowledge work?

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Název: Transforming boundaries: how does ChatGPT change knowledge work?
Autoři: Ritala, Paavo, Ruokonen, Mika, Ramaul, Laavanya
Zdroj: Journal of Business Strategy; 2024, Vol. 45 Issue 3, p214-220, 7p
Témata: GENERATIVE artificial intelligence, CHATGPT, KNOWLEDGE workers
Abstrakt: Purpose: This paper aims to demonstrate how the new generative artificial intelligence (AI) tool ChatGPT changes knowledge work for individuals and what are the implications of this change for companies. Design/methodology/approach: Based on 22 interviews from informants across different industries, the authors conducted an inductive analysis on the use and utility of ChatGPT in knowledge work. Based on this initial analysis, they discovered different ways in which ChatGPT either augments human agency, makes it redundant or lacks capability in that regard. Findings: The authors develop a 2 × 2 framework of algorithmic assistance, which demonstrates four ways in which ChatGPT (and generative AI in general) interacts with knowledge workers, depending on the usefulness of ChatGPT in particular tasks and the type of the task (routine vs creative). Practical implications: Based on the insights from the interviews, the authors propose a set of actionable questions for individual knowledge workers and companies from four viewpoints: skills and capabilities; team structure and workflow coordination; culture and mindset; and business model innovation. Originality/value: To the best of the authors' knowledge, this study is among the first to identify and analyze the use of ChatGPT by knowledge workers across different industries. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Purpose: This paper aims to demonstrate how the new generative artificial intelligence (AI) tool ChatGPT changes knowledge work for individuals and what are the implications of this change for companies. Design/methodology/approach: Based on 22 interviews from informants across different industries, the authors conducted an inductive analysis on the use and utility of ChatGPT in knowledge work. Based on this initial analysis, they discovered different ways in which ChatGPT either augments human agency, makes it redundant or lacks capability in that regard. Findings: The authors develop a 2 × 2 framework of algorithmic assistance, which demonstrates four ways in which ChatGPT (and generative AI in general) interacts with knowledge workers, depending on the usefulness of ChatGPT in particular tasks and the type of the task (routine vs creative). Practical implications: Based on the insights from the interviews, the authors propose a set of actionable questions for individual knowledge workers and companies from four viewpoints: skills and capabilities; team structure and workflow coordination; culture and mindset; and business model innovation. Originality/value: To the best of the authors' knowledge, this study is among the first to identify and analyze the use of ChatGPT by knowledge workers across different industries. [ABSTRACT FROM AUTHOR]
ISSN:02756668
DOI:10.1108/JBS-05-2023-0094