Organizational Implementation of AI: Craft and Mechanical Work

IF 6.3 2区 管理学 Q1 BUSINESS
Konstantin Hopf, Oliver Müller, Arisa Shollo, Tiemo Thiess
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引用次数: 0

Abstract

Recent years have brought major technological breakthroughs in artificial intelligence (AI), and firms are expected to invest nearly $98 B in 2023. However, many AI projects never leave the pilot phase, and many companies have difficulties extracting value from their AI initiatives. To explain this contradiction, this article reports on a study of 55 projects implementing AI in organizations. It shows that organizational challenges in implementing AI projects are a result of a paradoxical tension created by two different perspectives on data science work: craft and mechanical work. Executives, managers, and data scientists should actively manage this tension to enable and sustain value creation through AI.
人工智能的组织实施:工艺和机械工作
近年来,人工智能(AI)领域取得了重大技术突破,预计到2023年,企业将在人工智能领域投资近980亿美元。然而,许多人工智能项目从未离开试点阶段,许多公司很难从他们的人工智能计划中提取价值。为了解释这一矛盾,本文报告了对55个在组织中实施人工智能的项目的研究。它表明,实施人工智能项目的组织挑战是数据科学工作的两种不同观点(工艺和机械工作)造成的矛盾张力的结果。高管、经理和数据科学家应该积极管理这种紧张关系,以通过人工智能实现和维持价值创造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
18.40
自引率
3.00%
发文量
23
期刊介绍: California Management Review (CMR) serves as a vital link between academia and management practice, offering leading-edge research with practical applications. Edited at UC Berkeley’s Haas School of Business, CMR covers a wide range of management topics, including innovation, strategy, sustainable practices, and human resources. CMR ranks among the top management journals globally, distributing articles through outlets like Harvard Business School Publishing and SAGE Publishing. Focused on bridging academia and practice, CMR ensures that all articles are based on rigorous academic research while providing actionable insights for managers. In addition to regular issues, CMR publishes special issues and sections on relevant topics, often guest-edited by leading faculty. Recent special issues have covered dynamic capabilities, city innovation, big data, and intellectual property management.
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