Purpose

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

Design/methodology/approach

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

Findings

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

Research limitations/implications

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Practical implications

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Social implications

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

Originality/value

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

中文翻译：

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组织人工智能技术部署能力的自动化增强悖论；实现经济效益和社会效益同步的实证研究

目的

本研究的目的是了解组织中使用机器学习 (ML) 和人工智能 (AI) 同时带来的竞争和社会收益。关于机器学习和人工智能技术的部署的贡献及其对组织和社会的影响存在知识断层。

设计/方法论/途径

这项研究基于动态能力 (DC) 以及机器学习和人工智能自动化增强悖论文献。这项研究利用 239 名印度组织首席技术官 (CTO) 的回答来检验这些理论观点。应用偏最小二乘结构方程模型（PLS-SEM）路径模型进行数据分析。

发现

结果表明，机器学习和人工智能技术的组织使用对 DC 计划产生了积极影响。研究结果表明，DC 完全调节了基于机器学习和人工智能的技术对公司绩效和社会绩效的影响。

研究局限性/影响

这项研究促进了有关机器学习和人工智能技术的组织和社会结果之间紧张关系的理论讨论。该研究扩展了 DC 的作用，使其成为通过 ML 和 AI 的使用实现社会效益的重要战略。此外，还探讨了自动化增强悖论的理论张力。

实际影响

部署机器学习和人工智能技术的组织可以应用本研究的见解来理解组织惯例，以同时追求竞争优势和社会收益。此外，组织的首席技术主管可以从数据中心的角度设计机器学习和人工智能技术的使用如何帮助解决自动化增强悖论的紧张局势。

社会影响

组织中机器学习和人工智能技术使用的增加增强了数据中心。它们可以带来积极的社会效益，例如创造新的就业机会、增加熟练员工的薪酬以及提高就业中的性别参与度。这些见解可以根据这项研究得出。

原创性/价值

这项研究是首批进行的几项实证研究之一，旨在提供关于组织中使用 ML 和 AI 因其数据中心而带来的组织和社会效益的理论和实践见解。这项研究也是最早解决企业层面的自动化增强悖论的实证研究之一。