Decoding AI readiness: An in-depth analysis of key dimensions in multinational corporations

Document Type

Journal Article

Publication Title

Technovation

Volume

131

Publisher

Elsevier

School

School of Business and Law

RAS ID

62570

Funders

University of Western Australia

Comments

Tehrani, A. N., Ray, S., Roy, S. K., Gruner, R. L., & Appio, F. P. (2024). Decoding AI readiness: An in-depth analysis of key dimensions in multinational corporations. Technovation, 131, article 102948. https://doi.org/10.1016/j.technovation.2023.102948

Abstract

Artificial Intelligence (AI) stands ready to impact all aspects of business, from optimizing operations to personalizing services and enhancing customer value. However, many organizations grapple with implementing AI solutions due to a lack of necessary infrastructure and mechanisms. In short, many companies are not adequately prepared to adopt AI. To make matters worse, the literature does not offer sufficient insights into this issue. To help address this issue, in this article, the authors explore what it means to become ‘AI-ready.’ Specifically, this study identifies the various dimensions of AI readiness through in-depth semi-structured interviews with top- and middle-level managers from 52 multinational corporations in Southeast Asia, primarily in India. This study employed a qualitative data analysis approach to construct a grounded theory model focusing on AI readiness. The methodology involved systematic examination and coding of data to identify key themes and patterns, enabling the development of a comprehensive theoretical framework. The findings suggest that AI readiness can be categorized into eight dimensions: informational, environmental, infrastructural, participants, process, customers, data, and technological readiness. This study makes a significant contribution to marketing, management, and information systems by conceptualizing the AI readiness construct and identifying its key dimensions.

DOI

10.1016/j.technovation.2023.102948

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