Unlocking the value of artificial intelligence in human resource management through AI capability framework


Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner’s standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM.

Publication DOI: https://doi.org/10.1016/j.hrmr.2022.100899
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School > Aston India Foundation for Applied Research
College of Business and Social Sciences > Aston Business School
Additional Information: © 2022, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Publication ISSN: 1873-7889
Full Text Link:
Related URLs: https://www.sci ... 053482222000079 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-03-23
Published Online Date: 2022-03-23
Accepted Date: 2022-02-25
Authors: Chowdhury, Soumyadeb
Dey, Prasanta (ORCID Profile 0000-0002-9984-5374)
Joel-Edgar, Sian
Bhattacharya, Sudeshna
Rodríguez-Espíndola, Oscar (ORCID Profile 0000-0002-4889-1565)
Abadie, Amelie
Truong, Linh



Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 23 March 2024.

License: Creative Commons Attribution Non-commercial No Derivatives

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