AI-augmented HRM: Antecedents, assimilation and multilevel consequences

Verma Prikshat*, Ashish Malik, Pawan Budhwar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The current literature on the use of disruptive innovative technologies, such as artificial intelligence (AI) for human resource management (HRM) function, lacks a theoretical basis for understanding. Further, the adoption and implementation of AI-augmented HRM, which holds promise for delivering several operational, relational and transformational benefits, is at best patchy and incomplete. Integrating the technology, organisation and people (TOP) framework with core elements of the theory of innovation assimilation and its impact on a range of AI-Augmented HRM outcomes, or what we refer to as (HRM(AI)), this paper develops a coherent and integrated theoretical framework of HRM(AI) assimilation. Such a framework is timely as several post-adoption challenges, such as the dark side of processual factors in innovation assimilation and system-level factors, which, if unattended, can lead to the opacity of AI applications, thereby affecting the success of any HRM(AI). Our model proposes several testable future research propositions for advancing scholarship in this area. We conclude with implications for theory and practice.
Original languageEnglish
Article number100860
JournalHuman Resource Management Review
Issue number1
Early online date15 Sept 2021
Publication statusPublished - Mar 2023

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • Technology-driven HRM
  • AI-adoption in HRM
  • AI-augmented HRM
  • Processual factors


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