AI-Augmented HRM: Literature review and a proposed multilevel framework for future research

Verma Prikshat, Mohammad Islam, Parth Patel, Ashish Malik*, Pawan Budhwar, Suraksha Gupta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The research using artificial intelligence (AI) applications in HRM functional areas has gained much traction and a steep surge over the last three years. The extant literature observes that contemporary AI applications have augmented HR functionalities. AI-Augmented HRM HRM(AI) has assumed strategic importance for achieving HRM domain-level outcomes and organisational outcomes for a sustainable competitive advantage. Moreover, there is increasing evidence of literature reviews pertaining to the use of AI applications in different management disciplines (i.e., marketing, supply chain, accounting, hospitality, and education). There is a considerable gap in existing studies regarding a focused, systematic literature review on HRM(AI), specifically for a multilevel framework that can offer research scholars a platform to conduct potential future research. To address this gap, the authors present a systematic literature review (SLR) of 56 articles published in 35 peer-reviewed academic journals from October 1990 to December 2021. The purpose is to analyse the context (i.e., chronological distribution, geographic spread, sector-wise distribution, theories, and methods used) and the theoretical content (key themes) of HRM(AI) research and identify gaps to present a robust multilevel framework for future research. Based upon this SLR, the authors identify noticeable research gaps, mainly stemming from - unequal distribution of previous HRM(AI) research in terms of the smaller number of sector/country-specific studies, absence of sound theoretical base/frameworks, more research on routine HR functions(i.e. recruitment and selection) and significantly less empirical research. We also found minimal research evidence that links HRM(AI) and organisational-level outcomes. To overcome this gap, we propose a multilevel framework that offers a platform for future researchers to draw linkage among diverse variables starting from the contextual level to HRM and organisational level outcomes that eventually enhance operational and financial organisational performance.

Original languageEnglish
Article number122645
Pages (from-to)1-19
Number of pages19
JournalTechnological Forecasting and Social Change
Early online date24 May 2023
Publication statusPublished - Aug 2023

Bibliographical note

© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (


  • AI applications in HRM
  • AI-Augmented HRM
  • Artificial intelligence
  • HRM
  • Literature review


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