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1. Introduction
The worldwide networks of data sharing and supply chains have led to rampant globalisation; resulting in greater geographical mobility, resource sharing, virtual working and teleworking in multinational corporations (MNCs) (Bolton et al., 2019). These advancements have transformed the global HRM function significantly. On one hand, there are opportunities for global talent acquisition, learnings from global HRM practices and benefits of knowledge sharing through manpower movement across geographies. On the other hand, there are challenges pertaining to managing global talent, balancing globalization vs local adaptations and developing HRM competencies aimed at strengthening manpower productivity (Brookes et al., 2017; Chiang et al., 2017).
Intelligent algorithms, based on artificial intelligence (AI) and machine learning (ML), help in resolving some of these challenges as well as in increasing efficiency (reduced cost and effort of data analysis and subsequent decision support) and/or effectiveness (improved quality of data analysis and subsequent decision support) of HRM. IBM and Microsoft are using AI and ML to identify applicants suitable for particular jobs (Castellanos, 2019); thereby standardizing applicant sourcing and resume screening methods for all their subsidiaries. Similarly, Club Med is digging into its employee data to identify factors contributing to their job satisfaction (Bolton et al., 2019); useful for designing personalized incentives to boost job satisfaction.
The term “Artificial Intelligence” was coined in 1956 by John McCarthy, who invited researchers from all over the world to discuss the possibility of computers becoming as intelligent as humans. Discussions of this conference gave birth to the interdisciplinary field which is referred today as AI. AI has become a large field of interest for researchers and practitioners globally. ML is a subset of AI, but often, the terms are used interchangeably. AI is wider in scope and includes the diverse technological developments that help a computer simulate human intelligence, whereas ML is a way to achieve AI and includes development of algorithms that improve upon themselves with experience (Colonna, 2013). While the rhetoric on AI applications in HRM has moved quickly, the ground reality shows that these applications are thriving on ML and analytics so far. A global survey conducted by KPMG, representing 1201 senior HR executives from 64 countries, reveals that less than 20% companies have invested in AI...