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Using AI, ChatGPT, and ML tools in HRM: Insights from a Pakistani University Professor

Muhammad Rizwan Hussain

South Asian Journal of Human Resources Management2026https://doi.org/10.1177/23220937261419545article
AJG 1ABDC C
Weight
0.50

What the paper says

Artificial intelligence (AI) is transforming human resource management (HRM) globally, with South Asian organisations increasingly leveraging AI-driven solutions for recruitment, onboarding, performance management and compensation. However, unique challenges in the region—including high applicant volumes, skill shortages, informal recruitment practices and sociocultural complexities—shape the application and impact of AI across these HRM domains. This article examines these issues through an in-depth interview with Dr Khalid Bin Muhammad, an expert in both information technology and HRM, who offers nuanced insights into how AI can address pressing needs in efficient candidate screening, inclusive workplace practices, data-driven employee assessment and fair compensation frameworks. The discussion underscores the imperative of regionally adapted AI models and ethical oversight to mitigate potential biases. By bridging theoretical perspectives with practical examples, the findings offer actionable recommendations for HR professionals and decision-makers in South Asia to foster more effective, equitable and future-ready HRM practices through the responsible adoption of AI.

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https://doi.org/https://doi.org/10.1177/23220937261419545

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@article{muhammad2026,
  title        = {{Using AI, ChatGPT, and ML tools in HRM: Insights from a Pakistani University Professor}},
  author       = {Muhammad Rizwan Hussain},
  journal      = {South Asian Journal of Human Resources Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/23220937261419545},
}

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Using AI, ChatGPT, and ML tools in HRM: Insights from a Pakistani University Professor

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.