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AI-powered HR analytics: Transforming workforce optimization and decision-making

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Latifat Ayanponle 1, *, Chinenye Gbemisola Okatta 2 and Daniel Ajiga 3

1 Independent Researcher, Texas, USA.
2 Independent Researcher, Maryland, USA.
3 Independent Researcher, Seattle, USA.

Review Article
 
International Journal of Science and Research Archive, 2022, 05(02), 338–346.
Article DOI: 10.30574/ijsra.2022.5.2.0057
DOI url: https://doi.org/10.30574/ijsra.2022.5.2.0057

Received on 28 January 2022; revised on 10 March 2022; accepted on 13 March 2022

Integrating artificial intelligence and machine learning into human resource analytics has ushered in a transformative era for workforce management. This paper explores the applications of AI-driven predictive analytics in optimizing employee performance, enhancing decision-making, and leveraging data-driven insights. It highlights the role of machine learning models in talent acquisition and retention, streamlining recruitment processes, and improving employee satisfaction through personalized strategies. Ethical and sustainable practices are emphasized, addressing concerns about bias, transparency, and inclusivity in AI systems, while promoting long-term sustainability in AI-driven HR processes. The study concludes with actionable recommendations for organizations to integrate AI effectively into HR, including developing strategic implementation plans, ensuring data quality, fostering transparency, and prioritizing ethical and sustainable practices. These insights underline AI's potential to revolutionize HR while emphasizing the need for responsible and inclusive deployment.

Artificial Intelligence (AI); HR Analytics; Predictive Analytics; Machine Learning; Workforce Optimization; Ethical AI Practices

https://ijsra.co.in/sites/default/files/fulltext_pdf/IJSRA-2022-0057.pdf

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Latifat Ayanponle, Chinenye Gbemisola Okatta and Daniel Ajiga. AI-powered HR analytics: Transforming workforce optimization and decision-making. International Journal of Science and Research Archive, 2022, 05(02), 338–346. Article DOI: https://doi.org/10.30574/ijsra.2022.5.2.0057

Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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