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A study on the impact of artificial intelligence on talent sourcing

IAES International Journal of Artificial Intelligence · Vol. 13 No. 1 (2024) · hlm. 1–8

DOI
10.11591/ijai.v13.i1.pp1-8
ISSN
2252-8938, 2089-4872
Terbit
2024-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Talent sourcing is one of the most effective mechanisms to engage with the talent pool and convert a candidate into an applicant. Today, machine learning has emerged as a trend to assist employers in addressing recruitment challeng-es with the help of tools such as neuro-linguistic programming (NLP) and automated assessments. 80% of the executives strongly believe deep learning makes candidate screening highly efficient. Including current start-ups globally, only 15% use artificial intelligence (AI) and are expected to increase by 31%. The study focused on the impact of AI in recruitment process. There are a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on. Here we would like to analyze the impact of using AI in various phases of hiring in the organization.

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  • APJ Abdul Kalam Technological University
  • Mohan Babu University
  • Universiti Teknologi MARA
  • Kurukshetra University
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A study on the impact of artificial intelligence on talent sourcing</dc:title>
	<dc:creator>Hemachandran, Varun Chand</dc:creator>
	<dc:creator>Kumar, Kurakula Arun</dc:creator>
	<dc:creator>Sikanda, Syarul Azlina</dc:creator>
	<dc:creator>Sabharwal, Seema</dc:creator>
	<dc:creator>Kumar, Sivaprakasam Arun</dc:creator>
	<dc:subject xml:lang="en-US">Talent Sourcing, Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Candidate evaluation; Machine learning; Neuro-linguistic programming; Predictive analytics; Screening;</dc:subject>
	<dc:description xml:lang="en-US">Talent sourcing is one of the most effective mechanisms to engage with the talent pool and convert a candidate into an applicant. Today, machine learning has emerged as a trend to assist employers in addressing recruitment challeng-es with the help of tools such as neuro-linguistic programming (NLP) and automated assessments. 80% of the executives strongly believe deep learning makes candidate screening highly efficient. Including current start-ups globally, only 15% use artificial intelligence (AI) and are expected to increase by 31%. The study focused on the impact of AI in recruitment process. There are a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on. Here we would like to analyze the impact of using AI in various phases of hiring in the organization.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22252</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i1.pp1-8</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 1: March 2024; 1-8</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22252/13795</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>