Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource C., Balakrishnan; J., Mahalakshmi; R., Stephen; Warrier, Gayathry S. Title A name given to the resource Artificial intelligence-powered talent acquisition and onboarding Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Harnessing Artificial Intelligence to Ensure Diverse Global Teams;pp.89-104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34135-9.00003-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34135-9.00003-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033410452?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033410452?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant C. B., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; J. M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; R. S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Warrier G.S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource Artificial intelligence (AI)-powered instantaneous recruitment analysis and onboarding of the candidates has gained more popularity in recent years, mainly after the COVID-19 phase, largely due to the incredible growth of data-driven content. Basically, the art of summarizing the entire content of the resume of the candidates and leveraging the resumes that are relevant to the job description takes more time-consuming by using the traditional methods. AI is revolutionizing the landscape of talent acquisition and onboarding for applicants/selected candidates, making it much easier than conventional techniques. By harnessing the power of advanced AI-based algorithms and machine learning, organizations and multinational corporations (MNCs) can streamline their recruitment processes, from identifying top talent to integrating new hires seamlessly. AI-based tools automate time-consuming tasks such as screening of the resumes of the voluminous data, scheduling interviews based on each individual job role, and sending out offer letters for the selected applicants. This automation frees up human resource professionals to focus on strategic initiatives and build stronger relationships with candidates. AI algorithms can analyze vast amounts of data to identify candidates who possess the exact skills and qualifications required for a specific role. This precision reduces the risk of mismatches and ensures a higher quality of hires. This article specifically focuses on the tools and techniques based on AI for making the recruitment process easier and their impacts on the actual landscape, along with some recent case studies. The impact of AI on this field of recruitment and its significance on the job market is well analyzed in this article. By embracing AI, organizations can unlock new opportunities for growth and innovation. AI-driven talent acquisition and onboarding not only improve efficiency and accuracy but also enhance the overall candidate experience and foster a more diverse and inclusive workforce. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource AI-driven talent acquisition; automation; data-driven insights; Large language models; OpenAI; predictive analysis; summarization Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334135-9; 978-044334136-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online