IDEAS home Printed from https://ideas.repec.org/a/bja/isteus/y2025i1p113-125.html
   My bibliography  Save this article

The application of artificial intelligence tools in HR

Author

Listed:
  • Dmytro Kobets

    (Khmelnytskyi National University)

  • Olena Mantur-Chubata

    (Khmelnytskyi National University)

Abstract

an resource management by facilitating process automation, enhancing the efficiency of HR decision-making, and optimizing managerial functions. This article analyzes contemporary approaches to the application of AI tools in HR management, identifies their key advantages, potential challenges, and prospects for integration into human resource management practices. Specifically, the study examines the primary directions for AI implementation in HR, including recruitment automation, personalized career development management, employee behavior prediction, performance assessment, and internal communication enhancement. The use of chatbots, machine learning algorithms for resume screening and soft skills analysis, as well as software solutions for conducting video interviews, significantly reduces the time required for talent acquisition and selection, thereby improving the decision-making process. Additionally, AI systems can contribute to employee attrition forecasting and psychological well-being assessment, which are crucial factors in maintaining a healthy work environment. However, despite its numerous advantages, the application of AI in HR is not without challenges. Key risks include algorithmic bias, potential data privacy breaches, the lack of emotional intelligence in decision-making, and difficulties in adapting traditional management methods to new digital realities. Another important aspect is the need for HR professionals to enhance their competencies in digital technology, as the successful integration of AI into HR processes requires not only technical implementation but also a shift in human resource management approaches. The findings of this study have practical significance for companies seeking to improve HR management efficiency through digital technology adoption. It has been established that AI implementation enhances the productivity of HR processes, reduces administrative burdens on HR departments, minimizes human bias in decision-making, and ensures a personalized approach to each employee. At the same time, the necessity of developing ethical standards for AI application has been emphasized to ensure a harmonious balance between technological innovations and organizational management needs. Future research in this field should focus on developing comprehensive strategies for AI integration into HR, considering the socio-cultural characteristics of employees, and achieving an optimal balance between automation and the human factor in human resource management processes.

Suggested Citation

  • Dmytro Kobets & Olena Mantur-Chubata, 2025. "The application of artificial intelligence tools in HR," Economic Synergy, Higher Educational Institution Academician Yuriy Bugay International Scientific & Technical University, issue 1, pages 113-125.
  • Handle: RePEc:bja:isteus:y:2025:i:1:p:113-125
    DOI: 10.53920/ES-2025-1-8
    as

    Download full text from publisher

    File URL: https://es.istu.edu.ua/EconomicSynergy/article/view/254/189
    Download Restriction: no

    File URL: https://libkey.io/10.53920/ES-2025-1-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    artificial Intelligence; HR process automation; employee behavior prediction; employee performance evaluation; ethical standards of AI; personalized career development management; integration of digital technologies in HR;
    All these keywords.

    JEL classification:

    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • M54 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Management

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bja:isteus:y:2025:i:1:p:113-125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anna Duchenko (email available below). General contact details of provider: https://es.istu.edu.ua/EconomicSynergy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.