Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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System (ATS) for Revolutionizing Recruitment: AI Powered Application Tracking S Small Enterprises

Authors
M. Kiruthiga Devi1, Herbert Ashwin Moraes2, *, S. Arkeshwar3, M. Nikhil Kishore4
1Assistant Professor (Sr. G), Departmentof CSE, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
2Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, VadapalaniCampus, Chennai, India
3Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
4Student, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
*Corresponding author. Email: ashwinmoraes@gmail.com
Corresponding Author
Herbert Ashwin Moraes
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_41How to use a DOI?
Keywords
AI-powered ATS; small businesses; resume parsing; job matching; recruitment automation; machine learning; natural language processing; hiring efficiency; candidate ranking; comparative analysis
Abstract

Hiring is often very hard for small businesses because they don’t have enough people, their budgets are tight, and they get a lot of applications. Traditional Applicant Tracking Systems (ATS) work well, but they are often too expensive and complicated for small businesses to use. An AI-powered ATS made just for small businesses is a useful and effective way to automate resume parsing and job matching using machine learning and natural language processing (NLP). This smart system lets recruiters post jobs and gets a list of candidates ranked by how qualified and relevant they are. It also lets applicants upload their resumes for instant review. A statistical study of 15 small businesses showed that using automated methods cut screening time by 45% and improved the quality of candidate-job matches by 30% compared to doing it by hand. When compared to other ATS platforms, the AI-driven system was found to be just as accurate or even more accurate, while being much cheaper and easier to use. These systems help small businesses make hiring decisions faster, fairer, and with more information by making the hiring process easier and less biased.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_41How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Kiruthiga Devi
AU  - Herbert Ashwin Moraes
AU  - S. Arkeshwar
AU  - M. Nikhil Kishore
PY  - 2025
DA  - 2025/10/31
TI  - System (ATS) for Revolutionizing Recruitment: AI Powered Application Tracking S Small Enterprises
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 489
EP  - 502
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_41
DO  - 10.2991/978-94-6463-866-0_41
ID  - Devi2025
ER  -