stdClass Object
(
    [id] => 15949
    [paper_index] => 202505-07-021524
    [title] => EMPLOYEE RETENTION THROUGH AI: AN AI-DRIVEN APPROACH TO MITIGATING ATTRITION AND ENHANCING WORKFORCE STABILITY
    [description] => 
    [author] => Ms. Kaviya S, Dr. A. Giri Prakash, Dr.K.Priyatharsini
    [googlescholar] => 
    [doi] => 
    [year] => 2025
    [month] => May
    [volume] => 12
    [issue] => 5
    [file] => fm/jpanel/upload/2025/May/202505-07-021524.pdf
    [abstract] => Security of human resources stands as a crucial issue in modern flexible operating environments because employee departures create operational instability combined with severe financial consequences. An AI system will be examined throughout this research to combat employee attrition and strengthen organizational stability. Through predictive algorithms together with machine learning models and advanced analytics systems organizations obtain the capability to recognize at-risk staff members before their attrition and to identify dissatisfaction sources which enable them to apply customized solutions. The deployment of AI tools in human resource management allows real-time employee engagement checks and sentiment analysis together with customized career development paths which generates organizational support systems. Through this approach organizations enhance their business success along with reducing turnover rates and building organizational strength and boosting employee satisfaction rates. Through this study implementable methods are presented for various industries to use artificial intelligence for transforming employee retention strategies while demonstrating its potential to redefine workforce retention practices.
    [keywords] => Workforce Stability, Employee Attrition, Predictive Algorithms, Advanced Analytics, Employee Engagement, Turnover Reduction, Workforce Retention Strategies.
    [doj] => 2025-05-11
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 30
    [journal_id] => 7
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Economics, Business and Management Studies (EBMS)
    [short_code] => IJHS
    [eissn] => 2347-4378
    [pissn] => 
    [home_page_wrapper] => images/products_image/2.EBMS.png
)
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