{"id":11170,"date":"2026-08-26T21:38:14","date_gmt":"2026-08-26T13:38:14","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11170"},"modified":"2026-08-26T23:07:26","modified_gmt":"2026-08-26T15:07:26","slug":"jase-202612-35-006","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-006","title":{"rendered":"Applying Artificial Intelligence to Develop Adaptive Talent Management and Succession Planning Systems in HRM"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Zhu Lin and Shuai Dong<\/i><\/a><\/p>\n\n\n\n

College of Business Administration, Gingko College of Hospitality Management, Chengdu City, 611743, Sichuan Province, China<\/p>\n<\/div>\n\n\n\n

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Received: May 13, 2026
Accepted: July 29, 2026
Publication Date: August 26, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Applying Artificial Intelligence To Develop Adaptive Talent Management And Succession Planning Systems In HRM<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.006<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Human Resource Management (HRM) is increasingly driven by artificial intelligence, yet many HR analytics models remain static and fail to capture evolving career patterns. This study proposes an adaptive AI framework integrating Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models for employee attrition prediction and career-stage progression modeling. Using the IBM HR Analytics dataset, the framework employs weighted feature aggregation, DNN-based classification, and LSTM-driven temporal analysis, alongside an adaptive retraining mechanism. Results show improved performance with 95.58% accuracy, 1.00 precision, 72.34 recall, 83.95 F1-score, and 0.84 ROC-AUC. The model also enhances minority-class recall and PR-AUC under class imbalance. Overall, the framework offers a scalable and intelligent solution for succession planning and data-driven workforce management in dynamic organizational environments.<\/p>\n\n\n\n

Keywords: Adaptive Learning, Talent Management, Succession Planning, Employee Attrition, Deep Neural Networks<\/em><\/p>\n\n\n\n

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