Optimizing HR Systems through Machine Learning: A Case Study on Automation and Cost Reduction in People Operations
Keywords:
Machine Learning (ML), Human Resources (HR), Automation, Cost Reduction, Recruitment, Payroll, Employee Engagement, Data Integration, Predictive Analytics, Operational EfficiencyAbstract
This paper aims to discuss the application of machine learning (ML) for enhancing human resource (HR) systems concerning the Automation of HR activities to enhance cost-effectiveness and efficiency. Recruiting automation can significantly reduce expenses on digital platforms; the same goes for the payroll and engagement chores. The paper describes some of the ML techniques and potential issues related to implementing Automation in the HRM frameworks while underlining the consequences for developing global HR technology, organizational effectiveness, and national economies. It explores how the efficiency of human resource operations can be boosted and the quality of the HR department's decisions by automating several Automations. Moreover, the paper discusses the possibility of applying ML to reframe HR practices by enhancing the speed of HR operations, minimizing errors, and enhancing individual approaches to managing workers. However, there are certain risks associated with the integration of ML into HR systems, which include issues with data privacy and the fact that AI requires persons with certain qualifications and skills to operate it; all these notwithstanding, the use of AI in human resource management presents the future as a field of opportunities for organizations to embrace change. The study means that HR can leverage ML to reduce cost and increase employee satisfaction and thus help enhance the performance of organizations and larger economies. It is concluded in the paper that machine learning is an enabler that can have deep implications for changing human resource management practices and boosting economic development.
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