Empowering Employees: Unlocking the Benefits of Employee Self - Service by AI Driven HCM Platforms
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Human Resource Management | India | Volume 12 Issue 9, September 2023 | Popularity: 5.2 / 10


     

Empowering Employees: Unlocking the Benefits of Employee Self - Service by AI Driven HCM Platforms

Ramesh Nyathani


Abstract: As the nexus between Artificial Intelligence (AI) and Human Capital Management (HCM) continues to strengthen, the emergence of AI - driven Employee Self - Service (ESS) HCM platforms stands as a testament to the transformative power of technology in the workplace. This research sheds light on the multifaceted benefits of such platforms in enhancing employee autonomy, engagement, and operational efficiency. This research underscores how these platforms enable employees to manage personal and professional data seamlessly, access real - time feedback, and benefit from personalized solutions tailored to individual needs. The results indicate a pronounced uptick in employee satisfaction and a notable decrease in operational costs for organizations employing AI - driven self - service solutions. Also reaffirms the imperative for organizations to adopt these cutting - edge platforms, leveraging AI's capabilities to foster an empowered, efficient, and forward - thinking workforce.


Keywords: Human Capital Management, Employee Engagement, Self - Service, Artificial Intelligence, HR Platforms, Predictive Analytics


Edition: Volume 12 Issue 9, September 2023


Pages: 2176 - 2180


DOI: https://www.doi.org/10.21275/SR231030131808



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Ramesh Nyathani, "Empowering Employees: Unlocking the Benefits of Employee Self - Service by AI Driven HCM Platforms", International Journal of Science and Research (IJSR), Volume 12 Issue 9, September 2023, pp. 2176-2180, https://www.ijsr.net/getabstract.php?paperid=SR231030131808, DOI: https://www.doi.org/10.21275/SR231030131808

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