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Research Paper | Human Resource Management | India | Volume 10 Issue 11, November 2021 | Popularity: 5.8 / 10

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Revolutionizing HR: AI-Driven Digital Transformation for a Competitive Edge

Ramesh Nyathani

Abstract: Human Resources (HR) has historically been perceived as an administrative function. However, the advent of Artificial Intelligence (AI) and digital transformation has opened the doors to revolutionary changes in HR, ensuring a strategic advantage for businesses. This paper delves into the impact of AI on HR practices, highlighting its role in reshaping recruitment, employee engagement, learning and development, and predictive analytics. The integration of Artificial Intelligence (AI) into Human Resources (HR) management has unfolded a new chapter in steering organizational capabilities and enhancing workforce management towards a more efficacious and employee-centric approach. AI, distinguished for its adeptness at interpreting, learning from, and predicting data patterns, unveils new vistas in optimized decision-making processes, positioning itself as an indomitable asset in HR technological architectures. This paper aims to shed light on the pivotal ramifications and potential challenges interwoven with the incorporation of AI into various HR domains, such as talent acquisition, employee engagement, and data-driven decision-making, all whilst maintaining a lens on ethical and data security considerations. By leveraging specific use-cases and innovative models from various industries, the paper delves into AI's transformative potential in molding HR functionalities that are not only operationally superior but also intrinsically employee-oriented. Furthermore, the discussion underscores the criticality of AI-driven HR digital transformation in sustaining and augmenting a competitive edge in the fluctuating technological and economic landscape, propelling enhanced strategic management and operational efficiency.

Keywords: Artificial Intelligence, HR, Digital Transformation, Recruitment, Employee Engagement, Predictive Analytics

Edition: Volume 10 Issue 11, November 2021

Pages: 1465 - 1469

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