THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING KPIS AND OPTIMIZATION OF HUMAN RESOURCE MANAGEMENT

Abstract

MANAGEMENT SUMMARY This thesis explores how artificial intelligence (AI) can enhance human resource management (HRM), particularly in recruitment and onboarding. The study focuses on LinkedIn's AI tools, aiming to understand their effectiveness in improving key performance indicators (KPIs) and optimizing HR processes. The research draws on a broad literature review, examining the evolution of AI in HR. AI has shown potential in automating tasks like candidate screening and onboarding, but there are challenges, including biases in AI systems and the need for continuous improvement. Using Saunders' research onion framework, a mixed-methods approach was adopted, combining surveys and interviews with HR professionals who use LinkedIn's AI tools. This approach provided a comprehensive view of AI's impact on HRM. The results indicate that AI tools significantly enhance effectiveness by automating repetitive tasks and improving candidate matching, thus reducing the time-to-hire and increasing accuracy. However, some challenges remain, such as occasional inaccuracies and the need for better user training. It's clear that refining AI algorithms and incorporating human oversight can help address these issues. In onboarding, AI tools have been successful in automating administrative tasks and personalizing the onboarding experience. Feedback suggests that AI-driven processes help new hires feel more supported and prepared. The study concludes with recommendations for further research and practical steps for implementation. It highlights the need for ongoing refinement of AI tools, better integration practices, and comprehensive training for HR professionals. Future research should focus on long-term impacts and best practices for AI in HRM. In summary, AI has the potential to transform HRM by enhancing KPIs and optimizing processes. However, a balanced approach that combines technology with human judgment is essential for maximizing these benefits. This thesis provides a foundation for future advancements in using AI in HRM.