AI in HR- Leveraging Machine Learning for Talent Management.pptx

This presentation, delivered at the HR Tech Conference 2022, explores how AI and machine learning can optimize talent management processes. Key topics include predictive analytics for employee turnover, AI techniques for recruitment efficiency, and performance forecasting. Real-world case studies demonstrate the significant improvements in HR outcomes using AI.

1. AI in HR: Leveraging Machine Learning for Talent Management HR Tech Conference, 2022 Presenter: Cora Jones

2. Introduction

3. Predictive Analytics for Employee Turnover Predictive analytics can help identify employees at risk of leaving the company. This proactive approach allows HR to take action before turnover occurs. Machine Learning Models Used: Decision Trees, Random Forests. Data Collection and Preprocessing: We gather data from various sources such as employee surveys, performance metrics, and engagement scores. The data is then cleaned and normalized to ensure accuracy.

4. Recruitment Efficiency AI optimizes recruitment processes by screening resumes and matching candidates to job descriptions. This leads to faster hiring times and better candidate fit. Predictive Models for Candidate Screening: Logistic Regression, Support Vector Machines (SVM). Benefits and Outcomes: Reduced time-to-hire. Improved quality of hire. Reduced bias in candidate selection.

5. Performance Forecasting

6. Case Studies Let’s look at two case studies where AI significantly improved HR processes. Case Study 1: Reduced Turnover Rate at Company Wells Fargo. After implementing predictive analytics, the turnover rate dropped by 20% within a year. Case Study 2: Improved Recruitment Efficiency at Wells Fargo. The average time-to-hire decreased by 30%, and the quality of hire improved by 15%.

7. Conclusion