HR & Recruiting AI – Guide
Sourcing/matching/engagement/training: application, metrics, ethics/privacy.
Skills Analysis & L&D
AI analyzes skills and optimizes Learning & Development (L&D): analyzing skills for assessment of existing abilities and gaps. Planning L&D for personalized learning paths based on needs. Recommendations for courses or roles for career development based on skills and aspirations. Skills matching for internal mobility. Predictive analytics for career progression. Metrics: learning completion rate, skill improvement, internal mobility rate.
RAG-powered HR Assistants for Managers: RAG assistant for quick access
Predictive analytics for retention: AI predicts churn risks through behavioral analysis (engagement decline, productivity issues, sentiment changes). Retention prediction to identify high-risk employees. Personalized retention campaigns to address risks. Next-best action for recommendations of actions to retain. Integration with HRIS for data. Metrics: churn prediction accuracy > 85%, retention rate improvement 20%+, early intervention rate > 80%.
1. Integration with ATS/HRIS/LMS
Frequently asked questions
What is deep learning?
Deep learning is a family of machine learning methods that use multi-layer neural networks.
What is time-to-hire?
Time-to-hire measures the average duration from when a job posting goes live to when a candidate accepts an offer, aiming for a 50% or greater reduction.
What is quality of hire?
Quality of hire refers to the performance and retention rates of newly hired employees, with a target improvement of 20%.
What is employee retention?
Employee retention measures the percentage of employees who remain employed over a period, targeting an increase of 15%.
What is NPS for HR?
NPS (Net Promoter Score) measures employee satisfaction and loyalty, with a target score exceeding 50.
▶ Try it live
Everything above runs in your browser — open Earthquake Wave Propagation Simulation and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.