[Paper Review] A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

Chuan Qin, Le Zhang+9 more|arXiv (Cornell University)|Jul 3, 2023

AI and HR Technologies18 citations

TL;DR

This paper surveys AI techniques for talent analytics across talent management, organization management, and labor market analysis, emphasizing data foundations and methodological taxonomies.

ABSTRACT

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.

Motivation & Objective

• Provide a structured overview of talent analytics data foundations (internal and external data).
• Categorize AI techniques and models used in talent management, organization management, and labor market analysis.
• Summarize representative studies and data-to-method mappings in each scenario.
• Identify open challenges and future research directions in AI-driven talent analytics.

Proposed method

• Proposes a data-centric taxonomy organizing internal (recruitment, employee, organizational) and external data sources.
• Develops a three-scenario taxonomy: talent management, organization management, and labor market analysis.
• Reviews AI techniques and models applied to each data category, including NLP, deep learning, graph-based methods, and traditional classifiers.
• Provides tables that summarize data categories and AI approaches across scenarios.
• Discusses industry-scale data sources and trends (e.g., Indeed, LinkedIn, employer branding data) to motivate modeling choices.
• Outlines open challenges and potential future research directions.

Experimental results

Research questions

• RQ1 What data are essential for AI-enabled talent analytics across different organizational contexts?
• RQ2 What AI techniques have been applied to talent management, organization management, and labor market analysis, and how are they combined with data types?
• RQ3 What are the key challenges and future directions for AI-driven talent analytics?
• RQ4 How can large-scale external labor market data be integrated with internal HR data to inform decision-making?

Key findings

• AI-enabled talent analytics leverages large-scale internal and external data to inform recruitment, development, retention, and organizational decisions.
• Deep learning, NLP, and representation learning (e.g., BERT, CNN/RNN, LSTM) are widely used for resume/job posting matching, talent searching, and interview analysis.
• External data sources such as social media and job-search websites (e.g., LinkedIn, Indeed) provide rich context for labor market analysis and employer branding.
• Case examples illustrate impact, such as IBM achieving high prediction accuracy in turnover-related tasks and substantial savings in retention costs.
• The survey highlights a data-driven, taxonomy-based view as a foundation for integrating AI into talent analytics and identifying future research directions.