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.

在人力资源管理领域，我们提供了一份关于人工智能技术在人才分析方面的综合调查，首先提供了背景知识和分类相关研究努力，然后根据三种应用驱动场景对其进行了综合分类，最后总结了 AI 驱动的人才分析在未来研究方向的挑战和潜在前景。

人才分析的人工智能技术综述

A Comprehensive Survey of Artificial Intelligence Techniques for Talent  Analytics

AI has the potential to improve approaches to talent management enabling
dynamic provisions through implementing advanced automation. This study aims to
identify the new requirements for developing AI-oriented artifacts to address
talent management issues. Focusing on enhancing interactions between
professional assessment and planning attributes, the design artifact is an
intelligent employment automation solution for career guidance that is largely
dependent on a talent intelligent module and an individuals growth needs. A
design science method is adopted for conducting the experimental study with
structured machine learning techniques which is the primary element of a
comprehensive AI solution framework informed through a proposed moderation of
the technology-organization-environment theory.

本研究旨在设计一个人才管理智能化解决方案，通过 AI、自动化和机器学习等技术手段提高专业评估和职业规划之间的交互，并以人才智能模块和个人成长需求为基础，开发面向职业指导的智能就业自动化解决方案。通过在技术、组织和环境理论的基础上进行的设计科学方法实验研究，提出了一种综合 AI 解决方案框架。