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Exploring the Competency for AI-related Job Positions [Abstract]

2026 · InSITE Conference · pp. 18 · 0 citations

TL;DR

An overall picture of the critical competency in the AI-related job positions is provided, which helps job seekers, students in higher education, and recent graduates to tailor their skill sets and make strategic decisions about their career development.

Abstract

Aim/Purpose The emergence of artificial intelligence (AI) has transformed job roles in the labor market. Drawing on McClelland’s Theory of Competencies, this study aims to explore the core competencies required for AI-related positions. Background AI has become increasingly important in today’s business environment, with its applications continuing to expand across various business settings. The growing demand for AI-related job positions highlights the need to identify the industry-required competencies expected of job applicants. To address the gap between higher education and the labor market, this study explores the critical competencies required for AI-related job positions. Methodology Using Python-based web crawling techniques, this study collected 8,256 AI-related job postings from an online job bank across various companies. Following data cleaning and preprocessing, the study conducted a series of text analyses, including term frequency analysis, word co-occurrence network analysis, and latent Dirichlet allocation (LDA) topic modeling, to identify key patterns and competency requirements embedded in the job postings. Contribution This paper contributes to the literature by providing a comprehensive understanding of the critical competencies required for AI-related job positions. The findings also offer practical implications for job seekers, higher education students, and recent graduates by enabling them to tailor their skill sets and make informed decisions about their career development. Furthermore, by bridging the gap between academia and industry, this study provides valuable insights for curriculum design and highlights the AI-related competencies that graduates need to develop in order to succeed in the rapidly evolving AI job market. Findings The text analysis generated several key findings. First, AI-related job positions require proficiency in tools and technologies such as Python, C++, GitHub, C, Linux, C#, JavaScript, Java, and MySQL. Second, the core work skills identified in these positions include software programming, software engineering system development, machine learning, system architectures planning, database programming, artificial intelligence, and system integration analysis. Third, employers emphasize soft skills such as passion, proactivity, problem-solving, teamwork, and communication. Finally, AI applications are increasingly embedded in diverse business functions, including e-commerce, marketing, project management, and social media content planning, reflecting the expanding demand for AI competencies across industries. Recommendations for Practitioners Our findings show the practical demands and requirements of the competency from AI-related job positions. This paper provides an overall picture of the critical competency in the AI-related job positions. In addition, the findings of this paper contribute to job seekers, students in higher education, and recent graduates to tailor their skill sets and make strategic decisions about their career development. By bridging the gap between academia and industry, our findings can benefit curriculum design and encourage graduates to work on the AI competencies needed to thrive in the ever-changing AI job market. Recommendations for Researchers Based on the McClelland’s Theory of Competencies at work, this study identified the core AI-related competency. Both hard skills and soft skills shape the core competency for AI-related job positions. Impact on Society This study reduces the gap between higher education and the AI job market by providing clear, data-driven insights into the competencies employers actually demand. By guiding curriculum design and helping individuals develop relevant skills, it supports a more efficient, inclusive, and future-ready workforce. Future Research Future research should examine how AI competency requirements evolve over time across industries and how effectively educational interventions can adapt to meet these dynamic labor market demands.

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