How do we define an occupation? By its job title? An accountant at a small trading company keeps the books; at a listed firm the same title demands a certified-accountant licence, and the week goes to the reports that regulators and the board read. Same title, different bar, different work. What defines an occupation is who it lets in and what it asks them to do. In a rapidly changing labor market, tracking those requirements and tasks is how to take the market's pulse. Yet no instrument reads both at the speed they change. Official occupational directories like O*NET report one national average per occupation, updated every few years. Job postings are timely but unstructured. Research built on them works from job titles plus proprietary skill keywords, which blur what is asked of a candidate into what a candidate is asked to do. The blur matters, because rising requirements and changing tasks are different events with different causes. We separate them. From 752.6 million job ads posted on China's five leading recruitment platforms between 2022 and 2026, we extract the phrases employers write, unify those that name the same thing, and validate the mapping from text back to entry. By doing so we construct two catalogs, 20,721 requirements a candidate must meet and 44,479 tasks the hire will do. With the entries standardized, we annotate them further. Each task, for example, carries a score for how far a language model could absorb it. Matched back onto every ad, the catalogs read the market month by month. Two examples show what the layer beneath the job title buys. First, the occupational registry records one accountant where the ads record a staircase, the junior certificate at the bottom of the wage range and the intermediate one at the top. Second, counting occupations says the work most exposed to language models is disappearing, and counting tasks says far less of it is.
The increasing tax burden each year poses a significant challenge, particularly for people with limited education and job seekers who struggle to find suitable employment, as most job openings prioritize bachelor's degree holders with work experience, while even graduates often face difficulties since companies typically require prior experience as a primary qualification; as a result, many graduates choose to start their own businesses, which in turn require employees to support their operations. This condition highlights the need for an intelligent solution that connects job seekers—including graduates and those without work experience—with vacancies matching their qualifications. To address this issue, this study designs the Moilme application, which integrates AI-based job matching through Google's Talent Solution API and GPS technology to generate intelligent job recommendations based on job seekers' qualifications while also displaying nearby job locations, with the Black Box method applied to simplify the development process and improve system performance in delivering relevant recommendations. The results show that the designed application successfully provides intelligent job recommendations matching users' qualifications and displays nearby job locations, thereby helping accelerate the job search process for job seekers as well as recruitment for companies and SMEs. The novelty of this research lies in combining AI-based qualification matching with GPS-based location intelligence within a single application, offering smarter and more personalized job suggestions. It is concluded that Moilme has the potential to expand job opportunities for those in need of work while helping companies and SMEs find suitable employees quickly and efficiently.
Laura Monica, Noneng Marthiawati, Kevin Kurniawansyah et al.· International Journal of Com...· 0 citations
Many individuals view talking about pay as a taboo subject. Business students may lack the real-world experience needed to understand an appropriate starting salary given a candidate’s experience, responsibilities, and other compensable factors. Although resources that help students prepare for general on-the-job negotiations exist, these may fall short in effectively preparing students for the nuanced, data-driven, and context-specific salary research and evaluation process. Recent graduates may fail to recognize the importance of conducting robust preparation and evaluating salary data prior to compensation conversations. We share an in-class exercise for use with upper-division business undergraduates in compensation and benefits, career development, or other management courses where career preparation is discussed. We emphasize the importance of enhancing job search self-efficacy, offering a series of generative artificial intelligence (GenAI) prompts and reflection through which students gain firsthand experience in researching and evaluating a realistic salary for a specific job role.
Mariya Gavrilova Aguilar, Sarah Holtzen, Bahareh Javadizadeh et al.· Management Teaching Review· 0 citations
We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.
Aaron Chatterji, David Holtz, Neel Rakholia et al.· 0 citations
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.
Artificial intelligence automates execution more readily than evaluation: producing output is cheap, judging whether it is correct is not. Exposure measures rank tasks by whether AI can perform them, not by which function the human supplies. I score all $19{,}265$ O*NET task statements under fixed rubrics to build occupation-level execution and AI-capability shares. The execution share is reproducible across model coders and O*NET vintages and distinct from AI capability and routine-task intensity; it is a model-based measure, not human-validated ground truth, and adds only modest power beyond O*NET's evaluation activities. In a harmonized panel, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and equality of slopes cannot be rejected: the gradient is a secular trend rather than an AI-era event, largely between occupational families. The vintage-valid capability gradient steepens after 2022, a change that is dated but not causally attributable. The evidence establishes a measure and a chronology, not an AI-caused effect.