[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-263165-105":53,"doc-detail-263165-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","generative-ai-and-the-transformation-of-workforce-a-job-postings-driven-analysis","Generative-AI and the Transformation of Workforce - A Job Postings-Driven Analysis","","This paper investigates how generative artificial intelligence reshapes job requirements, skill compositions, and sector dynamics across global labor markets. It analyzes the evolving frequency and framing of AI-related competencies in job postings, assessing whether generative AI acts mainly as an augmentative or substitutive force. A multi-source corpus of 150,000+ English postings from 2018–2025 is processed using lexical extraction, topic modeling, and ARIMA forecasting, with an embedding-based Framing Index. Results show post-2021 growth in prompt engineering, fine-tuning, and model validation, alongside declines in routine tasks, with forecasts indicating continued hybrid human–AI employability.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/generative-ai-and-the-transformation-of-workforce-a-job-postings-driven-analysis/263165/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/generative-ai-and-the-transformation-of-workforce-a-job-postings-driven-analysis/263165.png","ImageObject",442,249,{"name":88,"@type":89},"Graffin","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-20","2026-09-14",true,{"@type":98,"interactionType":99,"userInteractionCount":9},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What does the paper analyze to measure generative-AI’s impact on jobs?","Question",{"text":108,"@type":109},"It analyzes how AI-related competencies appear in job postings over time, including their frequency and framing, and whether they suggest augmentation versus substitution at work.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What data and methods are used in the study?",{"text":113,"@type":109},"The study compiles a 150,000+ posting corpus from 12 open-access datasets and one public API, then applies lexical skill extraction, topic modeling, time-series forecasting (ARIMA), and an embedding-based Framing Index using cosine similarity.",{"name":115,"@type":106,"acceptedAnswer":116},"What key trends in skills does the research find after 2021?",{"text":117,"@type":109},"AI-related skill mentions increase sharply after 2021—especially prompt engineering, fine-tuning, and model validation—while routine tasks such as data entry and manual coding decline.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},263165,1789367394,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":73,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":140},3573418547284,"https://eur-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Generative-AI and the transformation of workforce. A job postings-driven analysis  \nDiana Maria POPA, Simona-Vasilica OPREA*, AdelaBÂRA  \nDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania  \n*Corresponding author. E-mail: [simona.oprea@csie.ase.ro](simona.oprea@csie.ase.ro)  \nAbstract This paper investigates how generative-artificial intelligence (AI) is reshaping job requirements, skill compositions and sectoral dynamics across global labor markets. It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive force in the workplace. A large-scale, multi-source corpus of over 150,000 English-language job postings (2018-2025) is compiled from twelve open-access datasets and one public API. The analytical framework integrates lexical skill extraction, semantic framing, topic modeling (BERTopic, LDA, KMeans) and time-series forecasting (ARIMA) . Skill mentions are categorized into five dimensions: AI_Data, Routine, Soft_Meta, Domain_Specific and Leadership, while cross-sectoral analyses and correlation matrices quantify interdependencies between competencies. Sentence-transformer embeddings and cosine similarity are used to compute a Framing Index, distinguishing augmentation-versus automation-oriented discourse. Investigating job postings, our research contributes a replicable, data-driven methodology for mapping the diffusion of AI-related skills across industries and time. Results reveal a sharp post-2021 increase in AI-related skill mentions: “prompt engineering”,“fine-tuning” and “model validation”, accompanied by a decline in routine tasks: “data entry” and “manual coding”. Forecasts suggest sustained growth in AI_Data and Soft_Meta skills through 2025, signaling a structural convergence toward hybrid human-AI expertise asa new foundation of employability.  \nKeywords: generative-AI; labor market transformation; skill evolution; semantic framing; topic modeling; workforce adaptation  \n1. Introduction  \nThe rise of generative artificial intelligence (AI) has triggered deep and multifaceted transformationsin labor market structures and the skillsets required across occupations. Unlike traditional automation, which primarily targeted repetitive physical or computational tasks, generative-AI systems such as ChatGPT, Copilot and Claude can produce text, images, code, abstraction and analytical insights with human-like fluency and machine-like rigor [1], [2] . This capability fundamentally redefines the nature of work, creativity and professional identity across some industries. Consequently, job requirements are undergoing rapid transformation, demanding new technical and cognitive competencies. Traditional job structures, once centered around the execution of defined tasks, are evolving into collaborative ecosystems where humans and AI systems co-create value. This transformation affects the skills required for specific professions and also the broader dynamics of organizational culture, education and regulations [3] . Thus, understanding how generative-AI influences job requirements and what forms of workforce adaptation are necessary has become an important question for researchers, policymakers and business leaders alike [4] . The changing nature of job requirements can be observed in several interrelated dimensions [5], [6] .  \nFirst, AI literacy and human-AI collaboration are emerging as core competencies across all sectors. Workers are expected to know how to collaborate with generative models rather than merely operate them. Skills such as prompt engineering, model interpretation, fine-tuning and evaluation of AI outputs are increasingly valued. Second, creative and analytical roles are being redefined; professionals in fields like journalism, marketing and software development are shifting from producing content to curating, supervising and stra","cbCaitnipU6e1UQo","https://ap.wps.com/l/cbCaitnipU6e1UQo","pdf",1596459,23,"English","# Introduction\n## Skill transformation and human-AI collaboration\n## Educational misalignment and adaptation deficit\n## Workforce adaptation and reskilling needs","[{\"question\":\"What does the paper analyze to measure generative-AI’s impact on jobs?\",\"answer\":\"It analyzes how AI-related competencies appear in job postings over time, including their frequency and framing, and whether they suggest augmentation versus substitution at work.\"},{\"question\":\"What data and methods are used in the study?\",\"answer\":\"The study compiles a 150,000+ posting corpus from 12 open-access datasets and one public API, then applies lexical skill extraction, topic modeling, time-series forecasting (ARIMA), and an embedding-based Framing Index using cosine similarity.\"},{\"question\":\"What key trends in skills does the research find after 2021?\",\"answer\":\"AI-related skill mentions increase sharply after 2021—especially prompt engineering, fine-tuning, and model validation—while routine tasks such as data entry and manual coding decline.\"}]","Generative-AI and the Transformation of Workforce - A Job Postings-Driven Analysis | PDF",8]