ABU DHABI, UAE — Large language models (LLMs) are emerging as transformative tools with the potential to redefine the job landscape as advances in generative artificial intelligence (AI) continue at an unprecedented pace.
Recent advancements in these tools, such as GitHub’s Copilot, Midjourney, and ChatGPT, are expected to cause significant shifts in global economies and labor markets.
According to the World Economic Forum’s recent report titled “Future of Jobs Report 2023,” these technological advancements coincide with a period of significant labor market upheaval caused by economic, geopolitical, green transition, and technological forces.
This report’s findings illuminate how implementing LLMs like ChatGPT could alter the job landscape, providing valuable insights for policymakers, educators, and business leaders. Instead of causing job displacement, LLMs might usher in a period of task-based occupation transformation, requiring proactive strategies to prepare the workforce for these future jobs.
Division of labor
Jobs with high automation potential:
* Credit Authorizers, Checkers, and Clerks: 81%
* Management Analysts: 70%
* Telemarketers: 68%
* Statistical Assistants: 61%
* Tellers: 60%
Jobs with high augmentation potential:
* Insurance Underwriters: 100%
* Bioengineers and Biomedical Engineers: 84%
* Mathematicians: 80%
* Editors: 72%
Jobs with low potential for automation or augmentation:
* Educational, Guidance, and Career Counselors and Advisors: 84%
* Clergy: 84%
* Paralegals and Legal Assistants: 83%
* Home Health Aides: 75%
Industries with the highest potential exposure to LLMs:
* Financial services and capital markets
* Insurance and pension management
* Followed by information technology and digital communications, then media, entertainment, and sports.
Function group exposure to LLMs:
* Information technology: 73%
* Finance: 70%
The report predicts that 23 percent of global jobs will change in the next five years due to industry transformation, including through artificial intelligence and other text, image, and voice processing technologies.
The report revealed that with 62 percent of total work time involving language-based tasks, the widespread adoption of LLMs could significantly impact a broad spectrum of job roles.
The analysis also showed that tasks with the highest potential for automation by LLMs tend to be routine and repetitive, while those with the highest potential for augmentation require abstract reasoning and problem-solving skills. Tasks with lower potential for exposure require a high degree of personal interaction and collaboration.
According to the study, the jobs ranking highest for potential automation are Credit Authorizers, Checkers, and Clerks (81 percent of work time could be automated), Management Analysts (70 percent), Telemarketers (68 percent), Statistical Assistants (61 percent), and Tellers (60 percent).
The jobs with the highest potential for task augmentation emphasize mathematical and scientific analysis, such as Insurance Underwriters (100 percent of work time potentially augmented), Bioengineers and Biomedical Engineers (84 percent), Mathematicians (80 percent), and Editors (72 percent).
On the other hand, jobs with lower potential for automation or augmentation are jobs that are expected to remain largely unchanged, such as Educational, Guidance, and Career Counselors and Advisors (84 percent of time spent on low exposure tasks), Clergy (84 percent), Paralegals and Legal Assistants (83 percent), and Home Health Aides (75 percent).
In addition to reshaping existing jobs, the adoption of LLMs is likely to create new roles within the categories of AI Developers, Interface and Interaction Designers, AI Content Creators, Data Curators, and AI Ethics and Governance Specialists, the report said.
An industry analysis, on the other hand, is performed by aggregating potential exposure levels of jobs to the industry level, keeping in mind that jobs may exist in multiple industries.
Results reveal that the industries with the highest estimates of total potential exposure (automation plus augmentation measures) are both segments of financial services: financial services and capital markets, and insurance and pension management. This is followed by information technology and digital communications, and then media, entertainment, and sports.
Similarly, a function group analysis reveals that the two thematic areas with the greatest total potential exposure to LLMs are information technology, with 73 percent of working hours exposed, and finance, with 70 percent of working hours exposed.
These new findings connect directly to earlier work done by the Center for the New Economy and Society in the Future of Jobs Report 2023. Many of the jobs found to have high potential for automation by LLMs were also expected by business leaders to undergo employment decline within the next five years, such as bank tellers and related clerks, data entry clerks, and administrative and executive secretaries.
Meanwhile, jobs with high potential for augmentation are expected to grow, such as AI and Machine Learning Specialists, Data Analysts and Scientists, and Database and Network Professionals.