WEF Jobs of Tomorrow Generative AI 2023 · Page 4

Executive summary
As advances in generative artificial intelligence (AI) 
continue at an unprecedented pace, large language 
models (LLMs) are emerging as transformative tools 
with the potential to redefine the job landscape. The 
recent advancements in these tools, like GitHub’s 
Copilot, Midjourney and ChatGPT, are expected 
to cause significant shifts in global economies and 
labour markets. These particular technological 
advancements coincide with a period of considerable 
labour market upheaval from economic, geopolitical, 
green transition and technological forces. The 
World Economic Forum’s Future of Jobs Report 
2023 predicts that 23% 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.
This white paper provides a structured analysis of 
the potential direct, near-term impacts of LLMs on 
jobs. With 62% of total work time involving language-
based tasks,1 the widespread adoption of LLMs, 
such as ChatGPT, could significantly impact a broad 
spectrum of job roles.
To assess the impact of LLMs on jobs, this paper 
provides an analysis of over 19,000 individual 
tasks across 867 occupations, assessing the 
potential exposure of each task to LLM adoption, 
classifying them as tasks that have high potential 
for automation, high potential for augmentation, low 
potential for either or are unaffected (non-language 
tasks). The paper also provides an overview of new 
roles that are emerging due to the adoption of LLMs.
The longer-term impacts of these technologies 
in reshaping industries and business models are 
beyond the scope of this paper, but the structured 
approach proposed here can be applied to other 
areas of technological change and their impact on 
tasks and jobs.
The analysis reveals 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.
–
The jobs ranking highest for potential automation
are Credit Authorizers, Checkers and Clerks (81%
of work time could be automated), Management
Analysts (70%), Telemarketers (68%), Statistical
Assistants (61%), and Tellers (60%).
–
Jobs with the highest potential for task
augmentation emphasize mathematical
and scientific analysis, such as Insurance
Underwriters (100% of work time potentially
augmented), Bioengineers and Biomedical
Engineers (84%), Mathematicians (80%), and
Editors (72%).
–
Jobs with lower potential for automation or
augmentation are jobs that are expected to
remain largely unchanged, such as Educational, 
Guidance, and Career Counsellors and Advisers 
(84% of time spent on low exposure tasks), 
Clergy (84%), Paralegals and Legal Assistants 
(83%), and Home Health Aides (75%).
–
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.
–
An industry analysis is done by aggregating
potential exposure levels of jobs to the industry
level, noting that jobs may exist in more than
one industry. Results reveal that the industries
with the highest estimates of total potential
exposure (automation plus a