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This white paper defines and addresses key concerns on the impact of large language models (LLMs) on jobs and emphasizes the need for businesses to adopt proactive and responsible strategies to manage these changes. Generative artificial intelligence (AI), particularly LLMs, have taken a leap in capabilities and have reached near-human-level performance in a variety of areas. Up to 40 per cent of working hours could be transformed by LLMs through the automation and augmentation of worker tasks.

The white paper offers a toolkit for business leaders, detailing how they can adapt, reskill and harness the potential of their workforce in the face of this technological evolution. It also serves as a call to action for business leaders to create strategies and practices that use LLMs for the benefit of businesses, employees and society at large, ensuring an improved future of work with new opportunities for workers.

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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. This white paper provides a structured analysis of the potential direct, near-term impacts of LLMs on jobs. With 62 per cent of total work time involving language-based tasks, 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 per cent of work time could be automated), Management Analysts (70 per cent), Telemarketers (68 per cent), Statistical Assistants (61 per cent), and Tellers (60 per cent). Jobs with the highest potential for task augmentation emphasize mathematical and scientific analysis, such as Insurance Underwriters (100 per cent of work time potentially augmented), Bioengineers and Biomedical Engineers (84 per cent), Mathematicians (80 per cent), and Editors (72 per cent). 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 per cent of time spent on low exposure tasks), Clergy (84 per cent), Paralegals and Legal Assistants (83 per cent), and Home Health Aides (75 per cent). 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.

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