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Generative AI to Enhance Creativity, Automate Routine Tasks for Future Jobs

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  • Large language models (LLMs) have the most potential to benefit jobs that involve critical thinking and complex problem-solving skills, according to a new World Economic Forum white paper.
  • Businesses and governments must take proactive steps to prepare for the effects of AI in the workforce, including creating an adaptable workforce and implementing systems that facilitate job transitions.
  • Jobs most at risk of disruption are those that involve routine and repetitive language tasks; jobs with a high degree of personal interaction or physical movement will be least affected.
  • For more information, read the white paper here. Learn more about the Sustainable Development Impact meetings here and share on social media using the hashtag #SDIM23.

How will large language models (LLMs), deep-learning algorithms that can recognize, summarize, translate, predict and generate content using very large datasets, affect current and future jobs? That is the focus of the new World Economic Forum white paper Jobs of Tomorrow: Large Language Models and Jobs.


The white paper finds that LLMs could be a boon for jobs that require critical thinking, complex problem-solving skills and creativity, including those in engineering, mathematics and scientific analysis. These tools could benefit workers by increasing the productivity of routine tasks and making their roles more rewarding and focused on a higher added value. This paper takes a structured approach to understanding the direct impact of LLMs on specific jobs.


“Generative AI is poised to impact labour markets significantly, but this impact will be highly different across different roles,” said Saadia Zahidi, Managing Director, World Economic Forum. “Business leaders, policy-makers and employees must collaborate on harnessing the potential of new jobs while managing displacement and ensuring a future of work that empowers and elevates people.”


According to the analysis, which examined more than 19,000 distinct tasks across 867 different occupations likely to be impacted by LLMs, the industries with the highest estimates of overall potential exposure – both in automation and augmentation – are financial services and capital markets, along with insurance and pension management.


As LLMs advance, new roles will also be created, including AI developers, interface and interaction designers, AI content creators, data curators and specialists in AI ethics and governance.


The jobs most at risk of automation – with up to four-fifths of the tasks automated – are those that involve routine and repetitive language tasks, including roles such as credit authorizers, checkers and clerks. The occupations projected to remain relatively unaltered include education, guidance, career counsellors and advisers, with 84% of their tasks having a low exposure to change.


This new data on the impact of LLMs supplements and reinforces the findings from the Forum’s Future of Jobs Report 2023, which found that up to a quarter of jobs are expected to change in the next five years from the combined impact of technology, the green transition and the geoeconomic outlook.


The paper argues that businesses and governments must take proactive steps to prepare for the effects of LLMs in the workforce, including by improving foresight, creating an adaptable workforce, implementing systems that facilitate job transitions and encouraging lifelong learning.


About the Sustainable Development Impact Meetings 2023


The Sustainable Development Impact Meetings 2023 are taking place on 18-22 September in New York. The meetings will reflect on progress made on the United Nations Sustainable Development Goals and revive momentum for multistakeholder action. More than 600 business leaders, policy-makers, leaders from international and civil society organizations, innovators and social entrepreneurs will come together in person to advance concrete progress on the SDGs.


Notes to editors

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Check out the Forum’s Strategic Intelligence Platform and Transformation Maps

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