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Embracing artificial intelligence in the labour market: the case of statistics

Author

Listed:
  • Jin Liu

    (Beijing Institute of Technology)

  • Kaizhe Chen

    (Beijing Institute of Technology)

  • Wenjing Lyu

    (Zhejiang University
    MIT Computer Science & Artificial Intelligence Lab)

Abstract

In an era marked by rapid advancements in artificial intelligence (AI), the dynamics of the labour market are undergoing significant transformation. A common concern amidst these changes is the potential obsolescence of traditional disciplines due to AI-driven productivity enhancements. This study delves into the evolving role and resilience of these disciplines within the AI-influenced labour market. Focusing on statistics as a representative field, we investigate its integration with AI and its interplay with other disciplines. Analyzing 279.87 million online job postings in the United States from 2010 to 2022, we observed a remarkable 31-fold increase in the demand for AI-specialized statistical talent, diversifying into 932 distinct AI-related job roles. Additionally, our research identified four major interdisciplinary clusters, encompassing 190 disciplines with a statistical focus. The findings also highlight a growing emphasis on specific hard skills within these AI roles and the differences in demand for AI talent in statistics across economic sectors and regions. Contrary to the pessimistic view of traditional disciplines’ survival in the AI age, our study suggests a more optimistic outlook. We recommend that professionals and organizations proactively adapt to AI advancements. Governments and academic institutions should collaborate to foster interdisciplinary skill development and evaluation for AI talents, thereby enhancing the employability of individuals from traditional disciplines and contributing to broader economic growth.

Suggested Citation

  • Jin Liu & Kaizhe Chen & Wenjing Lyu, 2024. "Embracing artificial intelligence in the labour market: the case of statistics," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03557-6
    DOI: 10.1057/s41599-024-03557-6
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    References listed on IDEAS

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