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AI-exposure and labour market: a systematic literature review on estimations, validations, and perceptions

Author

Listed:
  • Dona Ghosh

    (Thiagarajar School of Management
    Taylor’s University)

  • Rajarshi Ghosh

    (Indian Chamber of Commerce and FPM Scholar, IMI-Kolkata)

  • Sahana Roy Chowdhury

    (International Management Institute)

  • Boudhayan Ganguly

    (International Management Institute)

Abstract

The artificial intelligence (AI) revolution is still in its infancy, but it is constantly changing across time and space. Any AI-penetration, adoption and diffusion trigger further rounds of supply-induced demand for newer AI-exposures. Understandably, the demand and supply are intertwined, and currently, it is a formidable challenge to comprehensively capture and delineate the extent of AI's influence, as evidenced in existing literature. The paper details out the fundamental inferences drawn by the recent literature on this, thoroughly and systematically. The findings are based on peer-reviewed articles from the domains of economics, HR, business and management literature spanning 2010–2023. Although growing AI exposure is widely acknowledged in the literature, clarity and comprehensiveness are missing on several grounds. This review analyses the adoption of technology based on AI, whether in a broad or narrow sense, as well as the skill sets required and its impact on the labour market as documented in extant literature, encompassing employment, occupations, earnings, and organisation. Three overarching concerns are identified: First, to assess and infer on possible labour market impacts due to the AI-exposure of firms the proxies of AI exposure taken by the literature are quite diverse: robotization, digital evolution index, human–machine collaboration, technological progress etc. Second, the methodologies employed in qualitative and quantitative literature exhibit a wide range of diversity; these techniques often yielded contradictory and divergent conclusions, potentially influenced by the size and structure of the data utilised in the respective methods. Third, research is limited to China or regions of developed countries, making it difficult to draw general conclusions. The literature at present, is dispersed, and is yet to build any consensus on deciphering the skill-types that possibly would see the largest impacts, the sectors that might see plethora of AI-enabled economic repercussions, whether indeed there will be an overall net- job destruction or net-job creation, and does that differ across economic regions? The paper details out the fundamental inferences drawn by the recent literature on this, thoroughly and systematically.

Suggested Citation

  • Dona Ghosh & Rajarshi Ghosh & Sahana Roy Chowdhury & Boudhayan Ganguly, 2025. "AI-exposure and labour market: a systematic literature review on estimations, validations, and perceptions," Management Review Quarterly, Springer, vol. 75(1), pages 677-704, February.
    • Handle: RePEc:spr:manrev:v:75:y:2025:i:1:d:10.1007_s11301-023-00393-x
    DOI: 10.1007/s11301-023-00393-x
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    Keywords

    Artificial intelligence; Automation; Employment; Job creation; Job destruction;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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