Your browser does not support the PubReader view.
Go to this page to see a list of supporting browsers.
The Korean Journal of Public Administration
[ Article ]
Korean Journal of Public Administration - Vol. 63, No. 2, pp.149-196
ISSN: 1229-6694 (Print)
Print publication date 30 Jun 2025
Received 21 Apr 2025 Revised 01 Jun 2025 Accepted 11 Jun 2025
DOI: https://doi.org/10.24145/KJPA.63.2.5

인공지능 파도 넘기: 커리어래더(Career Ladder) 어프로치

김규환** ; 최영빈*** ; 구교준****
**제1저자, 서울지역 인적자원개발위원회 책임연구원 khkim01@innobiz.or.kr
***공동저자, 한국농촌경제연구원 농촌정책연구실 연구원 cyb6702@krei.re.kr
****교신서자, 고려대학교 행정학과 교수 jkoo@korea.ac.kr
Riding the Wave of Artificial Intelligence: A Career Ladder Approach
Gyuhwan Kim** ; Yeongbin Choi*** ; Jun Koo****

초록

본 연구는 커리어래더 접근법을 활용하여 인공지능에 의하여 대체가능성이 높은 직업을 확인하고, 이들 직업이 대체가능성이 낮은 직업으로 어떻게 이･전직할 수 있는지를 분석하였다. 분석에 사용된 자료는 ONET과 한국표준직업분류를 매칭하여 구축한 데이터로 직업별 기술과 역량의 중요도 정보를 포함한다. 해당 자료를 기반으로 직업 간 유사도는 유클리디언 거리를 활용하여 측정하였으며, 대체가능성이 높은 직업의 유형 구분은 잠재프로파일분석을 사용하였다. 분석 결과, 대체가능성이 높은 직업을 총 6가지 유형으로 분류하였으며, 직업들은 공통적으로 복잡성과 전문성이 상대적으로 낮은 특징을 보이는 것으로 나타났다. 한편, 이들 직업은 다양한 대체가능성이 낮은 직업으로 이･전직이 가능한데, 대표적으로 자동차 정비원, 채굴, 토목, 화학 관련 직업 등이 있는데, 이러한 대체가능성이 낮은 직업들은 기술적 숙련도뿐 아니라 상황적응 능력, 인지적 역량, 신체적 숙련의 균형을 강하게 요구하는 것으로 나타났다. 따라서 향후 직업훈련과정에서는 장비 선택, 수리, 조작과 관련된 인지적 스킬 및 신체적 균형을 중심으로 한 훈련이 요구된다고 할 수 있다.

Abstract

This study employs the career ladder approach to identify occupations with a high susceptibility to being replaced by artificial intelligence and to analyze potential transitions from these occupations to those with a lower susceptibility. The analysis is based on a dataset constructed by matching ONET with the Korean Standard Classification of Occupations, which includes information on the importance of various skills and competencies for each occupation. Occupational similarity was measured using Euclidean distance, while latent profile analysis was employed to classify highly replaceable occupations. The results revealed six distinct profiles of highly-replaceable occupations, which commonly exhibited relatively low levels of complexity and specialization. Meanwhile, transitions to low-replaceability occupations were found to be feasible, particularly into automotive maintenance, mining, civil engineering, and chemical-related positions. These low-replaceable occupations were characterized by a strong demand for a balanced combination of technical proficiency, situational adaptability, cognitive abilities, and physical skills. Accordingly, future vocational training programs should emphasize cognitive and physical skills related to equipment selection, repair, and operation.

Keywords:

career ladder, artificial intelligence, ai job replacement index, occupational transition, job, labor market

키워드:

커리어래더, 인공지능, 일자리 대체가능지수, 일자리 이동, 일자리, 노동시장

Acknowledgments

본 연구는 고려대학교 특별연구비(K2406861)의 지원을 받아 수행되었음.

References

  • 구교준･김지원･최영빈･김규환･홍지은. (2024). 인공지능과 로봇기술의 노동시장 파급효과 분석을 위한 일자리 대체가능성 지수의 개발. ｢행정논총｣, 62(3): 37–74.
  • 김건우. (2018). 인공지능에 의한 일자리 위험 진단. 서울: LG경제연구원.
  • 김동규･양인준･박세정･조아름･최기성. (2024). 인공지능에 의한 화이트칼라의 직무 대체 및 변화. 서울: 연구사업보고서.
  • 김세움･고선･조영준. (2014). 기술진보의 노동시장에 대한 동태적 영향. 서울: 한국노동연구원.
  • 김정훈･김을식･이다겸. (2015). 경기도 비경제활동인구의 노동시장 유인 정책방안. 서울: 정책연구.
  • 박광옥･박성희･김연희･최정희. (2014). 임상간호사 경력개발 프로그램 평가. ｢간호학의 지평｣, 11(2): 123–131.
  • 박성희･박광옥･박성애. (2006). 간호사 임상 경력 관리 프로그램 개발. ｢간호행정학회지｣, 12(4): 624–632.
  • 방준식. (2023). 인공지능(AI)과 일자리: 직업능력개발을 중심으로. ｢노동법포럼｣, (39): 313–337.
  • 송성수. (2022). 인공지능은 인간의 일자리를 얼마나 대체할 것인가: 인공지능 시대의 기술과 노동에 관한 시론. ｢코기토｣, (96): 7–37.
  • 엄효진･이명진. (2020). 인공지능(AI) 기반 지능정보사회 시대의 노동시장 변화: 경제사회학적 접근을 중심으로. ｢정보사회와 미디어｣, 21(2): 1–20.
  • 이대창. (2021). 한국의 경기변동과 일자리 상향이동. ｢Korean Journal of Labor Economics｣, 44(4).
  • 한요셉. (2023). 인공지능으로 인한 노동시장의 변화와 정책방향. 서울: KDI 연구보고서.
  • 한지우･오삼일. (2023). AI와 노동시장 변화. 서울: BOK 이슈노트, 30.
  • Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52: 317–332. [https://doi.org/10.1007/BF02294359]
  • Autor, D., Chin, C., Salomons, A., & Seegmiller, B. (2024). New frontiers: The origins and content of new work, 1940–2018. The Quarterly Journal of Economics, 139(3): 1399–1465. [https://doi.org/10.1093/qje/qjae008]
  • Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5): 1553–1597. [https://doi.org/10.1257/aer.103.5.1553]
  • Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4): 1279–1333. [https://doi.org/10.1162/003355303322552801]
  • Burrus, J., Jackson, T., Xi, N., & Steinberg, J. (2013). Identifying the most important 21st century workforce competencies: An analysis of the Occupational Information Network (ONET). ETS Research Report Series, 2013(2): i–55. [https://doi.org/10.1002/j.2333-8504.2013.tb02328.x]
  • Chen, N., Li, Z., & Tang, B. (2022). Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations. PLOS ONE, 17(11): e0277280. [https://doi.org/10.1371/journal.pone.0277280]
  • Del Rio-Chanona, R. M., Mealy, P., Beguerisse-Díaz, M., Lafond, F., & Farmer, J. D. (2021). Occupational mobility and automation: A data-driven network model. Journal of the Royal Society Interface, 18(174): 20200898. [https://doi.org/10.1098/rsif.2020.0898]
  • Felten, E. W., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12): 2195–2217. [https://doi.org/10.1002/smj.3286]
  • Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerisation? University of Oxford Working Paper.
  • Goldman Sachs. (2023). The potentially large effects of artificial intelligence on economic growth. Goldman Sachs.
  • Gruetzemacher, R., Paradice, D., & Lee, K. B. (2020). Forecasting extreme labor displacement: A survey of AI practitioners. Technological Forecasting and Social Change, 161: 120323. [https://doi.org/10.1016/j.techfore.2020.120323]
  • Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2): 155–172. [https://doi.org/10.1177/1094670517752459]
  • Idrisi, M. J., Geteye, D., & Shanmugasundaram, P. (2024). Modeling the complex interplay: Dynamics of job displacement and evolution of artificial intelligence in a socio-economic landscape. International Journal of Networked and Distributed Computing, 1–10. [https://doi.org/10.1007/s44227-024-00025-0]
  • IMF. (2024). Gen AI: Artificial intelligence and the future of work (IMF Staff Discussion Note No. 2024/01). International Monetary Fund.
  • Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065): 20150202. [https://doi.org/10.1098/rsta.2015.0202]
  • Lassébie, J., & Quintini, G. (2022). What skills and abilities can automation technologies replicate and what does it mean for workers? New evidence. OECD Social, Employment and Migration Working Papers, (282): 1–66.
  • Magidson, J., & Vermunt, J. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20(1): 36–43.
  • Mäkelä, E., & Stephany, F. (2024). Complement or substitute? How AI increases the demand for human skills. arXiv preprint, arXiv:2412.19754, .
  • Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey & Company.
  • Masriadi, D., Ekaningrum, N. E., Hidayat, M. S., & Yuliaty, F. (2023). Exploring the future of work: Impact of automation and artificial intelligence on employment. Endless International Journal of Future Studies, 6(1): 125–136. [https://doi.org/10.54783/endlessjournal.v6i1.131]
  • McLachlan, G. J. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(3): 318–324. [https://doi.org/10.2307/2347790]
  • Morgan, P., De Lappe, J., & Summers, N. (2024). A mixed-methods exploration of the nursing associate role, programme experiences and future career pathways. British Journal of Nursing, 33(11): 488–495. [https://doi.org/10.12968/bjon.2023.0246]
  • Moscarini, G., & Postel-Vinay, F. (2016). Did the job ladder fail after the Great Recession? Journal of Labor Economics, 34(S1): S55–S93. [https://doi.org/10.1086/682366]
  • Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4): 440. [https://doi.org/10.1037/tps0000176]
  • Oschinski, M., Crawford, A., & Wu, M. (2024). AI and the future of workforce training. The Conference Board of Canada.
  • Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1): 103–124. [https://doi.org/10.1287/mksc.12.1.103]
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2): 461–464. [https://doi.org/10.1214/aos/1176344136]
  • Sireci, S. G., Longe, B., Suárez-Álvarez, J., & Oliveri, M. E. (2024). Analyzing the dimensionality of ONET cognitive ability ratings to inform assessment design. Education Sciences, 14(11): 1202. [https://doi.org/10.3390/educsci14111202]
  • Sullivan, S. E., & Al Ariss, A. (2021). Making sense of different perspectives on career transitions: A review and agenda for future research. Human Resource Management Review, 31(1): 100727. [https://doi.org/10.1016/j.hrmr.2019.100727]
  • Wang, Q., Liu, X., & Huang, K. (2019, December). Displaced or augmented? How does artificial intelligence affect our jobs: Evidence from LinkedIn. In Proceedings of the International Conference on Information Systems (ICIS).
  • Webb, M. (2019). The impact of artificial intelligence on the labor market. SSRN. [https://doi.org/10.2139/ssrn.3482150]
  • World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum.
  • Yu, J. C., Sokolowski, H. M., Rao, K. S., Moraglia, L. E., Khoubrouy, S. A., Abdi, H., & Levine, B. (2024). Visualization of latent components assessed in O*NET occupations (VOLCANO): A robust method for standardized conversion of occupational labels to ratio scale format. Behavior Research Methods, 56(1): 417–432. [https://doi.org/10.3758/s13428-022-02044-7]