INTEGRATION OF GENERATIVE AI SKILLS IN JOB REQUIREMENTS: EVIDENCE FROM JOB ADVERTISEMENTS IN GEORGIA

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published: Nov 2, 2026

  Natia Khukhunaishvili

  Tsotne Zhghenti

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) tools has created a demand for new skills in labour markets. However, research into how employers articulate GenAI competencies in job advertisements has focused almost exclusively on large, technologically advanced economies. This study examines how employers in Georgia, a small upper-middle-income transitional economy, indicate GenAI skill requirements in job advertisements, considering four dimensions: skill visibility, linguistic framing, organisational readiness and skill depth. Deductive content analysis was applied to 94 job advertisements collected in January 2026 from three major Georgian labour market platforms (LinkedIn, Jobs.ge and HR.ge). Each advertisement was coded using a structured 20-indicator codebook derived from established theoretical frameworks. The results show that employers articulate GenAI skills in two ways: by explicitly naming the tool, which is present in 38.3 per cent of advertisements, and by using implicit task language, which is present in 61.7 per cent of advertisements and constitutes the dominant form of signalling. Regardless of occupation or skill depth, employers frame GenAI requirements as mandatory in 81.9 per cent of advertisements, indicating the rapid institutionalisation of GenAI literacy as a baseline labour market expectation. Signals of organisational readiness remain weak: only 21.3 per cent of job advertisements acknowledge any organisational role in enabling AI adoption, while the remaining 78.7 per cent present GenAI competence purely as a prerequisite for candidates. Skill complexity is almost evenly divided between basic and advanced requirements, and occupation strongly predicts this division, creating a clear distinction between a technically advanced group and a large group adopting AI on a more general operational level. Signals of organisational readiness remain weak: only 21.3 per cent of job advertisements acknowledge any organisational role in enabling AI adoption, while the remaining 78.7 per cent present GenAI competence purely as a prerequisite for candidates. Skill complexity is almost evenly divided between basic and advanced requirements, and occupation strongly predicts this division, creating a clear distinction between a technically advanced group and a large group adopting AI on a more general operational level.

How to Cite

Khukhunaishvili, N., & Zhghenti, T. (2026). INTEGRATION OF GENERATIVE AI SKILLS IN JOB REQUIREMENTS: EVIDENCE FROM JOB ADVERTISEMENTS IN GEORGIA. Baltic Journal of Economic Studies, 12(5), 47-57. https://doi.org/10.30525/2256-0742/2026-12-5-47-57
Article views: 0 | PDF Downloads: 0

##plugins.themes.bootstrap3.article.details##

Keywords

generative AI, job advertisements, labour market, skill demand, organisational readiness, content analysis, Georgia

References

Acemoglu, D. (2024). The simple macroeconomics of AI. Economic Policy, 40(121), 1–53. https://doi.org/10.1093/epolic/eiae042

Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1), S293–S340.

Albanesi, S., Dias da Silva, A., Jimeno, J. F., Lamo, A., & Wabitsch, A. (2025). New technologies and jobs in Europe. Economic Policy, 40(121), 71–139. https://doi.org/10.1093/epolic/eiae058

Alekseeva, L., Azar, J., Giné, M., Samila, S., & Taska, B. (2021). The demand for AI skills in the labor market. Labour Economics, 71, 102002. https://doi.org/10.1016/j.labeco.2021.102002

Armenakis, A. A., & Harris, S. G. (2009). Reflections: Our journey in organizational change research and practice. Journal of Change Management, 9(2), 127–142. https://doi.org/10.1080/14697010902879079

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801

Babina, T., Fedyk, A., He, A. X., & Hodson, J. (2024). Artificial intelligence, firm growth and product innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745

Bansal, G., Vaughan, J. W., Kamar, E., Horowitz, B., & Weld, D. S. (2019). Beyond accuracy: The role of mental models in human-AI interaction. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7, 2–11.

Benford, R. D., & Snow, D. A. (2000). Framing processes and social movements: An overview and assessment. Annual Review of Sociology, 26, 611–639. https://doi.org/10.1146/annurev.soc.26.1.611

Berger, P. G., Cai, W., Qiu, L., & Shen, C. X. (2024). Employer and employee responses to generative AI: Early evidence. SSRN. https://doi.org/10.2139/ssrn.4874061

Bluemelhuber, B., & Langer, B. (2026). Generative AI competencies in demand: A job market analysis of knowledge, skills and abilities. Proceedings of the 59th Hawaii International Conference on System Sciences. University of Hawaiʻi at Mānoa. https://doi.org/10.24251/HICSS.2026.025

Bonin, H., Gregory, T., & Zierahn, U. (2015). Übertragung der Studie von Frey/Osborne auf Deutschland [Transfer of the Frey/Osborne study to Germany]. ZEW.

Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics and algorithms (STARA): Employees' perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55

Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044

Brynjolfsson, E., & Mitchell, T. (2017). What can machines learn and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43–47. https://doi.org/10.1257/pandp.20181019

Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A. J., Pizzinelli, C., Rockall, E., & Tavares, M. M. (2024). Gen-AI: Artificial intelligence and the future of work (IMF Staff Discussion Notes No. 2024/001). International Monetary Fund. https://doi.org/10.5089/9798400262548.006

Chen, Q., Ge, J., Xie, H., Xu, X., & Yang, Y. (2025). Large language models at work in China's labor market. China Economic Review, 92, 102413. https://doi.org/10.1016/j.chieco.2025.102413

Chen, W. X., Srinivasan, S., & Zakerinia, S. (2025). Displacement or complementarity? The labor market impact of generative AI (Harvard Business School Working Paper No. 25-039).

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419

Dell'Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838

Deming, D. J., & Kahn, L. B. (2018). Skill requirements across firms and labor markets: Evidence from job postings for professionals. Journal of Labor Economics, 36(S1), S337–S369. https://doi.org/10.1086/697913

Deming, D. J., & Noray, K. (2020). Earnings dynamics, changing job skills and STEM careers. Quarterly Journal of Economics, 135(4), 1965–2005. https://doi.org/10.1093/qje/qjaa021

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308. https://doi.org/10.1126/science.adj0998

Fairclough, N. (2003). Analysing discourse: Textual analysis for social research. Routledge.

Felten, E. W., Raj, M., & Seamans, R. (2023). Occupational heterogeneity in exposure to generative AI. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.4414065

Gmyrek, P., Berg, J., & Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality (ILO Working Paper No. 96). International Labour Organization.

Hershbein, B., & Kahn, L. B. (2018). Do recessions accelerate routine-biased technological change? American Economic Review, 108(7), 1737–1772. https://doi.org/10.1257/aer.20161570

Holt, D. T., Armenakis, A. A., Feild, H. S., & Harris, S. G. (2007). Readiness for organizational change. Journal of Applied Behavioral Science, 43(2), 232–255. https://doi.org/10.1177/0021886306295295

Hui, X., Reshef, O., & Zhou, L. (2024). The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market. Organization Science, 35(6), 1977–1989. https://doi.org/10.1287/orsc.2023.18441

Ji, Y., Chen, L., Wang, L., Hou, J., Chen, X., & Zhu, H. (2023). Generative AI's labor-replacing impacts on occupations also foster short-run job opportunities for early adopters. SSRN. https://doi.org/10.2139/ssrn.4862800

Khukhunaishvili, N., & Zhghenti, T. (2026). Mapping the skill architecture of generative AI occupations: Evidence from global job postings. Humanities and Social Sciences, 33(1), 37–50. https://doi.org/10.7862/rz.2026.hss.03

Lukauskas, M., Sarkauskaite, V., Pilinkiene, V., Stundziene, A., Grybauskas, A., & Bruneckiene, J. (2023). Enhancing skills demand understanding through job ad segmentation using NLP and clustering techniques. Applied Sciences, 13(10), 6119. https://doi.org/10.3390/app13106119

Marr, B. (2024). Generative AI in practice: 100+ amazing ways generative artificial intelligence is changing business and society. John Wiley & Sons.

Nakash, M., & Peretz, O. (2025). Decoding the AI job market: Mapping skills and classifying careers through online job ads. Employee Relations: The International Journal. https://doi.org/10.1108/ER-07-2025-0566

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

Salari, N., Beiromvand, M., Hosseinian-Far, A., Habibi, J., Babajani, F., & Mohammadi, M. (2025). Impacts of generative artificial intelligence on the future of labor market: A systematic review. Computers in Human Behavior Reports, 18, 100652. https://doi.org/10.1016/j.chbr.2025.100652

Smith, A., van Wagoner, H. P., Keplinger, K., & Celebi, C. (2025). Navigating AI convergence in human–artificial intelligence teams: A signaling theory approach. Journal of Organizational Behavior. https://doi.org/10.1002/job.2856

Spence, M. (1973). Job market signaling. In Uncertainty in economics (pp. 281–306). Academic Press.

Tona, O., Leidner, D. E., van der Meulen, N., Wixom, B., Nunes, J., & Shagam, D. (2025). The deployment of AI to infer employee skills: Insights from Johnson & Johnson's digital-first workforce initiative. Information Systems Journal, 35(6), 1516–1527. https://doi.org/10.1111/isj.12594

Zarifhonarvar, A. (2023). Economics of ChatGPT: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics, 3(2), 100–116. https://doi.org/10.1108/JEBDE-10-2023-0021