IGGQ Research Publishing
Markets, Organizations & Public Value

Generative AI and Task Redesign: Evidence for Managers and Policymakers

Read & download PDF
Abstract

This evidence review examines generative artificial intelligence and task redesign. The organizing question is which tasks are augmented, displaced, recombined, or newly created when generative tools enter organizations. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to generalizing short-run experimental productivity gains to whole occupations or long-run welfare. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in organizational adoption and labor-market policy.

Keywords
generative AIwork designproductivitylabor marketsmanagement
References
  1. Cramarenco, R. E., Burcă-Voicu, M. I., & Dabija, D. C. (2023). The impact of artificial intelligence (AI) on employees’ skills and well-being in global labor markets: A systematic review. Oeconomia Copernicana, 14(3), 731-767. https://doi.org/10.24136/oc.2023.022 DOI
  2. 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 DOI
  3. Hartley, J., Jolevski, F., Melo, V., & Moore, B. (2025). The Labor Market Effects of Generative Artificial Intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5136877 DOI
  4. Hui, X., Reshef, O., & Zhou, L. (2023). The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4527336 DOI
  5. Humphreys, D., Koay, A., Desmond, D., & Mealy, E. (2024). AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business. AI and Ethics, 4(3), 791-804. https://doi.org/10.1007/s43681-024-00443-4 DOI
  6. Kanbach, D. K., Heiduk, L., Blueher, G., Schreiter, M., & Lahmann, A. (2023). The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective. Review of Managerial Science, 18(4), 1189-1220. https://doi.org/10.1007/s11846-023-00696-z DOI
  7. Lazaroiu, G., & Rogalska, E. B. (2023). How generative artificial intelligence technologies shape partial job displacement and labor productivity growth. Oeconomia Copernicana, 14(3), 703-706. https://doi.org/10.24136/oc.2023.020 DOI
  8. Naqbi, H. A., Bahroun, Z., & Ahmed, V. (2024). Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166 DOI
  9. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2302.06590 DOI
  10. 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 DOI
Publication details
Journal
Markets, Organizations & Public Value
Volume
1 (2026)
Article number
mv20260002
License
CC BY 4.0