Artificial Intelligence, Productivity, and Job Quality

This evidence review examines artificial intelligence, productivity, and job quality. The organizing question is which task and organizational changes generate productivity while preserving autonomy, learning, fairness, and worker well-being. 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-term task gains to firm productivity or social welfare. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in workplace technology strategy and labour policy.

1. Acemoglu, D. M., & Restrepo, P. M. (2018). Artificial Intelligence, Automation and Work. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3098384 DOI
2. Aly, H. (2020). Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?. Seikei ronsō/Review of economics and political science, 7(4), 238-256. https://doi.org/10.1108/reps-11-2019-0145 DOI
3. Bhargava, A., Bester, M., & Bolton, L. (2020). Employees’ Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation (RAIA) on Job Satisfaction, Job Security, and Employability. Journal of Technology in Behavioral Science, 6(1), 106-113. https://doi.org/10.1007/s41347-020-00153-8 DOI
4. Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior & Organization, 211, 188-205. https://doi.org/10.1016/j.jebo.2023.05.008 DOI
5. Damioli, G., Roy, V. V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian economic review :, 11(1), 1-25. https://doi.org/10.1007/s40821-020-00172-8 DOI
6. Gao, X., & Feng, H. (2023). AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity. Sustainability, 15(11), 8934. https://doi.org/10.3390/su15118934 DOI
7. 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
8. 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 DOI
9. Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116, 68-74. https://doi.org/10.1016/j.jbusres.2020.05.019 DOI
10. Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631. https://doi.org/10.1002/smj.3322 DOI