From Task Automation to Job Transformation: How Artificial Intelligence is Reshaping Productivity, Skills, Wages, and Job Security in Pakistan’s IT Labour Market

Abstract

Companies all over the world are transforming the way they work with artificial intelligence. Routine tasks are becoming automated, and decisions are being aided by machines, creating demand for new types of skills. The study explores the impact of AI on the future of work, specifically for information technology professionals in the workplace in Islamabad and Rawalpindi, Pakistan. Twenty IT professionals were interviewed, and their experiences and concerns were discussed. Work-related open-ended questions addressed five main topics: productivity, changes in jobs, roles, skills and training needs, pay and compensation, and job security. The encouraging part is that some things we found out are encouraging, and the other parts are, too. Most respondents feel that AI has improved their productivity at work, automates repetitive tasks such as coding, or improves the quality of their work output. Data processing frees people from more tedious and boring tasks to engage in more interesting and creative work. However, AI is transforming the nature of work. Programming, testing, and maintenance are regular activities; documentation is being automated. But individuals require new abilities: knowing how to use AI, interacting with data, crafting more effective AI prompts and queries, and keeping their skills up to date. In the economic domain, it is expected that AI will enhance the disparity between highly skilled and routine workers. Individuals with AI skills will likely be better compensated, whereas those who are not will likely be left behind. There could be pressure on wages for repetitive work. Importantly, workers do not expect a sudden wave of layoffs. However, they are concerned about less entry-level employment and growing job insecurity. The research suggests that AI will be more likely to change jobs than to destroy them. We recommend regular training initiatives, university-industry collaboration, ethical use of AI, and more to ensure the growth of Pakistan's IT industry.

References

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labour. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3

Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from U.S. labour markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

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 (NBER Working Paper No. 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161

Cui, K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2026). The effects of generative AI on high-skilled work: Evidence from three field experiments with software developers. Management Science. https://doi.org/10.1287/mnsc.2025.00535

Gambacorta, L., Qiu, H., Shan, S., & Rees, D. M. (2026). Generative AI and labour productivity: A quasi-experiment on coding. Journal of Financial Stability, 84, Article 101543. https://doi.org/10.1016/j.jfs.2026.101543

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689

Green, A. (2024). The changing nature of work in the age of AI. OECD Publishing.

International Labour Organization. (2024). World employment and social outlook: Trends 2024. International Labour Office.

International Labour Organization. (2025). World employment and social outlook: Trends 2025. International Labour Office.

International Labour Organization. (2026). World employment and social outlook: Trends 2026. International Labour Office.

Müller, S., & Schmitz, M. (2025). Task composition and skill demand in artificial intelligence-exposed occupations: Evidence from Germany. Research Policy, 54(4), Article 104122.

Organisation for Economic Co-operation and Development. (2024). OECD employment outlook 2024: The net-zero transition and the labour market. OECD Publishing. https://doi.org/10.1787/ac8b3538-en

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv. https://doi.org/10.48550/arXiv.2302.06590

Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101