Beyond Automation: How AI is Reshaping the Job Market and Redefining What it Means to be Career Ready

Authors

Maham Mughal

National University of Sciences & Technology (Pakistan)

Henna A. Qureshi

National University of Sciences & Technology (Pakistan)

Article Information

DOI: 10.47772/IJRISS.2025.910000722

Subject Category: Artificial Intelligence

Volume/Issue: 9/10 | Page No: 8782-8793

Publication Timeline

Submitted: 2025-11-06

Accepted: 2025-11-12

Published: 2025-11-22

Abstract

In an era defined by digital transformation, Artificial Intelligence (AI) is fundamentally reshaping the world of work and redefining the notion of career readiness. As industries integrate intelligent systems into their operations, higher education must evolve to equip students with the competencies required to navigate the AI-driven landscape. This qualitative study examines undergraduates’ perceptions of AI’s impact on employability, skill development, and future career preparation within the context of Pakistan’s higher education system. Using purposive and snowball sampling, twenty-one semi-structured interviews were conducted with final-year students across STEAM disciplines. The data were analyzed through Reflexive Thematic Analysis (RTA) to identify patterns in how learners interpret AI’s role in shaping future career pathways. Findings indicate that students perceive AI as both an opportunity and a challenge, enhancing efficiency, innovation, and access to knowledge, while also raising concerns about its ethical, technical, and adaptability implications. Participants emphasized the growing need for hybrid skill sets that blend technological fluency with human capabilities such as creativity, communication, and critical thinking. The study provides a framework, illustrating what is currently happening in the labour market and what needs to be done to learn to foster sustainable employability in an evolving job market.

Keywords

Artificial Intelligence, Career Readiness, Employability, Soft skills, AI-related skills, Thematic Analysis

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References

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

2. Akinnagbe, O. B. (2024). Human–AI collaboration: Enhancing productivity and decision-making. International Journal of Education Management and Technology, 2(3), 387–417. [Google Scholar] [Crossref]

3. https://doi.org/10.58578/ijemt.v2i3.4209 [Google Scholar] [Crossref]

4. Ali, M. D., Ali, M. F., & Mujahid, M. (2025). Impact of artificial intelligence on the job market and the future of work. International Journal of Engineering Works, 12(5), 73–88. https://doi.org/ 10. 34259/ijew.25.12057380 [Google Scholar] [Crossref]

5. Automation, inequality and the future of work. (2021, September 22). Pakistan Institute of Development Economics (PIDE). https://pide.org.pk/research/automation-inequality-and-the-future-of-work/ [Google Scholar] [Crossref]

6. Bankins, S., Jooss, S., Lloyd, S., Marrone, M., Ocampo, A. C., & Shoss, M. (2024). Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review. Journal of Vocational Behavior, 153, 104011. https://doi.org/10.1016/j.jvb.2024.104011 [Google Scholar] [Crossref]

7. Battisti, E., Alfiero, S., & Leonidou, E. (2022). Remote working and digital transformation during COVID-19: Economic–financial impacts and psychological drivers. Journal of Business Research, 150, 38–50. https://doi.org/10.1016/j.jbusres.2022.06.010 [Google Scholar] [Crossref]

8. Bernal, R. (2025). Education can help prepare learners for tomorrow’s demands. World Economic Forum. https://www.weforum.org/stories/2025/01/future-of-education-and-skills/ [Google Scholar] [Crossref]

9. Borenstein, J., & Howard, A. (2020). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1). https://doi.org/10.1007/s43681-020-00002-7 [Google Scholar] [Crossref]

10. Borg, J., Scott-Young, C. M., & Bartram, T. (2025). A review of graduate work readiness literature: A conceptual exploration of the implications for HRM research and practice. Asia Pacific Journal of Human Resources, 63(2). https://doi.org/10.1111/1744-7941.70012 [Google Scholar] [Crossref]

11. Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806 [Google Scholar] [Crossref]

12. Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/ 10.1080/ 14780887.2020.1769238 [Google Scholar] [Crossref]

13. Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE Publications. [Google Scholar] [Crossref]

14. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 [Google Scholar] [Crossref]

15. Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence How AI Fits into Your Data Science Team. https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-ArtificialIntelligence.pdf [Google Scholar] [Crossref]

16. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8 [Google Scholar] [Crossref]

17. Charness, N., & Boot, W. (2020). Technology Acceptance Model – an overview. ScienceDirect Topics. [Google Scholar] [Crossref]

18. https://www.sciencedirect.com/topics/social-sciences/technology-acceptance-model [Google Scholar] [Crossref]

19. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114(1), 254–280. https://doi.org/ 10.1016/j.techfore.2016.08.019 [Google Scholar] [Crossref]

20. Government of Pakistan, Ministry of Information Technology & Telecommunication. (2023). National AI policy consultation draft (V1). Islamabad, Pakistan. [Google Scholar] [Crossref]

21. https://moitt.gov.pk/SiteImage/Misc/files/National%20AI%20Policy%20Consultation%20Draft%20V1pdf [Google Scholar] [Crossref]

22. Habets, O., Stoffers, J., van der Heijden, B., & Peters, P. (2020). Am I fit for tomorrow’s labor market? Graduates’ skills development for the 21st-century workforce. Sustainability, 12(18), 7746. https://doi.org/10.3390/su12187746 [Google Scholar] [Crossref]

23. Heinisch, D. P., Koenig, J., & Otto, A. (2019). A supervised machine learning approach to trace doctorate recipients’ employment trajectories. Quantitative Science Studies, 1–23. https://doi.org/ 10 .1162/qss_a_00001 [Google Scholar] [Crossref]

24. Higher Education Commission (HEC). (2023). Undergraduate education policy (Version 1.1). [Google Scholar] [Crossref]

25. Islamabad, Pakistan. https://www.hec.gov.pk/english/services/students/UEP/Documents/UGE-Policy.pdf [Google Scholar] [Crossref]

26. International Economic Development Council (IEDC). (2025, July). Artificial intelligence impact on labor markets: Literature review. Washington, DC: Economic Development Research Partners. [Google Scholar] [Crossref]

27. Johanna, C., Baines, S., Livingstone, C., Pereira, M., & Aditya, D. (2024). Chatting with the future: Parents’ perspectives on conversational AI in children’s education. International Journal of Technology in Education, 7(3), 573–586. https://doi.org/10.46328/ijte.812 [Google Scholar] [Crossref]

28. Khogali, H. O., & Mekid, S. (2023). The blended future of automation and AI: Long-term societal and ethical implications. Technology in Society, 73(1). https://doi.org/10.1016/j.techsoc.2023.102232 [Google Scholar] [Crossref]

29. Kim, J., & Kim, J. (2024). Acceptance of artificial intelligence technology and optimism regarding its impact on the job market among high school students. Journal of Higher Education Theory and Practice, 24(10). https://doi.org/10.33423/jhetp.v24i10.7373 [Google Scholar] [Crossref]

30. Kumar, K. P., Swarubini, P. J., & Ganapathy, N. (2025). Cognitive artificial intelligence. In Cognitive Artificial Intelligence (pp. 301–323). CRC Press. https://doi.org/10.1201/9781003492726-18 [Google Scholar] [Crossref]

31. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. International Journal of Intercultural Relations, 9(4), 289–331. http://dx.doi.org/10.1016/0147-1767(85)90062-8 [Google Scholar] [Crossref]

32. Mughal, M., & Qureshi, H. A. (2025). Shaping the Future of Learning: Students’ Perceptions of Artificial Intelligence in Pakistan’s Educational Ecosystem. International Journal of Social Sciences: Current and Future Research Trends, 23(1), 247-232. [Google Scholar] [Crossref]

33. https://ijsscfrtjournal.isrra.org/Social_Science_Journal/article/view/1965 [Google Scholar] [Crossref]

34. Mason, C. M., Chen, H., Evans, D., & Walker, G. (2023). Applying a skills taxonomy and machine learning to inform career and training decisions. International Journal of Information and Learning Technology, 40(4), 353–371. https://doi.org/10.1108/ijilt-05-2022-0106 [Google Scholar] [Crossref]

35. Ministry of Information Technology & Telecommunication [Pakistan]. (2025). National Artificial Intelligence Policy: Consultation Draft V1 [Pdf]. Government of Pakistan. [Google Scholar] [Crossref]

36. https://moitt.gov.pk/SiteImage/Misc/files/National%20AI%20Policy.pdf [Google Scholar] [Crossref]

37. National Career Development Association (NCDA). (2024). NCDA code of ethics. Broken Arrow, OK. https://www.ncda.org/aws/NCDA/asset_manager/get_file/3395 [Google Scholar] [Crossref]

38. Ojan, M. P., Lara-Navarra, P., & Sánchez-Navarro, J. (2025). Revolution or adaptation of soft skills? Evolution and priorities in the Spanish labour market. Transforming Government: People, Process and Policy. https://doi.org/10.1108/tg-11-2024-0278 [Google Scholar] [Crossref]

39. Organisation for Economic Co-operation and Development (OECD). (2024). OECD economic outlook: [Google Scholar] [Crossref]

40. Volume 2024, Issue 2 – Understanding labour shortages: The structural forces at play. [Google Scholar] [Crossref]

41. https://www.oecd.org/en/publications/2024/12/oecd-economic-outlook-volume-2024-issue- [Google Scholar] [Crossref]

42. 2_67bb8fac/full-report/understanding-labour-shortages-the-structural-forces-at-play_321e116a.html [Google Scholar] [Crossref]

43. Paić, G., & Serkin, L. (2025). The impact of artificial intelligence: From cognitive costs to global inequality. The European Physical Journal Special Topics, 234, 3045–3050. https://doi.org/ 10.1140/ epjs/s11734-025-01561-8 [Google Scholar] [Crossref]

44. Pakistan Institute of Development Economics (PIDE). (2021, September 22). Automation, inequality and the future of work. https://pide.org.pk/research/automation-inequality-and-the-future-of-work/ [Google Scholar] [Crossref]

45. Poláková, M., Suleimanová, J. H., Madzík, P., Copuš, L., Molnárová, I., & Polednová, J. (2023). Soft skills and their importance in the labour market under the conditions of industry 5.0. Heliyon, 9(8). Sciencedirect. https://doi.org/10.1016/j.heliyon.2023.e18670 [Google Scholar] [Crossref]

46. Qasim, M. (2025). The state of K–12 education in Pakistan: Challenges, reforms, and a way forward. HBond.https://hbond.org/the-state-of-k-12-education-in-pakistan-challenges-reforms-and-a-wayforward/ [Google Scholar] [Crossref]

47. Qureshi, H. A. (2018). Rethinking sampling in grounded theory: Reflections for novice grounded theorists. International Journal of Contemporary Research and Review, 9(6), 20187–20194. https://doi.org/10.15520/ijcrr/2018/9/06/530 [Google Scholar] [Crossref]

48. Qureshi, H. A. (2018). Theoretical sampling in qualitative research: A multi-layered nested sampling scheme. International Journal of Contemporary Research and Review, 9(8), 20218–20222. https:// doi.org/10.15520/ijcrr/2018/9/08/576 [Google Scholar] [Crossref]

49. Sain, Z. H. (2023). Revitalizing education in Pakistan: Challenges and recommendations. International Journal of Higher Education Management, 9(2). https://doi.org/ 10.24052/ijhem/ v09 n02/art-4 [Google Scholar] [Crossref]

50. Selenko, E., Bankins, S., Shoss, M., Warburton, J., & Restubog, S. L. D. (2022). Artificial intelligence and the future of work: A functional-identity perspective. Current Directions in Psychological Science, 31(3), 272–279. https://doi.org/10.1177/09637214221091823 [Google Scholar] [Crossref]

51. Sharf, R. S. (2013). Applying career development theory to counseling (6th ed., pp. 307–310). Brooks/Cole Cengage Learning. [Google Scholar] [Crossref]

52. Sharps, S. (2024). The impact of AI on the labour market. Institute for Global Change. [Google Scholar] [Crossref]

53. https://institute.global/insights/economic-prosperity/the-impact-of-ai-on-the-labour-market [Google Scholar] [Crossref]

54. Shen, Y. (2024). Artificial intelligence and the economy: The impact of artificial intelligence on the job market. Advances in Economics, Management and Political Sciences, 92(1), 71–74. https://doi.org/10.54254/2754-1169/92/20231275 [Google Scholar] [Crossref]

55. Soulami, M., Benchekroun, S., & Galiulina, A. (2024). Exploring how AI adoption in the workplace affects employees: A bibliometric and systematic review. Frontiers in Artificial Intelligence, 7(1). https://doi.org/10.3389/frai.2024.1473872 [Google Scholar] [Crossref]

56. United Nations Statistics Division. (2024). Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. United Nations [Google Scholar] [Crossref]

57. https://unstats.un.org/sdgs/indicators/Global-Indicator-Framework-after-2024-refinement-English.pdf [Google Scholar] [Crossref]

58. Ünlu, Z., & Qureshi, H. (2023). Theoretical saturation in grounded theory studies: An evaluative tool. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(1), 139–162. https://doi.org/ 10.18037/ ausbd. 1272631 [Google Scholar] [Crossref]

59. Vo, N. N. Y., Vu, Q. T., Vu, N. H., Vu, T. A., Mach, B. D., & Xu, G. (2022). Domain-specific NLP system to support learning path and curriculum design in tech universities. Computers and Education: Artificial Intelligence, 3, 100042. https://doi.org/10.1016/j.caeai.2021.100042 [Google Scholar] [Crossref]

60. Wong, L. P. W. (2024). Artificial Intelligence and Job Automation: Challenges for Secondary Students’ Career Development and Life Planning. Merits, 4(4), 370–399. https://doi.org/ 10.3390/ merits4040027 [Google Scholar] [Crossref]

61. World Bank. (2025). New technologies have boosted employment in East Asia and Pacific, but reforms needed to ensure continued job-creating growth. World Bank. [Google Scholar] [Crossref]

62. https://www.worldbank.org/en/news/press-release/2025/06/17/new-technologies-have-boostedemployment-in-east-asia-and-pacific-but-reforms-needed-to-ensure-continued-job-creating-growth. [Google Scholar] [Crossref]

63. World Economic Forum. (2023). The future of jobs report 2023. https://www3.weforum.org/docs/ WEF_Future_of_Jobs_2023.pdf [Google Scholar] [Crossref]

64. World Economic Forum. (2025). The future of jobs report 2025. [Google Scholar] [Crossref]

65. https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/introduction-the-globallabour-market-landscape-in-2025/ [Google Scholar] [Crossref]

66. Yang, T.-C., & Chang, C.-Y. (2022). Using institutional data and social media messages to predict students’ career decisions: A data-driven approach. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11185-3 [Google Scholar] [Crossref]

67. Zeineb Mezghani & Turki, A. (2025). Bridging the Gap Between Higher Education and Employability Through a Categorization of Employability Competences. https://doi.org/10.4018/979-8-3693-86231.ch011 [Google Scholar] [Crossref]

68. Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace AI coexistence: Emerging themes and research agenda. Technovation, 124, Article 102747. [Google Scholar] [Crossref]

69. https://www.sciencedirect.com/science/article/pii/S0166497223000585 [Google Scholar] [Crossref]

70. Zouheir, D. M. (2025). The Impact of Artificial Intelligence on the Labor Market: A Systematic Review. Pakistan Journal of Life and Social Sciences (PJLSS), 23(1). https://doi.org/10.57239/ pjlss2025-23.1.00544 [Google Scholar] [Crossref]

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