ISSN : 2583-2646

Impact of Artificial Intelligence on IT Industry Jobs and Emerging Employment Opportunities

ESP Journal of Engineering & Technology Advancements
© 2025 by ESP JETA
Volume 5  Issue 4
Year of Publication : 2025
Authors : Sandeep Gupta
:10.56472/25832646/JETA-V5I4P123

Citation:

Sandeep Gupta, 2025. "Impact of Artificial Intelligence on IT Industry Jobs and Emerging Employment Opportunities", ESP Journal of Engineering & Technology Advancements  5(4): 156-167.

Abstract:

The advent of the use of Artificial Intelligence (AI) in the Information Technology (IT) field is causing a paradigm shift in the dynamics of the workforce. The given paper evaluates the two-fold effect of AI on IT employment, i.e., job displacement risk in the field of routine coding and support versus the establishment of high-value jobs by 2026. Industry indicator-based forecast shows that a 15-percent loss of entry-level manual testing and support positions occur, which is about 1.8 million displaced jobs worldwide. On the other hand, predict the development of 2.3 million AI-specialized jobs to appear, which translate into approximately 500,000 net new jobs. Explore the new careers that are emerging like Prompt Engineers and AI Ethicists, and the skills shortage that is endangering the development of the industry and found that, at the same time that automation jeopardizes traditional linear career trajectory, it prompts a so-called hybridization of roles, in which specialists must not only demonstrate technical skills but also understand the field to the same extent as the IT expert would do. This paper has shown a detailed road map that the stakeholders can use to make the transition into the AI-enhanced workforce in 2026.

References:

[1] S. R. Sagili, “Prompt-Instructed Generative based AI for Enhancing Transformer effectiveness Analysis,” in 2024 Asian Conference on Intelligent Technologies (ACOIT), 2024, pp. 1–5. doi: 10.1109/ACOIT62457.2024.10939616.

[2] G. V. RESEARCH, “Artificial Intelligence Market Size Report, 2030.”

[3] R. Dattangire, R. Vaidya, D. Biradar, and A. Joon, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), IEEE, 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730334.

[4] S. P. Kalava, “Building Trust in AI: Ethical Principles for Transparent Autonomous Systems,” urfpublishers, no. 2583–9888, p. 5, 2024.

[5] B. N. Ilag, S. Phalke, and Y. D. Athave, “A Multi-Layered Approach to IT Infrastructure Governance and Compliance: Security, Hardening, and Audit Readiness,” Int. J. Comput. Appl., vol. 187, no. 12, p. 9, Jun. 2025.

[6] S. Mathur and S. Gupta, “Supervised Machine Learning-Based Classification and Prediction of Breast Cancer,” Int. J. Intell. Syst. Appl. Eng, vol. 12, no. 3, pp. 0–3, 2024.

[7] N. Prajapati, “The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and Applications,” ESP J. Eng. Technol. Adv., vol. 5, no. 2, 2025, doi: 10.56472/25832646/JETA-V5I2P103.

[8] R. Palwe and A. Kumar, “Redefining usability in the age of generative AI : Towards a new evaluation paradigm,” Int. J. Comput. Artif. Intell., vol. 6, no. 2, pp. 155–163, 2025.

[9] B. Mann et al., “Language models are few-shot learners,” arXiv Prepr. arXiv2005.14165, vol. 1, no. 3, p. 3, 2020.

[10] S. K. Tiwari, “Automation Driven Digital Transformation Blueprint: Migrating Legacy QA to AI Augmented Pipelines,” Front. Emerg. Artif. Intell. Mach. Learn., vol. 2, no. 12, pp. 01–20, Dec. 2025, doi: 10.64917/feaiml/Volume02Issue12-01.

[11] S. Garg, “Next-Gen Smart City Operations with AIOps & IoT : A Comprehensive look at Optimizing Urban Infrastructure,” J. Adv. Dev. Res., vol. 12, no. 1, 2021, doi: 10.5281/zenodo.15364012.

[12] J. Thomas, K. V. Vedi, and S. Gupta, “The Effect and Challenges of the Internet of Things (IoT) on the Management of Supply Chains,” Int. J. Res. Anal. Rev., vol. 8, no. 3, 2021.

[13] A. Kushwaha, P. Pathak, and S. Gupta, “Review of optimize load balancing algorithms in cloud,” Int. J. Distrib. Cloud Comput., vol. 4, no. 2, pp. 1–9, 2016.

[14] M. Chui, E. Hazan, R. Roberts, A. Singla, and K. Smaje, “The economic potential of generative AI,” 2023.

[15] V. Prajapati, “Enhancing Supply Chain Resilience through Machine Learning- Based Predictive Analytics for Demand Forecasting,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 11, no. 3, 2025.

[16] K. M. R. Seetharaman and S. Pandya, “Importance Of Artificial Intelligence In Transforming Sales, Procurement, And Supply Chain Processes,” Int. J. Recent Technol. Sci. Manag., vol. 8, no. July, pp. 140–148, 2023.

[17] J. Thomas, K. V. Vedi, and S. Gupta, “Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics,” J. Emerg. Technol. Innov. Res., vol. 8, no. 9, 2021.

[18] N. Kolli, J. W. Sajja, and A. Nerella, “Building Secure AI Agents for Autonomous Data Access in Compliance/Regulatory-Critical Environments,” Comput. Fraud Secur., pp. 363–373, Sep. 2024, doi: 10.52710/cfs.746.

[19] S. Mathur and S. Gupta, “Classification and Detection of Automated Facial Mask to COVID-19 based on Deep CNN Model,” in 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023, 2023. doi: 10.1109/CICT59886.2023.10455699.

[20] A. Di Battista et al., “Future of jobs report 2023,” in World Economic Forum, 2023, pp. 972–978.

[21] R. Susskind and D. Susskind, “The future of the professions: How technology will transform the work of human experts,” J. Nurs. Regul., vol. 8, no. 2, p. 52, 2017.

[22] W. E. Forum, “Future of Jobs Report 2023: Up to a Quarter of Jobs Expected to Change in Next Five Years,” 2023.

[23] H. J. W. Daugherty, Paul R., Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press, 2018.

[24] K. Roose, “Futureproof: 9 Rules for Humans in the Age of Automation,” 2021.

[25] Y. LeCun, “A Path Towards Autonomous Machine Intelligence,” pp. 1–62, 2022.

[26] V. Verma, “Optimizing Database Performance for Big Data Analytics and Business Intelligence,” Int. J. Eng. Sci. Math., vol. 13, no. 11, pp. 56–75, 2024.

[27] P. Chandrashekar, “Advancements in Automated Incident Management: A Survey within Cloud-Native SRE (Site Reliability Engineering) Practices,” vol. 13, no. 6, pp. 601–609, 2023.

[28] I. A. T. for Humanity, “Ethically Aligned Design,” 2023.

[29] D. Acemoglu and P. Restrepo, “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment,” Am. Econ. Rev., vol. 108, no. 6, pp. 1488–1542, Jun. 2018, doi: 10.1257/aer.20160696.

[30] IDC, “Worldwide AI and Generative AI Spending Guide,” Int. Data Corp., p. 33198, 2025.

[31] S. Altman, “Moore’s Law for Everything,” 2021.

[32] U. S. B. of L. Statistics, “Employment Projections: 2022-2032,” no. 202, 2023.

[33] G. Maddali, “An Efficient Bio-Inspired Optimization Framework for Scalable Task Scheduling in Cloud Computing Environments,” Int. J. Curr. Eng. Technol., vol. 15, no. 3, 2025.

[34] A. R. Duggasani, “Scalable and Optimized Load Balancing in Cloud Systems: Intelligent Nature-Inspired Evolutionary Approach,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 5, May 2025, doi: 10.38124/ijisrt/25may1290.

[35] S. Overflow, “2024 Developer Survey.”

[36] D. Patel, “The Role of Amazon Web Services in Modern Cloud Architecture: Key Strategies for Scalable Deployment and Integration,” Asian J. Comput. Sci. Eng., vol. 9, no. 4, pp. 1–9, 2024.

[37] V. M. L. G. Nerella, “Automated Compliance Enforcement in Multi-Cloud Database Environments: A Comparative Study of Azure Purview, AWS Macie, and GCP DLP,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 11, no. 4, pp. 270–283, Jul. 2025, doi: 10.32628/CSEIT25111668.

[38] A. Parupalli and S. Pandya, “Compliance-Driven Data Governance : A Survey on GDPR , and HIPAA in Cloud Databases,” vol. 12, no. 6, pp. 828–836, 2022.

Keywords:

Artificial Intelligence, IT Industry, Job Displacement, Job Creation, Future of Work, Automation, 2026 Employment Trends.