Artificial Intelligence (AI) has become an important technological advancement that influences employee performance and job redesign across different industries. This study focuses on analysing the impact of AI adoption on employee performance and job redesign in Theni district through a cross-industry perspective. The study covers respondents from education, human resource management, architecture, photography, and other professional sectors. A descriptive research design was adopted, and data were collected from 100 respondents using convenience sampling. The collected data were analysed using Frequency Analysis, Friedman Test, Mean and Standard Deviation, and Factor Analysis. The findings reveal that AI adoption has improved productivity, work quality, efficiency, creativity, and decision-making processes in various industries. AI tools are widely used for recruitment, academic evaluation, photography editing, architectural designing, and automation of routine tasks. At the same time, respondents expressed concerns regarding stress in adapting to AI technologies, job insecurity, reduced human involvement, and overdependence on technology. The study also identified that AI has significantly transformed traditional work methods by shifting manual activities toward technology-oriented tasks and changing employee roles and responsibilities.
Sasirekha, J. (2026). Impact of Artificial Intelligence Adoption on Employee Performance and Job Redesign: A Cross-Industry Study. International Journal of Education, Modern Management, Applied Science & Social Science, 08(03(I)), 11–23. https://doi.org/10.62823/IJEMMASSS/8.3(I).9196
- Jiang, Y., Li, X., Luo, H. et al. Quo vadis artificial intelligence?. DiscovArtif Intell 2, 4 (2022). https://doi.org/10.1007/s44163-022-00022-8
- Oluwagbemiga, A. (2008). A literature review on artificial intelligence. International Journal of Information and Management Sciences, 19(4), 535–570.
- E. S. Brunette, R. C. Flemmer and C. L. Flemmer, "A review of artificial intelligence," 2009 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand, 2009, pp. 385-392, doi: 10.1109/ICARA.2000.4804025.
- Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of artificial intelligence in machine learning: Review and prospect. International Journal of Computer Applications, 115(9), 31–41.
- Radhakrishnan, J., & Chattopadhyay, M. (2020). Determinants and barriers of artificial intelligence adoption – A literature review. In International Working Conference on Transfer and Diffusion of IT (pp. 89–99). Springer. https://doi.org/10.1007/978-3-030-64849-7_9
- Riaz, H., &Ghanghas, S. (2024). Artificial intelligence in employee performance evaluation and its managerial implication. Journal of Informatics Education and Research, 4(1), 299–307.
- Purohit, M., Chopde, M., & Tale, T. (2026). Transforming the future of work through AI, automation, and job redesign. International Journal of Commerce and Management Studies (IJCAMS), 11(1), 91–94.
- Bhattacharjee, G. (2023). Art and photography in the age of artificial intelligence. 1–9.
- Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
- Kalay, Y. E. (2004). Architecture’s new media: Principles, theories, and methods of computer-aided design. MIT Press.
- Charlwood, A., &Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal, 32(4), 729–742. https://doi.org/10.1111/1748-8583.12433.
