AI-Driven Digital Transformation in Business Process Optimization

Mohd Azher Parvez

doi:10.65301/iitm.2025.17.2.920

Articles Volume 16 Issue 2 July-December 2025

DOI 10.65301/iitm.2025.17.2.920

AI-Driven Digital Transformation in Business Process Optimization

Authors

MO

Mohd Azher Parvez

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Published 2025-12-30

Pages 1-7

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern business and industries, known as digital transformation, necessitates a fundamental shift in traditional understanding of a business process optimization and human resilience to turbulent socio economic and technological environment. AI emerges as a disruptive force, with immense potential to effect businesses and individuals on an unprecedented scale and exponential pace. This paper is to present AI-driven digital transformation in business process optimization through the theoretical fundamentals of the concept, business process management (BPM) by AI, finance and accounting and HR by AI-powered digital transformation for a business optimization perspective. AI accelerate how work gets done, making organizations organized, avoiding refusal, and increasing efficiency at scale.

Keywords

AI, AI-Powered, Digital, Block Chain, Ecological Need

References

1. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: uman–AI collaboration in the workplace. AI & Society, 33(1), 1-8.
https://doi.org/10.1007/s00146- 018-0812-5.
2. Lee, S. H., & Choi, Y. M. (2020). Smart manufacturing and AI: A review of applications and challenges in industrial automation. Journal
of Manufacturing Science and Engineering, 142(9), 091011. https:// doi.org/10.1115/1.4046729.
3. Li, C., Xu, X., & Lu, Y. (2021). The integration of AI technologies into industrial robotics for smart manufacturing systems: A review. Robotics and Computer-Integrated Manufacturing, 67, 101949. https://doi.org/ 10.1016/j.rcim.2020.101949.
4. Binns, S., & Ryan, M. (2021). AI and its role in industry: A systematic review of AI applications in manufacturing. AI Open, 2, 99-109. https:/ /doi.org/10.1016/j.aiopen.2021.02.002.
5. Goh, M., & Ng, S. (2020). Sustainable AI-driven optimization of supply chain management. Journal of Supply Chain Management, 56(2),123- 36. https://doi.org/10.1111/jscm.12147.
6. Tariq, M, Yawar H, Adil H, Aftab T, & Saad R. (2020). Principles and perspectives in medical diagnostic systems employing artificial intelligence (AI) algorithms. International Research Journal of Economics and Management Studies IRJEMS, 3(1).
7. Hussain, H. K., Tariq, A, & Gill, A.Y. (2023). Role of AI in cardiovascular health care: A brief overview. Journal of World Science 2(4): 794 802.
8. Ghelani, H. K. (2024). AI-driven quality control in PCB manufacturing: Enhancing production efficiency and precision. Valley International Journal Digital Library. 1549-1564. IITM Journal of Management and IT (December 2025) 16(2) 7
9. Kokala, A. (2024). Business process management: The synergy of intelligent automation and AI-driven workflows. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets65186.
10. Aani, S, Bonny, T, Hasan, S.W. & Hilal, N. (2019) Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? Desalination. https://doi.org/10.1016/ j.desal.2019.02.005.

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Mohd Azher Parvez . (2025). AI-Driven Digital Transformation in Business Process Optimization. IITM Journal of Management and IT, 16(2), 1-7.

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IITM Journal of Management and IT

Published by Institute of Information Technology & Management

ISSN 2349-9826

✓ Citation copied to clipboard

[1] 1. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: uman–AI collaboration in the workplace. AI & Society, 33(1), 1-8.

[2] https://doi.org/10.1007/s00146- 018-0812-5.

[3] 2. Lee, S. H., & Choi, Y. M. (2020). Smart manufacturing and AI: A review of applications and challenges in industrial automation. Journal

[4] of Manufacturing Science and Engineering, 142(9), 091011. https:// doi.org/10.1115/1.4046729.

[5] 3. Li, C., Xu, X., & Lu, Y. (2021). The integration of AI technologies into industrial robotics for smart manufacturing systems: A review. Robotics and Computer-Integrated Manufacturing, 67, 101949. https://doi.org/ 10.1016/j.rcim.2020.101949.

[6] 4. Binns, S., & Ryan, M. (2021). AI and its role in industry: A systematic review of AI applications in manufacturing. AI Open, 2, 99-109. https:/ /doi.org/10.1016/j.aiopen.2021.02.002.

[7] 5. Goh, M., & Ng, S. (2020). Sustainable AI-driven optimization of supply chain management. Journal of Supply Chain Management, 56(2),123- 36. https://doi.org/10.1111/jscm.12147.

[8] 6. Tariq, M, Yawar H, Adil H, Aftab T, & Saad R. (2020). Principles and perspectives in medical diagnostic systems employing artificial intelligence (AI) algorithms. International Research Journal of Economics and Management Studies IRJEMS, 3(1).

[9] 7. Hussain, H. K., Tariq, A, & Gill, A.Y. (2023). Role of AI in cardiovascular health care: A brief overview. Journal of World Science 2(4): 794 802.

[10] 8. Ghelani, H. K. (2024). AI-driven quality control in PCB manufacturing: Enhancing production efficiency and precision. Valley International Journal Digital Library. 1549-1564. IITM Journal of Management and IT (December 2025) 16(2) 7

[11] 9. Kokala, A. (2024). Business process management: The synergy of intelligent automation and AI-driven workflows. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets65186.

[12] 10. Aani, S, Bonny, T, Hasan, S.W. & Hilal, N. (2019) Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? Desalination. https://doi.org/10.1016/ j.desal.2019.02.005.