International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Integrating Generative AI and Robotic Process Automation (RPA) in Business Processes: Opportunities, Challenges, and Future Directions

Abstract

This paper explores the integration of Generative Artificial Intelligence (AI) and Robotic Process Automation (RPA) in transforming business processes. The study provides a comprehensive overview of both technologies, analyzes their convergence, and evaluates their impact on efficiency, innovation, and decision-making. Through literature review, case studies, and expert interviews, we assess the opportunities and challenges associated with deploying Generative AI and RPA across industries. The paper concludes with recommendations for implementation and future research directions.

Country : USA

1 Madan Mohan Ganapam

  1. Software Engineering Manager, AI, Intelligent Automation, RPA

IRJIET, Volume 9, Issue 6, June 2025 pp. 133-136

doi.org/10.47001/IRJIET/2025.906016

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References

  1. D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3
  2. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  3. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.
  4. Columbus, L. (2020). How AI is improving customer experience. Forbes. https://www.forbes.com/sites/louiscolumbus/2020/02/02/how-ai-is-improving-customer-experience/
  5. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  6. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
  7. Gasser, U., & Almeida, V. A. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62.
  8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680).
  9. Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83.
  10. Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146.
  11. Van der Aalst, W. M. (2021). Process mining and robotic process automation: Better together. IEEE Access, 9, 55521–55533. https://doi.org/10.1109/ACCESS.2021.3071420.
  12. Willcocks, L., Lacity, M., & Craig, A. (2015). The IT function and Robotic Process Automation. London School of Economics Outsourcing Unit Working Paper Series.
  13. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680).
  15. Van der Aalst, W. M. (2021). Process mining and robotic process automation: Better together. IEEE Access, 9, 55521–55533. https://doi.org/10.1109/ACCESS.2021.3071420.

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