Engineering AI Augmented Admin Tools: Automation of Repetitive Workflows in B2B SaaS Systems |
||
 |
 |
|
| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-5 |
||
| Year of Publication : 2025 | ||
| Authors : Naga Ravi Teja Vadrevu | ||
| DOI : 10.14445/22312803/IJCTT-V73I5P124 | ||
How to Cite?
Naga Ravi Teja Vadrevu, "Engineering AI Augmented Admin Tools: Automation of Repetitive Workflows in B2B SaaS Systems," International Journal of Computer Trends and Technology, vol. 73, no. 5, pp. 185-195, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P124
Abstract
The rise of AI technologies has radically reshaped the world of B2B SaaS systems, especially when automating dull, repetitive admin processes. This article reviews the engineering paradigms and methods of developing AI-assisted admin tools harnessing intelligent process automation (IPA), robotic process automation (RPA), and machine learning algorithms to optimize enterprise workflows. Based on extensive coverage of the recent literature between 2015 and 2025, this paper explores the architectural structures, deployment strategies, and performance indicators used in AI‐based workflow automation solutions. The main results are hybrid approaches supporting rule-based automation and knowledge and learning-based approaches, resulting in higher scalability and flexibility than simple sequential data processing solutions. This work informs design principles for next-generation admin tools. It elucidates emerging trends such as agentic AI, hyper-automation, and federated learning architecture that stand to revolutionize B2B SaaS operational efficiency further.
Keywords
Artificial Intelligence, Intelligent Process Automation, B2B SAAS, Workflow Automation, Enterprise Systems.
Reference
[1] Botan Shivan Mustafa, and Subhi R. M. Zeebaree, “AI Driven Innovations in Enterprise Systems,” International Journal of Scientific World, vol. 11, no. 1, pp. 127-136, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Claudio Di Ciccio, Andrea Marrella, and Alessandro Russo, “Knowledge Intensive Processes: Characteristics, Requirements, and Analysis of Contemporary Approaches,” Journal on Data Semantics, vol. 4, no. 1, pp. 29-57, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Andres Jimenez-Ramirez et al., “A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle,” Advanced Information Systems Engineering, pp. 446-461, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Wil M.P. van der Aalst, Martin Bichler, and Armin Heinzl, “Robotic Process Automation,” Business & Information Systems Engineering, vol. 60, no. 4, pp. 269 272, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Antonio Bosco et al., “Discovering Automatable Routines from User Interaction Logs,” Business Process Management Forum, vol. 360, pp. 144-162, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Tathagata Chakraborti et al., “From Robotic Process Automation to Intelligent Process Automation,” Business Process Management: Blockchain, and Robotic Process Automation Forum, vol. 393, pp. 215-228, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Simone Agostinelli, Andrea Marrella, and Massimo Mecella, “Towards Intelligent Robotic Process Automation for BPMers,” arXiv:2001.00804, pp. 1-7, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Marlon Dumas et al., “AI Augmented Business Process Management Systems: A Research Manifesto,” ACM Transactions on Management Information Systems, vol. 14, no. 1, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Junxiong Gao et al., “Automated Robotic Process Automation: A Self Learning Approach,” On the Move to Meaningful Internet Systems: OTM Conferences, pp. 95-112, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] M. Lacity, and L. Willcocks, “A New Approach to Automating Services,” MIT Sloan Management Review, vol. 58, no. 1, pp. 40-49, 2016.
[Google Scholar] [Publisher Link]
[11] Volodymyr Leno et al., “Automated Discovery of Data Transformations for Robotic Process Automation,” arXiv preprint arXiv:2001.01007, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Deborah Ferreira et al., “On the Evaluation of Intelligent Process Automation,” arXiv:2001.02639, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ingo Weber et al., “Untrusted Business Process Monitoring, and Execution Using Blockchain,” Business Process Management, pp. 329-347, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Abdulaziz Aldoseri, Khalifa N. Al-Khalifa, and Abdel Magid Hamouda, “AI Powered Innovation in Digital Transformation: Key Pillars, and Industry Impact,” Sustainability, vol. 16, no. 5, pp. 1-25, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Philip Jorzik et al., “AI Driven Business Model Innovation: A Systematic Review, and Research Agenda,” Journal of Business Research, vol. 182, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Yanhuai Jia, and Zheng Wang, “Application of Artificial Intelligence Based on the Fuzzy Control Algorithm in Enterprise Innovation,” Heliyon, vol. 10, no. 6, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ashutosh Ahuja, Anant Wairagade, and Nikhil Gupta, “AIREA: An AI Driven Optimization Framework for Intelligent Automation in Large Scale Enterprise Systems,” SSRN, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fethi Rabhi, Amin Beheshti, and Asif Gill, “Editorial: Business Transformation through AI Enabled Technologies,” Frontiers in Artificial Intelligence, vol. 8, pp. 1-3, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Prachi Juyal et al., “The Role of Artificial Intelligence in Enhancing Decision Making in Enterprise Information Systems,” Journal of Information Systems Engineering, and Management, vol. 10, no. 3s, pp. 196-205, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Najah Mary El-Gharib, and Daniel Amyot, “Robotic Process Automation Using Process Mining: A Systematic Literature Review,” arXiv:2204.00751, pp. 1-51, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Lukas-Valentin Herm et al., “A Framework for Implementing Robotic Process Automation Projects,” Information Systems, and e Business Management, vol. 21, no. 1, pp. 1-35, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yuqing Wang, and Xiao Yang, “Machine Learning Based Cloud Computing Compliance Process Automation,” arXiv preprint arXiv:2502.16344, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]