Automate Everything: Mastering Salesforce Flows

Main Article Content

Ranjith Kumar Kollu

Abstract

This work proposes the quantitative measures of Salesforce automation effect of three key CRM operations, which are quote approvals, rebate approvals, and customer course approvals. Findings obtained on a baseline of 90 days and a post-deployment of 90 days indicate that there have been significant changes in regard to the speed of the processes, their accuracy, and decrease in the number of man-hours performed manually. Flow Builder, Dynamic Forms, and Invocable Methods Apex along with each other helped to lessen the time of operation of the processes, reduce errors, and reduce the rate of rework of the thousands of transactions. The results affirm that the use of low-code automation allows generating quantifiable returns on performance and consistency. The evidence indicates that Salesforce Flows are very effective in enhancing reliability and efficiency in CRM business at the enterprise level.

18 19

Article Details

Section

Articles

How to Cite

Automate Everything: Mastering Salesforce Flows. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 252-257. https://doi.org/10.15662/kzt2p465

References

[1] Pokhrel, G., Sapkota, M., & Ghimire, S. (2024). Digital Transformation with Low-Code and No-Code Platforms. Digital Transformation With Low-Code and No-Code Platforms. https://doi.org/10.13140/rg.2.2.19131.94242

[2] Ledro, C., Nosella, A., Vinelli, A., Pozza, I. D., & Souverain, T. (2025). Artificial intelligence in customer relationship management: A systematic framework for a successful integration. Journal of Business Research, 199, 115531. https://doi.org/10.1016/j.jbusres.2025.115531

[3] Perla, S. & Wilmington University. (2022). Salesforce Automation with Flows: From Admin to AI. Journal of Computational Analysis and Applications, 850–856. https://www.researchgate.net/publication/391454730_ Salesforce_Automation_with_Flows_From_Admin_to_AI

[4] Okeke, N. H. E., & Akinbolajo, N. O. D. (2023). Integrating AI and automation into low-code development: Opportunities and challenges. International Journal of Science and Research Archive, 8(1), 1094–1109. https://doi.org/10.30574/ijsra.2023.8.1.0077

[5] Basu, S., Sinha, S. (2025). Comparative Analysis of Machine Learning and Deep Learning Approaches for Human Activity Recognition. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M., Nayak, S., Platos, J. (eds) Human-Centric Smart Computing. ICHCSC 2024. Smart Innovation, Systems and Technologies, vol 440. Springer, Singapore. https://doi.org/10.1007/978-981- 96-3420-0_27

[6] Krupcała, K. (2025). Usefulness of business simulations using CRM in the opinion of students of an economic and technical university. Procedia Computer Science, 270, 2443–2452. https://doi.org/10.1016/j.procs.2025.09.366

[7] Kandukuri, B. (2025). Digital Transformation in Child Welfare Services: A case study in enterprise Technology Implementation for Vulnerable Populations. European Modern Studies Journal, 9(5), 356–363. https://doi.org/10.59573/emsj.9(5).2025.33

[8] Wang, P., Wang, Z., Duan, J., Miao, Y., & Tian, W. (2025). Review of the development status of low-code programming software for robots. Digital Engineering., 4, 100039. https://doi.org/10.1016/j.dte.2025.100039

[9] Silva, J. X., Lopes, M., Avelino, G., & Santos, P. (2023). Low-code and No-code Technologies Adoption: A Gray Literature Review. Low-code and No-code Technologies Adoption: A Gray Literature Review, 388–395. https://doi.org/10.1145/3592813.3592929

[10] Al-Amin, N. K. O., Ewim, N. C. P., Igwe, N. a. N., & Ofodile, N. O. C. (2024). AI-Driven end-to-end workflow optimization and automation system for SMEs. International Journal of Management & Entrepreneurship Research, 6(11), 3666–3684. https://doi.org/10.51594/ijmer.v6i11.1688

[11] Shridhar, S. (2021). Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies. International Journal for Research in Applied Science and Engineering Technology, 9(12), 508–513. https://doi.org/10.22214/ijraset.2021.39328