Cloud-Native AI-Driven Enterprise Automation for Scalable Digital Process Transformation in Multi-Industry Ecosystems
DOI:
https://doi.org/10.71426/jasm.v1.i1.pp60-74Keywords:
Artificial intelligence, enterprise automation, cloud-native systems, enterprise analytics, workflow optimization, enterprise scalabilityAbstract
The rapid digital transformation of enterprise ecosystems has increased the demand for scalable, adaptive, and governance-aware automation frameworks capable of supporting heterogeneous operational environments across healthcare, finance, oil and gas, cloud services, and public-sector infrastructures. Conventional enterprise automation systems primarily rely on static rule-based workflow execution mechanisms that exhibit limited adaptability under dynamic workload conditions, evolving compliance constraints, and distributed cloud-resource environments. This paper proposes an AI-driven enterprise automation framework for scalable digital process transformation in multi-industry ecosystems. The proposed framework integrates intelligent workflow orchestration, adaptive AI agents, governance-aware compliance validation, cloud-native resource optimization, process mining, and enterprise analytics within a unified operational architecture. A mathematical formulation is developed to represent workflow orchestration, enterprise utility optimization, compliance satisfaction, orchestration confidence, and cloud-resource scalability. The framework is experimentally evaluated using heterogeneous enterprise workflow datasets, including the BPI Challenge 2017 event logs, Helpdesk workflow traces, Google cluster workload telemetry, and derived robotic process automation execution traces. Experimental results demonstrate substantial improvements over conventional automation approaches, including a 58.64% reduction in workflow latency, a 110.57% increase in operational throughput, a 30.27% improvement in cloud-resource utilization efficiency, and a 77.31% reduction in workflow interruption probability. Governance-aware validation further improved compliance satisfaction across HIPAA, ADA, Section 508, and audit-consistency domains. The results confirm that the proposed framework provides an effective foundation for intelligent, scalable, and resilient enterprise automation in next-generation digital transformation ecosystems.
References
[1] Mahfouz A, Mahmoud TM, Eldin AS. A survey on behavioral biometric authentication on smartphones. Journal of Information Security and Applications. 2017 Oct 23;37:28–37. Available from: https://doi.org/10.1016/j.jisa.2017.10.002
[2] Meng W, Wong DS, Furnell S, Zhou J. Surveying the Development of Biometric User Authentication on Mobile Phones. IEEE Communications Surveys & Tutorials. 2014 Dec 31;17(3):1268-1293. Available from: https://ieeexplore.ieee.org/abstract/document/7000543
[3] Bours P, Mondal S. Continuous Authentication with Keystroke Dynamics. Norwegian Information Security Laboratory NISlab. 2015 Jan 1:41-58. Available from: https://doi.org/10.15579/gcsr.vol2.ch3
[4] Mu N, Xu X, Zhang X. Finding autofocus region in low contrast surveillance images using CNN-based saliency algorithm. Pattern Recognition Letters. 2019 Apr 16;125:124–132. Available from: https://doi.org/10.1016/j.patrec.2019.04.011
[5] Finnegan OL, White JW, Armstrong B, Adams EL, Burkart S, Beets MW, et al. The utility of behavioral biometrics in user authentication and demographic characteristic detection: A scoping review. Systematic Reviews. 2024 Feb 8;13(1):61. Available from: https://doi.org/10.1186/s13643-024-02451-1
[6] Keselj A, Milicevic M, Zubrinic K, Car Z. The Application of Deep Learning for the Evaluation of User Interfaces. Sensors. 2022 Nov 30;22(23):9336. Available from: https://doi.org/10.3390/s22239336
[7] Kim DH, Song BC. Virtual sample-based deep metric learning using discriminant analysis. Pattern Recognition. 2020 Sep 10;110:107643. Available from: https://doi.org/10.1016/j.patcog.2020.107643
[8] Teh PS, Teoh ABJ, Yue S. A survey of Keystroke Dynamics Biometrics. The Scientific World Journal. 2013 Jan 1;2013(1):408280. Available from: https://doi.org/10.1155/2013/408280
[9] Dillon R, De Marsico M. Behavioral biometrics for remote exam integrity: Continuous authenticity assessment via keystroke dynamics. Procedia Computer Science. 2025 Jan 1;274:402–411. Available from: https://doi.org/10.1016/j.procs.2025.12.040
[10] Syed R, Suriadi S, Adams M, Bandara W, Leemans M, Ouyang C, et al. Robotic Process Automation: Contemporary themes and challenges. Computers in Industry. 2020;115:103162. Available from: https://doi.org/10.1016/j.compind.2019.103162
[11] Aguirre S, Rodriguez A. Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study. In: Figueroa-García JC, López-Santana ER, Villa-Ramírez JL, Ferro-Escobar R, editors. Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science. vol 742. Springer, Cham; 2017. p. 65--71. Available from: https://doi.org/10.1007/978-3-319-66963-2_7
[12] Leno V, Dumas M, La Rosa M, Maggi FM. Multi-Perspective Process Model Discovery for Robotic Process Automation. In: CEUR Workshop Proceedings. vol 2114; 2018. p. 37--45. Available from: https://ceur-ws.org/Vol-2114/paper5.pdf
[13] van der Aalst WMP, Bichler M, Heinzl A. Robotic Process Automation. Business & Information Systems Engineering. 2018;60(4):269--272. Available from: https://doi.org/10.1007/s12599-018-0542-4
[14] Marrella A. What Automated Planning Can Do for Business Process Management. Business Process Management Journal. 2019;25(6):1502--1527. Available from: https://doi.org/10.1007/978-3-319-74030-0_1
[15] Mendling J, Pentland BT, Recker J. Building a complementary agenda for business process management and digital innovation. European Journal of Information Systems. 2020;29(3):208--219. Available from: https://doi.org/10.1080/0960085X.2020.1755207
[16] Binz T, Breiter G, Leymann F, Spatzier T. Portable Cloud Services Using TOSCA. IEEE Internet Computing. 2014;18(3):80--85. Available from: https://doi.org/10.1109/MIC.2012.43
[17] Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J. Borg, Omega, and Kubernetes. Communications of the ACM. 2016;59(5):50--57. Available from: https://dl.acm.org/doi/10.1145/2890784
[18] Newman S. Building Microservices. 2nd ed. O’Reilly Media; 2021. Available from: https://www.oreilly.com/library/view/building-microservices-2nd/9781492034018/
[19] van der Aalst WMP. Process Mining: Data Science in Action. 2nd ed. Springer; 2016. Available from: https://doi.org/10.1007/978-3-662-49851-4
[20] Dumas M, La Rosa M, Mendling J, Reijers HA. Fundamentals of Business Process Management. 2nd ed. Springer; 2018. Available from: https://doi.org/10.1007/978-3-662-56509-4
[21] Weske M. Business Process Management: Concepts, Languages, Architectures. 3rd ed. Springer; 2019. Available from: https://doi.org/10.1007/978-3-662-59432-2
[22] Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Available from: https://aima.cs.berkeley.edu/
[23] Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016. Available from: https://www.deeplearningbook.org/
[24] El-Gharib NM, Amyot D. Robotic Process Automation Using Process Mining: A Systematic Literature Review. arXiv preprint arXiv:2204.00751. 2022. Available from: https://arxiv.org/abs/2204.00751
[25] White SA. Introduction to BPMN. IBM Cooperation; 2004. Available from: https://www.omg.org/bpmn/Documents/Introduction_to_BPMN.pdf
[26] Ko RKL. A computer scientist’s introductory guide to business process management. Crossroads. 2009;15(4):11--18. Available from: https://doi.org/10.1145/1558897.1558901
[27] Paul J, Lu S, He B. Database systems on GPUs. Foundations and Trends in Databases. 2021 Jul 12;11(1):1-08. Available from: https://doi.org/10.1561/1900000076
[28] Dintakurthy Y, Innmuri RK, Vanteru A, Thotakuri A. Emerging applications of artificial intelligence in Edge computing: A comprehensive review. Journal of Modern Technology. 2024:175-185. Available from: https://doi.org/10.71426/jmt.v1.i2.pp175-185
[29] Soma AK. Hybrid RNN-GRU-LSTM model for accurate detection of DDOS attacks on IDS dataset. Journal of Modern Technology. 2024 May 14;2(1):283–291. Available from: https://doi.org/10.71426/jmt.v2.i1.pp283-291
[30] Penaganti R. Technical Implementation Guide: Modern Payment Solutions for Captive Finance. Journal of Computer Science and Technology Studies. 2025 Nov 6;7(11):346-362. Available from: https://doi.org/10.32996/jcsts.2025.7.11.34
[31] Saripudi K. A study on Artificial Intelligence and Cloud Computing Assistance for Enhancement of Startup Businesses. Journal of Computing and Data Technology. 2025 Jul 26;1(1):68–76. Available from: https://doi.org/10.71426/jcdt.v1.i1.pp68-76
[32] van Dongen B. BPI Challenge 2012. Eindhoven University of Technology; 2012. Available from: https://data.4tu.nl/articles/_/12689204/1
[33] Polato M. Dataset belonging to the help desk log of an Italian Company. University of Padova; 2017. Available from: https://data.4tu.nl/articles/_/12675977/1
[34] Reiss C, Wilkes J, Hellerstein JL. Google cluster-usage traces: format + schema. Google Inc., White Paper. 2011 Nov;1(1-14):83. Available from: https://github.com/google/cluster-data
[35] Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA. Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing. 2012 Oct 14. p. 1-13. Available from: https://doi.org/10.1145/2391229.2391236
[36] Berti A, Van Zelst SJ, van der Aalst W. Process mining for python (PM4Py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169. 2019 May 15. Available from: https://doi.org/10.48550/arXiv.1905.06169
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sudheekar Reddy Pothireddy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Journal of Applied Sciences and Modelling (JASM) Journal operates under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation. All authors publishing in JMT Journal accept these as the terms of publication.
