Hyper Automation, a Combination of AI, ML, and Robotic Process Automation (RPA), to Achieve End-to-End Automation in Enterprise Workflows
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P109Keywords:
Hyperautomation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Enterprise Workflow, Digital TransformationAbstract
Modern enterprise context witnesses an exponentially increasing amount of data, business complexity, and the need to be agile. Such a situation requires abandoning the classical principles of automation in favor of adapting to the new requirements of intelligent and scalable automation. The next frontier in digital transformation is hyperautomation. This strategic initiative combines Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and other technologies to create an automation-driven business and technology strategy. In contrast to standalone automation, hyperautomation orchestrates the full capabilities of technology to automate business processes comprehensively, enabling cognitive business decisions and self-healing business systems to be performed. This paper addresses the theoretical and practical aspects of hyperautomation in business processes, explaining the concepts of the architectural approach, the actions and approaches, and the combinatory interaction of artificial intelligence, machine learning, and robotic process automation. The practical applications in industry fields, namely finance, healthcare, and manufacturing, are discussed and demonstrate quantifiable increases in efficiency, accuracy, and scalability. Furthermore, the paper highlights the challenges of integration, data governance, security, and workforce transformation, offering a progressive perspective on the future sustainability of automation. With this thorough literature survey, flowcharts, tabular assessments, and practical outcomes in place, this article provides a solid foundation for any further research and adoption in enterprises
References
[1] Gao, J., van Zelst, S. J., Lu, X., & van der Aalst, W. M. (2019, October). Automated robotic process automation: A self-learning approach. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 95-112). Cham: Springer International Publishing.
[2] Lacity, M. C., & Willcocks, L. P. (2016). A new approach to automating services. MIT Sloan Management Review.
[3] Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J., Ouyang, C., ... & Reijers, H. A. (2020). Robotic process automation: contemporary themes and challenges. Computers in industry, 115, 103162.
[4] Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International journal of production economics, 224, 107546.
[5] Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). Using AI to enhance business operations. MIT Sloan Management Review, 60(4).
[6] Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans-and where they can't (yet). The McKinsey Quarterly, 1-12.
[7] Vial, G. (2021). Understanding digital transformation: A review and a research agenda. Managing digital transformation, 13-66.
[8] Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press.
[9] Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108–116.
[10] Willcocks, L. P., Lacity, M., & Craig, A. (2015). The IT function and robotic process automation.
[11] Dery, K., Sebastian, I. M., & van der Meulen, N. (2017). The digital workplace is key to digital innovation. MIS Quarterly Executive, 16(2).
[12] Haleem, A., Javaid, M., Singh, R. P., Rab, S., & Suman, R. (2021). Hyperautomation for enhancing automation in industries. Sensors International, 2, 100124.
[13] Chakraborti, T., Isahagian, V., Khalaf, R., Khazaeni, Y., Muthusamy, V., Rizk, Y., & Unuvar, M. (2020, September). From Robotic Process Automation to Intelligent Process Automation: –Emerging Trends–. In International Conference on Business Process Management (pp. 215-228). Cham: Springer International Publishing.
[14] Kopeć, W., Skibiński, M., Biele, C., Skorupska, K., Tkaczyk, D., Jaskulska, A., ... & Marasek, K. (2018). Hybrid approach to automation, RPA and machine learning: a method for the human-centred design of software robots. arXiv preprint arXiv:1811.02213.
[15] Tyagi, A. K., & Chahal, P. (2020). Artificial intelligence and machine learning algorithms. In Challenges and applications for implementing machine learning in computer vision (pp. 188-219). IGI Global Scientific Publishing.
[16] What are the Benefits of Hyper-Automation?, itconvergence, 2023. online. https://www.itconvergence.com/blog/what-are-the-benefits-of-hyper-automation/
[17] Rajput, V. U. (2013). Research on Know Your Customer (KYC). International Journal of Scientific and Research Publications, 3(7), 541-546.
[18] Wilson, R. (2022). Age of Invisible Machines: A Practical Guide to Creating a Hyperautomated Ecosystem of Intelligent Digital Workers. John Wiley & Sons.
[19] George, A. S., George, A. H., Baskar, T., & Sujatha, V. (2023). The rise of hyperautomation: a new frontier for business process automation. Partners Universal International Research Journal, 2(4), 13-35.
[20] Lacity, M., & Willcocks, L. (2016). Robotic Process Automation: The Next Transformation Lever for Shared Services. LSE Outsourcing Unit.
[21] Aguirre, S., & Rodriguez, A. (2017). Automation in BPO: RPA as a digital enabler. In The Future of Business Process Outsourcing (pp. 45–55). Springer.
[22] Rusum, G. P., Pappula, K. K., & Anasuri, S. (2020). Constraint Solving at Scale: Optimizing Performance in Complex Parametric Assemblies. International Journal of Emerging Trends in Computer Science and Information Technology, 1(2), 47-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I2P106
[23] Pappula, K. K. (2020). Browser-Based Parametric Modeling: Bridging Web Technologies with CAD Kernels. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 56-67. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P107
[24] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[25] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
[26] Pappula, K. K. (2021). Modern CI/CD in Full-Stack Environments: Lessons from Source Control Migrations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 51-59. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P106
[27] Pedda Muntala, P. S. R. (2021). Prescriptive AI in Procurement: Using Oracle AI to Recommend Optimal Supplier Decisions. International Journal of AI, BigData, Computational and Management Studies, 2(1), 76-87. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I1P108
[28] Rahul, N. (2021). Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 43-53. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106
[29] Enjam, G. R., Chandragowda, S. C., & Tekale, K. M. (2021). Loss Ratio Optimization using Data-Driven Portfolio Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 54-62. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107
[30] Rusum, G. P., & Pappula, K. K. (2022). Federated Learning in Practice: Building Collaborative Models While Preserving Privacy. International Journal of Emerging Research in Engineering and Technology, 3(2), 79-88. https://doi.org/10.63282/3050-922X.IJERET-V3I2P109
[31] Pappula, K. K. (2022). Modular Monoliths in Practice: A Middle Ground for Growing Product Teams. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 53-63. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P106
[32] Anasuri, S. (2022). Next-Gen DNS and Security Challenges in IoT Ecosystems. International Journal of Emerging Research in Engineering and Technology, 3(2), 89-98. https://doi.org/10.63282/3050-922X.IJERET-V3I2P110
[33] Pedda Muntala, P. S. R. (2022). Detecting and Preventing Fraud in Oracle Cloud ERP Financials with Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 57-67. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P107
[34] Rahul, N. (2022). Enhancing Claims Processing with AI: Boosting Operational Efficiency in P&C Insurance. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 77-86. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P108
[35] Enjam, G. R., & Tekale, K. M. (2022). Predictive Analytics for Claims Lifecycle Optimization in Cloud-Native Platforms. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P110
[36] Rusum, G. P., & Pappula, K. K. (2023). Low-Code and No-Code Evolution: Empowering Domain Experts with Declarative AI Interfaces. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 105-112. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P112
[37] Pappula, K. K., & Rusum, G. P. (2023). Multi-Modal AI for Structured Data Extraction from Documents. International Journal of Emerging Research in Engineering and Technology, 4(3), 75-86. https://doi.org/10.63282/3050-922X.IJERET-V4I3P109
[38] Anasuri, S. (2023). Confidential Computing Using Trusted Execution Environments. International Journal of AI, BigData, Computational and Management Studies, 4(2), 97-110. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P111
[39] Pedda Muntala, P. S. R., & Jangam, S. K. (2023). Context-Aware AI Assistants in Oracle Fusion ERP for Real-Time Decision Support. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 75-84. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P109
[40] Rahul, N. (2023). Transforming Underwriting with AI: Evolving Risk Assessment and Policy Pricing in P&C Insurance. International Journal of AI, BigData, Computational and Management Studies, 4(3), 92-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P110
[41] Enjam, G. R. (2023). AI Governance in Regulated Cloud-Native Insurance Platforms. International Journal of AI, BigData, Computational and Management Studies, 4(3), 102-111. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P111
[42] Pappula, K. K., & Anasuri, S. (2020). A Domain-Specific Language for Automating Feature-Based Part Creation in Parametric CAD. International Journal of Emerging Research in Engineering and Technology, 1(3), 35-44. https://doi.org/10.63282/3050-922X.IJERET-V1I3P105
[43] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[44] Enjam, G. R. (2020). Ransomware Resilience and Recovery Planning for Insurance Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 1(4), 29-37. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P104
[45] Pappula, K. K., Anasuri, S., & Rusum, G. P. (2021). Building Observability into Full-Stack Systems: Metrics That Matter. International Journal of Emerging Research in Engineering and Technology, 2(4), 48-58. https://doi.org/10.63282/3050-922X.IJERET-V2I4P106
[46] Pedda Muntala, P. S. R., & Karri, N. (2021). Leveraging Oracle Fusion ERP’s Embedded AI for Predictive Financial Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(3), 74-82. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P108
[47] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[48] Enjam, G. R. (2021). Data Privacy & Encryption Practices in Cloud-Based Guidewire Deployments. International Journal of AI, BigData, Computational and Management Studies, 2(3), 64-73. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P108
[49] Rusum, G. P. (2022). WebAssembly across Platforms: Running Native Apps in the Browser, Cloud, and Edge. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 107-115. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P112
[50] Pappula, K. K. (2022). Architectural Evolution: Transitioning from Monoliths to Service-Oriented Systems. International Journal of Emerging Research in Engineering and Technology, 3(4), 53-62. https://doi.org/10.63282/3050-922X.IJERET-V3I4P107
[51] Anasuri, S. (2022). Adversarial Attacks and Defenses in Deep Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 77-85. https://doi.org/10.63282/xs971f03
[52] Pedda Muntala, P. S. R. (2022). Anomaly Detection in Expense Management using Oracle AI Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 87-94. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P109
[53] Rahul, N. (2022). Automating Claims, Policy, and Billing with AI in Guidewire: Streamlining Insurance Operations. International Journal of Emerging Research in Engineering and Technology, 3(4), 75-83. https://doi.org/10.63282/3050-922X.IJERET-V3I4P109
[54] Enjam, G. R. (2022). Energy-Efficient Load Balancing in Distributed Insurance Systems Using AI-Optimized Switching Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 68-76. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P108
[55] Rusum, G. P., & Anasuri, S. (2023). Composable Enterprise Architecture: A New Paradigm for Modular Software Design. International Journal of Emerging Research in Engineering and Technology, 4(1), 99-111. https://doi.org/10.63282/3050-922X.IJERET-V4I1P111
[56] Pappula, K. K. (2023). Reinforcement Learning for Intelligent Batching in Production Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 76-86. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P109
[57] Anasuri, S. (2023). Secure Software Supply Chains in Open-Source Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 62-74. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P108
[58] Pedda Muntala, P. S. R., & Karri, N. (2023). Leveraging Oracle Digital Assistant (ODA) to Automate ERP Transactions and Improve User Productivity. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 97-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P111
[59] Rahul, N. (2023). Personalizing Policies with AI: Improving Customer Experience and Risk Assessment. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 85-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P110
[60] Enjam, G. R. (2023). Modernizing Legacy Insurance Systems with Microservices on Guidewire Cloud Platform. International Journal of Emerging Research in Engineering and Technology, 4(4), 90-100. https://doi.org/10.63282/3050-922X.IJERET-V4I4P109
[61] Pappula, K. K., & Rusum, G. P. (2021). Designing Developer-Centric Internal APIs for Rapid Full-Stack Development. International Journal of AI, BigData, Computational and Management Studies, 2(4), 80-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P108
[62] Pedda Muntala, P. S. R. (2021). Integrating AI with Oracle Fusion ERP for Autonomous Financial Close. International Journal of AI, BigData, Computational and Management Studies, 2(2), 76-86. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P109
[63] Rusum, G. P., & Pappula, kiran K. . (2022). Event-Driven Architecture Patterns for Real-Time, Reactive Systems. International Journal of Emerging Research in Engineering and Technology, 3(3), 108-116. https://doi.org/10.63282/3050-922X.IJERET-V3I3P111
[64] Anasuri, S. (2022). Formal Verification of Autonomous System Software. International Journal of Emerging Research in Engineering and Technology, 3(1), 95-104. https://doi.org/10.63282/3050-922X.IJERET-V3I1P110
[65] Pedda Muntala, P. S. R. (2022). Enhancing Financial Close with ML: Oracle Fusion Cloud Financials Case Study. International Journal of AI, BigData, Computational and Management Studies, 3(3), 62-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P108
[66] Rusum, G. P. (2023). Secure Software Supply Chains: Managing Dependencies in an AI-Augmented Dev World. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 85-97. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P110
[67] Anasuri, S. (2023). Synthetic Identity Detection Using Graph Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 87-96. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P110
[68] Pedda Muntala, P. S. R. (2023). AI-Powered Chatbots and Digital Assistants in Oracle Fusion Applications. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 101-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P111
[69] Enjam, G. R. (2023). Optimizing PostgreSQL for High-Volume Insurance Transactions & Secure Backup and Restore Strategies for Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 104-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P112
