Intelligent Decision Support Systems for Enterprise Modernization using Generative AI Predictive Modeling and Process Automation
DOI:
https://doi.org/10.15662/IJARCST.2024.0703012Keywords:
Intelligent decision support systems, generative AI, predictive modeling, process automation, enterprise modernization, machine learning, robotic process automation, decision intelligence, digital transformation, workflow automation, enterprise analytics, AI governance, data-driven decision-making, intelligent systems, business intelligenceAbstract
learning algorithms, and generative AI capabilities to transform raw enterprise data into actionable insights, automated recommendations, and adaptive workflows. Generative AI enhances decision support by producing contextual insights, scenario simulations, and natural language explanations, enabling decision-makers to interpret complex data more effectively. Predictive modeling further strengthens these systems by forecasting trends, identifying risks, and optimizing resource allocation across business functions. Process automation, including robotic process automation and intelligent workflow orchestration, ensures seamless execution of decisions with minimal human intervention. Together, these technologies create a unified framework for intelligent enterprise modernization that improves responsiveness, reduces operational inefficiencies, and supports data-driven strategic planning. However, challenges such as model transparency, data governance, integration complexity, and ethical concerns remain significant barriers. This study explores the architectural and conceptual foundations of intelligent decision support systems in enterprise environments. A qualitative research methodology based on systematic literature review and conceptual synthesis is employed. Findings indicate that integrating generative AI, predictive modeling, and automation significantly enhances enterprise decision intelligence and modernization outcomes
References
1. Amazon Web Services. (2024). Generative AI and analytics services. https://aws.amazon.com
2. Berman, S. J. (2012). Digital transformation and enterprise decision systems. Strategy & Leadership, 40(2), 16–24.
3. Databricks. (2024). AI-powered data intelligence platform. https://www.databricks.com
4. Google Cloud. (2024). Vertex AI and decision intelligence tools. https://cloud.google.com
5. IBM. (2023). AI-driven enterprise decision systems. https://www.ibm.com
6. Kumar, V., et al. (2020). Intelligent decision systems in enterprises. International Journal of Information Management.
7. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
8. Polamreddy, V. R. (2023). Event-Driven Integration Patterns for Financially Sensitive Enterprise Platforms. International Journal of Science, Research and Technology, 6(4), 10313-10323.
9. Sarngadharan, S. (2023). Federated data pipelines enabling continuous contract and asset state traceability. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8114–8123. https://doi.org/10.15662/IJRPETM.2023.0601011
10. Veershetty, G. (2023). SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis. International Journal of Emerging Research in Engineering and Technology, 4(1), 218-227.
11. Gollapudi, R. (2024). Event-aware multi-layer storage risk forecasting for Oracle database estates using HAPF. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.5183
12. Chettiyar, S. S. S. (2023). A vendor-neutral omnichannel conversational payment architecture for conversational commerce integrating BYOP, native solutions, and PCI compliance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8124–8135. https://doi.org/10.15662/IJRPETM.2023.0601012
13. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
14. Sivakumer, D. (2023). ServiceNow-based project management models for scalable enterprise workflow automation. International Journal of Future Innovative Science and Technology (IJFIST), 6(4), 11003–11014. https://doi.org/10.15662/IJFIST.2023.0604006
15. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
16. Syed, S. (2023). A GxP-compliant integrated ERP framework for synchronizing OPM, SCM, and quality lab systems in pharmaceutical manufacturing. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8064–8076. https://doi.org/10.15662/IJRPETM.2023.0601007
17. Lanka, S. (2023). Blurring boundaries where artificial intelligence ends and human potential begins. International Journal of Computer Technology and Electronics Communication, 6(4), 7331–7341.
18. Devineni, A. (2023). Automated Compliance-Driven Patch Management and Security Hardening in Multi-Cloud Banking Infrastructure Using IaC and Python Orchestration. The American Journal of ET, 5(12), 68-80.
19. Govindan, V. (2023). AI-powered optimization of non-production environments: Turning constraints into business value. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8089–8104. https://doi.org/10.15662/IJRPETM.2023.0601009
20. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
21. Joyce, S. (2023). Accelerating Enterprise SAP Workload Performance and Automation Using Microsoft Azure Center for SAP Solutions Through Cloud Native Architecture Intelligent Orchestration and Infrastructure as Code. IACSE-International Journal of Information Technology (IACSE-IJIT), 4(1), 8-30.
22. Gandikota, S. P. (2023). An elastic cloud-native framework for processing millions of IoT events per second in smart grid environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8049–8063. https://doi.org/10.15662/IJRPETM.2023.0601006
23. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
24. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
25. Siddiqui, M. I. H., Bishnu, K. K., Al Mamun, M. A., Raihan, M., Islam, A., Akter, S., & Hossain, I. (2023). Explainable Federated Deep Learning for Low-Cost and Privacy-Preserving Early Breast Cancer Screening to Reduce US Healthcare Burden. Vascular and Endovascular Review, 6(2), 45-54.
26. Katta, T. B. (2023). Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration. International Journal of Science, Research and Technology, 6(6), 11080-11084.
27. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
28. Gopisetty, S. (2023). Who Watches the Cloud Watcher? Building a Team of AI Agents to Continuously Verify Shared Security Controls When a Mid-Sized Bank Can't Trust the SOC Report Alone. European Journal of Advances in Engineering and Technology, 10(10), 165-178.
29. Mannem, S. (2023). Intelligent service behavior analysis for early cyber threat prediction. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8077–8088. https://doi.org/10.15662/IJRPETM.2023.0601008
30. Parasa, M. (2023). Integrating SAP SuccessFactors LMS with external digital learning ecosystems: Toward a unified enterprise knowledge framework. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(7), 514–534.
31. Chenna, S. (2023). Solution-led integration architecture in Oracle EBS: A dual case study from foundational enterprise engagements. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8105–8113. https://doi.org/10.15662/IJRPETM.2023.0601010
32. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
33. Navandar, P. (2023). Ensemble based intrusion detection in heterogeneous networks: A machine learning framework with zero trust integration. International Journal of Advanced Engineering Science and Information Technology, 6(1), 10827–10837. https://doi.org/10.15662/IJAESIT.2023.0601004
34. Goel, N. Vulnerability Management in Computer Systems: Challenges and Approaches. Educational Administration: Theory and Practice, 28(04), 718-724. Doi: 10.53555/kuey.v28i4.11607.Salesforce. (2024). AI-powered CRM and automation. https://www.salesforce.com
35. ServiceNow. (2023). Workflow automation and AI operations. https://www.servicenow.com
36. Snowflake. (2024). Data cloud for predictive analytics. https://www.snowflake.com
37. Konakalla, K. (2022). Automating customer feedback integration in the sales cycle: Enhancing sales performance and accountability through Salesforce and Medallia. Journal of Marketing & Supply Chain Management, 1-3.
38. Zhang, Y., et al. (2022). Generative AI in enterprise decision-making. IEEE Transactions on Artificial Intelligence.
39. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
