AI-Driven Enterprise Transformation through Secure Mobile Platforms and Broadband Networks with Ethical Automation and Real-Time Intelligence
Abstract
Artificial Intelligence (AI) has emerged as a foundational driver of enterprise transformation, reshaping how organizations operate, compete, and innovate. The convergence of AI with secure mobile platforms, high-speed broadband networks, ethical automation frameworks, and real-time intelligence systems is redefining enterprise ecosystems across industries. This paper explores how AI-driven technologies enable scalable, agile, and intelligent enterprises by enhancing decision-making, operational efficiency, and customer engagement. Secure mobile platforms extend enterprise capabilities beyond traditional boundaries, while broadband networks provide the high-capacity, low-latency infrastructure required for real-time data processing. Ethical automation ensures responsible AI adoption by addressing transparency, fairness, accountability, and data privacy concerns. Furthermore, real-time intelligence enables organizations to respond dynamically to changing business environments through continuous data analysis and predictive insights. By synthesizing existing literature and proposing a comprehensive research methodology, this study examines the strategic, technological, and ethical dimensions of AI-enabled enterprise transformation. The paper also evaluates the advantages and limitations associated with this transformation, offering a balanced perspective for organizations seeking sustainable AI adoption. The findings contribute to both academic research and practical implementation strategies for AI-driven enterprises
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 4 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14466-14474 |
Published |
July 10, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Pedro André Ferreira (2024). AI-Driven Enterprise Transformation through Secure Mobile Platforms and Broadband Networks with Ethical Automation and Real-Time Intelligence. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 4 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14466-14474. https://doi.org/10.15662/IJAESIT.2024.0704003 |
References
2. Chandramohan, A. (2017). Exploring and overcoming major challenges faced by IT organizations in business process improvement of IT infrastructure in Chennai, Tamil Nadu. International Journal of Mechanical Engineering and Technology, 8(12), 254.
3. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
4. Kubam, C. S. (2026). Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines. arXiv preprint arXiv:2601.06241.
5. Panda, M. R., Selvaraj, A., & Muthusamy, P. (2023). FinTech Trading Surveillance Using LLM-Powered Anomaly Detection with Isolation Forests. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 530-564.
6. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157
7. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
8. Sakhawat Hussain, T., Rahanuma, T., & Md Manarat Uddin, M. (2023). Privacy-Preserving Behavior Analytics for Workforce Retention Approach. American Journal of Engineering, Mechanics and Architecture, 1(9), 188-215.
9. Gangina, P. (2023). Edge computing architectures for IoT data aggregation in industrial manufacturing. International Journal of Humanities and Information Technology (IJHIT), 5(1), 48–67. https://www.ijhit.info
10. Shapiro, C., & Varian, H. R. (1998). Information rules: A strategic guide to the network economy. Harvard Business School Press.
11. Rahman, M., Arif, M. H., Alim, M. A., Rahman, M. R., & Hossen, M. S. (2021). Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis.
12. Natta, P. K. (2023). Harmonizing enterprise architecture and automation: A systemic integration blueprint. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9746–9759. https://doi.org/10.15662/IJRPETM.2023.0606016
13. Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–334). Cambridge University Press.
14. Ponugoti, M. (2023). Frameworks for ensuring compliance in digital platform governance. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7575–7586.
15. Riggins, F. J., & Wamba, S. F. (2015). Research directions on the adoption, usage, and impact of the Internet of Things through the use of big data analytics. 24th European Conference on Information Systems (ECIS) Proceedings.
16. Sriramoju, S. (2024). Optimizing data flow: A unified approach for product, pricing, and revenue sync in enterprise systems. International Journal of Engineering & Extended Technologies Research, 6(1), 7492–7503
17. Singh, A. (2021). Unlocking Mesh Networks: Tackling Scalability in Dynamic Environments. IJSAT-International Journal on Science and Technology, 12(1).
18. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.
19. Surisetty, L. S. (2022). Modernizing Legacy Systems with AI Orchestration: From Monoliths to Autonomous Micro services. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(6), 7299-7306.
20. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.
21. Hasenkhan, F., Keezhadath, A. A., & Amarapalli, L. (2023). Intelligent Data Partitioning for Distributed Cloud Analytics. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 106-145.
22. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
23. Islam, M. M., Zerine, I., Rahman, M. A., Islam, M. S., & Ahmed, M. Y. (2024). AI-Driven Fraud Detection in Financial Transactions-Using Machine Learning and Deep Learning to Detect Anomalies and Fraudulent Activities in Banking and E-Commerce Transactions. Available at SSRN 5287281.
24. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations, 6(5), 9534-9538.
25. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
26. Thumala, S. R., & Pillai, B. S. (2024). Cloud Cost Optimization Methodologies for Cloud Migrations. International Journal of Intelligent Systems and Applications in Engineering.
27. Cheekati, S. (2023). Blockchain technology, big data, and government policy as catalysts of global economic growth. International Journal of Research and Applied Innovations (IJRAI), 6(2), 8593–8596. https://doi.org/10.15662/IJRAI.2023.0602004
28. Kesavan, E. (2023). ML-Based Detection of Credit Card Fraud Using Synthetic Minority Oversampling. International Journal of Innovations in Science, Engineering And Management, 55-62.
29. Pimpale, Siddhesh. (2021). Power Electronics Challenges and Innovations Driven by Fast- Charging EV Infrastructure. International Journal of Intelligent Systems and Applications in Engineering. 9. 144.
30. Rajan, P. K. (2023). Predictive Caching in Mobile Streaming Applications using Machine Learning Models. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8737-8745.
31. Chennamsetty, C. S. (2023). Standardizing Software Delivery: Unified Data Models and Scalable Infrastructure for Subscription Ecosystems. International Journal of Computer Technology and Electronics Communication, 6(2), 6658-6665.
32. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
33. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
34. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
35. Mudunuri, P. R. (2023). Governance-aware infrastructure-as-code for regulated research environments. International Journal of Research in Engineering, Project Management and Technology (IJRPETM), 6(4), 9017–9028.
36. Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda (pp. 23–57). University of Chicago Press.