A Scalable Human Centered Artificial Intelligence Architecture for Decision Support Systems in Large Scale Digital Transformation Ecosystems
DOI:
https://doi.org/10.62951/ijcts.v1i1.360Keywords:
Artificial Intelligence, Decision Support Systems, Human-Centered Artificial Intelligence, Scalability, Trustworthy AIAbstract
Artificial Intelligence (AI)-based Decision Support Systems (DSS) have become a central component of digital transformation initiatives across various industries. While prior studies have primarily emphasized technical aspects such as accuracy, performance, and computational efficiency, less attention has been given to the integration of human-centered principles and scalable architectural design. This study aims to examine how AI-based DSS can be enhanced through the combined application of Human-Centered Artificial Intelligence (HCAI) principles and scalable AI architecture. Using a qualitative, literature-based research methodology, this study systematically analyzes peer-reviewed publications indexed in Scopus to identify key dimensions influencing the effectiveness and sustainability of AI-driven DSS. The findings indicate that technical capabilities alone are insufficient to ensure successful adoption and long term impact. Instead, transparency, explainability, ethical governance, and user empowerment core elements of HCAI are critical for fostering trust and user acceptance. Furthermore, scalable architectural principles, including modularity, interoperability, and adaptability, are essential for enabling AI-based DSS to operate reliably in large-scale and dynamic environments. This study contributes a unified conceptual framework that bridges technical scalability and human-centered design, offering theoretical insights and practical guidance for developing trustworthy, scalable, and sustainable AI-based Decision Support Systems in digital transformation contexts.
References
Ahmed, Q., Sumbal, M. S., & Lee, C. (2025). AI-driven knowledge management in megaprojects: The leadership factor. Proceedings of the European Conference on Knowledge Management, 2, 1163–1168.
Atf, Z. (2025). Is trust correlated with explainability in AI? A meta-analysis. IEEE Transactions on Technology and Society. https://doi.org/10.1109/TTS.2025.3558448
Benois-Pineau, J., & Petkovic, D. (2023). Introduction. In Explainable deep learning AI: Methods and challenges (pp. 1–6). https://doi.org/10.1016/B978-0-32-396098-4.00007-7
Bertl, M., Ross, P., & Draheim, D. (2023). Systematic AI support for decision-making in the healthcare sector: Obstacles and success factors. Health Policy and Technology, 12(3), 100748. https://doi.org/10.1016/j.hlpt.2023.100748
Bhardwaj, A., & Choudhary, S. K. (2024). AI based decision support system for cyber forensics investigations. Proceedings of the 2024 IEEE 4th International Conference on ICT in Business Industry and Government (ICTBIG 2024). https://doi.org/10.1109/ICTBIG64922.2024.10911602
Biggar, O., Zamani, M., & Shames, I. (2022). On modularity in reactive control architectures, with an application to formal verification. ACM Transactions on Cyber-Physical Systems, 6(2). https://doi.org/10.1145/3511606
Daoud, M., Ennouni, A., & Sabri, M. A. (2025). The strategic role of AI in business digital transformation: Opportunities and challenges. Proceedings of the 2025 International Conference on Circuit, Systems and Communication (ICCSC 2025). https://doi.org/10.1109/ICCSC66714.2025.11134890
Donati, D., Inverardi, P., Melis, B., & Pelliccione, P. (2025). Beyond the checklist: Rethinking trustworthiness in AI system. Proceedings of the 2025 IEEE 33rd International Requirements Engineering Conference Workshops (REW 2025), 468–474. https://doi.org/10.1109/REW66121.2025.00071
Duan, J. (2025). Application and optimization of industrial internet and big data analytics in enterprise decision-making. Engineering Proceedings, 103(1), 27. https://doi.org/10.3390/engproc2025103027
Freeda, A., Kanthavel, R., & Anju, A. (2025). Scalability issues in AI computing in large-scale networks. In AI for large scale communication networks (pp. 395–413). https://doi.org/10.4018/979-8-3693-6552-6.ch0018
Gillingham, P. (2019). Can predictive algorithms assist decision-making in social work with children and families? Child Abuse Review, 28(2), 114–126. https://doi.org/10.1002/car.2547
Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262. https://doi.org/10.1111/puar.13483
Hameed, S., Karagoz, S., Ozdemir, A., Khosravi, A., Fatima, R., Zulqarnain, & Aitkaliyeva, G. (2025). Real-time decision support systems in chemical and process engineering. In Artificial intelligence in chemical engineering (pp. 313–348). https://doi.org/10.1016/B978-0-443-34076-5.00021-3
Hepworth, A. J., Baxter, D. P., Hussein, A., Yaxley, K. J., Debie, E., & Abbass, H. A. (2021). Human-swarm-teaming transparency and trust architecture. IEEE/CAA Journal of Automatica Sinica, 8(7), 1281–1295. https://doi.org/10.1109/JAS.2020.1003545
Kanchibhotla, C., Kota, K. T., Srinivas, P., Channappa, S., Kumar, C. K., & Katta, S. K. (2024). Innovations in AI and deep learning for scalable network data processing. Proceedings of the Intelligent Computing and Emerging Communication Technologies (ICEC 2024). https://doi.org/10.1109/ICEC59683.2024.10837414
Khanna, A., & Bhusri, A. (2025). Enabling interoperable AI in IoT: A unified data framework approach. Proceedings of the IEEE 6th Annual World AI IoT Congress (AIIoT 2025), 32–34. https://doi.org/10.1109/AIIoT65859.2025.11105335
Koukoutsis, E., Papaodysseus, C., Tsavdaridis, G., Karadimas, N. V., Ballis, A., Mamatsi, E., & Mamatsis, A. R. (2020). Design limitations, errors and hazards in creating decision support platforms with large- and very large-scale data and program cores. Algorithms, 13(12), 341. https://doi.org/10.3390/a13120341
Krejcar, O., Abdullah, J., & Namazi, H. (2026). Implementing XAI in life sciences: Key challenges and pathways to solutions. Artificial Intelligence in the Life Sciences, 9, 100153. https://doi.org/10.1016/j.ailsci.2026.100153
Lausberg, C., & Krieger, P. (2021). Decision support systems in real estate: History, types and applications. In Decision Support Systems: Types, Advantages and Disadvantages (pp. 1–77).
Lu, X., Hu, N., & Zou, J. (2025). Artificial intelligence in decision support systems: Impact on organizational management theory. Proceedings of the 9th International Conference on Electronic Information Technology and Computer Engineering (EITCE 2025), 335–340. https://doi.org/10.1145/3766671.3766731
Maikantis, T., Tsintzira, A.-A., Ampatzoglou, A., Arvanitou, E.-M., Chatzigeorgiou, A., Stamelos, I., Bibi, S., & Deligiannis, I. (2020). Software architecture reconstruction via a genetic algorithm: Applying the move class refactoring. ACM International Conference Proceeding Series, 135–139. https://doi.org/10.1145/3437120.3437292
Maksoud, A., Al-Beer, H. B., Mushtaha, E., & Yahia, M. W. (2022). Self-learning buildings: Integrating artificial intelligence to create a building that can adapt to future challenges. IOP Conference Series: Earth and Environmental Science, 1019(1), 12047. https://doi.org/10.1088/1755-1315/1019/1/012047
Mallioris, P., Kokkas, G., Styliadis-Heinz, A., Margaritis, I., Stergiopoulos, F., & Bechtsis, D. (2024). Development of a decision support system in a canning industry. In Lecture Notes in Networks and Systems (Vol. 824, pp. 371–380). https://doi.org/10.1007/978-3-031-47715-7_25
Mantouzi, S., & Youssef, S. (2025). The role of AI-driven decision support systems (DSS) in enhancing organizational performance: A qualitative study of Morocco’s automotive industry. In Transparency in AI-Assisted Management Decisions (pp. 397–422). https://doi.org/10.4018/979-8-3373-1737-3.ch012
Mishra, A. (2024). Scalable AI and design patterns: Design, develop, and deploy scalable AI solutions. Springer. https://doi.org/10.1007/979-8-8688-0158-7
Nilsson, J., Javed, S., Albertsson, K., Delsing, J., Liwicki, M., & Sandin, F. (2024). AI concepts for system of systems dynamic interoperability. Sensors, 24(9), 2921. https://doi.org/10.3390/s24092921
Palanivel Rajan, S., & Abirami, T. (2023). AI role in making IoT-based medical devices a success. In Internet of things in biomedical sciences: Challenges and applications (pp. 3–19). https://doi.org/10.1088/978-0-7503-5311-3ch3
Patjoshi, P. K., Khan, B. M. A., Manjunatha, S., Inumula, K. M., Sharma, K., & Mittal, P. (2025). Digital transformation in management: Leveraging emerging technologies for enhanced business financial operations. Proceedings of the 6th International Conference for Emerging Technology (INCET 2025). https://doi.org/10.1109/INCET64471.2025.11140452
Patolia, D. (2025). The role of AI in next-generation BI innovations in data processing and visualization for food chain management. In Modernizing the food industry: AI-powered infrastructure, security, and supply chain innovation (pp. 233–256). https://doi.org/10.4018/979-8-3373-5288-6.ch011
Rakshitha, Priya, S., Namjoshi, A., Mane, M., Mohammed, I. A., & Shettigar, R. (2025). Data analytics for decision support systems in techno-driven enterprises. Proceedings of the 1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT 2025), 1009–1012. https://doi.org/10.1109/CE2CT64011.2025.10941224
Rosário, A. T., & Dias, J. C. (2025). Illuminating industry evolution: Reframing artificial intelligence through transparent machine reasoning. Information (Switzerland), 16(12), 1044. https://doi.org/10.3390/info16121044
Sahoo, K., & Saurav, S. (2025). Designing AI systems with user empathy and inclusivity: Navigating bias and representation. In Studies in Computational Intelligence (Vol. 1185, pp. 1–11). https://doi.org/10.1007/978-3-031-87252-5_1
Salgado-Reyes, N., Nicolalde-Rodriguez, D., Meza, J., & Vaca-Cardenas, M. (2024). Artificial intelligence and its impact on digital transformation processes. In Smart Innovation, Systems and Technologies (Vol. 380, pp. 37–44). https://doi.org/10.1007/978-981-99-8894-5_4
Schmager, S., Pappas, I., & Vassilakopoulou, P. (2024). Citizens-focused design principles for human-centered AI in public services. Proceedings of the 30th Americas Conference on Information Systems (AMCIS 2024).
Usmani, U. A., Happonen, A., & Watada, J. (2023). Human-centered artificial intelligence: Designing for user empowerment and ethical considerations. Proceedings of the 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA 2023). https://doi.org/10.1109/HORA58378.2023.10156761
Vernadat, F. B. (2023). Interoperability and standards for automation. In Springer handbooks (pp. 729–752). https://doi.org/10.1007/978-3-030-96729-1_33
Wajdi, A. A., Sakly, H., Guetari, R., & Kraiem, N. (2025). Fundamental principles of AI scalability in healthcare. In Scalable artificial intelligence for healthcare: Advancing AI solutions for global health challenges (pp. 17–35). https://doi.org/10.1201/9781003480594-2
Zhang, Y., Chen, N., Zhang, Y., & Wu, W. (2025). Research on business decision support system based on big data and artificial intelligence. Proceedings of the 2025 International Conference on Data Science and Its Applications (ICoDSA 2025), 1285–1290. https://doi.org/10.1109/ICoDSA67155.2025.11157569
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computer Technology and Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
