Adaptive Knowledge Systems for IT Ops
Keywords:
Cross-border e-commerce data governance; cloud computing; data localization and residency; data sovereignty; service-oriented architecture; scalable architectures; third-party risk management; trusted identity management; unified-consent framework.Abstract
Artificial Intelligence (AI) is increasingly being integrated into Knowledge Management (KM) systems to provide enhanced decision-making support to Information Technology (IT) operations teams. AI-driven systems are empowering these teams to reduce service resolution times, streamline troubleshooting processes, and share knowledge more effectively by improving the speed, relevance, and contextual accuracy of knowledge retrieval and understanding. Alongside these advantages, such AI-powered capabilities are redesigning the nature of knowledge work, augmenting the collaborative aspect of KM efforts, and shifting the composition and skill requirements of IT operations teams.
Fundamental considerations for developing AI-driven KM systems for IT operations are explored, including a discussion of deployed architectural patterns, data quality and governance concerns, and deployment and processing options. Further, the impact of these systems and capabilities on the KM aspect of IT operations teams is examined. Together, these insights provide a foundation for the adoption of AI capabilities within KM systems used by IT operations teams.
References
[1] Alvarado, J., & Ortega, R. (2023). AI-augmented knowledge management systems: Design challenges and strategic impacts. Journal of Knowledge Management, 27(4), 889–905.
[2] Anderson, J., & Martinez, L. (2023). Predictive accuracy in AI-driven knowledge management: Bridging the implementation gap. Journal of Knowledge Management, 27(6), 1120–1142.
[3] Beheshti, A., Schiliro, F., Ghodratnama, S., Amouzgar, F., Benatallah, B., Terrizzano, I., ... & Yang, J. (2023). ProcessGPT: Transforming business process management with generative AI. IEEE Access, 11, 115234–115251.
[4] Borgia, F., Giannakis, M., & Richey, R. G. (2023). Strategic frameworks for AI-driven knowledge transfer in complex organizational structures. International Journal of Production Research, 61(12), 4015–4038.
[5] Chen, Y., Lin, H., & Lin, C. (2023). Deep reinforcement learning for dynamic knowledge discovery in IT distribution networks. Expert Systems with Applications, 215, 119283.
[6] Ferreira, J. J. M., Fernandes, C. I., & Veiga, P. M. (2023). The effects of knowledge spillovers, digital capabilities, and innovation on firm performance: A moderated mediation model. Technological Forecasting and Social Change, 200, 123086.
[7] Georgiev, G., & Antonova, A. Advancing knowledge management systems through the integration of AI, big data, and cloud computing. Journal of Enterprise Information Management, 37(1), 45–68.
[8] Grabner, M., Wang, Y., Wen, Q., Blažič, B., & Štruc, V. (2023). A global modeling framework for load forecasting in distribution networks. IEEE Transactions on Smart Grid, 14(6), 4927–4941. https://doi.org/10.1109/tsg.2023.3264525
[9] Gupta, M., Akter, S., & Wamba, S. F. (2023). Technology-driven to value-driven transformation: The role of AI in organizational resilience. Journal of Business Research, 167, 114147.
[10] He, X., & Liu, Y. (2023). Knowledge evolutionary process of artificial intelligence in e-commerce: Main path analysis and science mapping analysis. Expert Systems with Applications, 238, 121801. https://doi.org/10.1016/j.eswa.2023.121801
[11] Idrees, H., Xu, J., Haider, S. A., & Tehseen, S. (2023). A systematic review of knowledge management and new product development projects: Trends, issues, and challenges. Journal of Innovation & Knowledge, 8(2), 100350. https://doi.org/10.1016/j.jik.2023.100350
[12] Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145.
[13] Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87–99. https://doi.org/10.1016/j.bushor.2022.03.002
[14] Kudryavtsev, D., Khan, U., & Kauttonen, J. Transforming knowledge management using generative AI: From theory to practice. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 362–370. https://doi.org/10.5220/0013071400003838
[15] Kumar, S., & Zhang, H. (2023). The convergence of digital learning and AI: A strategic framework for enterprise operations. Computers in Human Behavior, 141, 107675.
[16] Maroufkhani, P., Wagner, R., & Wan Ismail, W. K. (2022). Artificial intelligence in knowledge management: A review. Journal of Innovation & Knowledge, 7(1), 100135.
[17] Mithas, S., Chen, Z. L., & Khuntia, J. (2022). AI-driven insights for operational excellence: Evidence from large-scale IT operations. MIS Quarterly, 46(2), 895–918.
[18] Nagy, J., Oláh, J., & Fogarassy, C. (2023). Challenges of change management in the integration of AI within enterprise infrastructures. Sustainability, 15(4), 3125.
[19] Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 1–35.
[20] Zhu, L., & Vuppalapati, C. (2023). AI-powered automation in IT service management: Impact on operational efficacy and cost reduction. Journal of Information Technology Management, 34(2), 112–134.
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