AI Service Intelligence in Data Center Customer Experience
Keywords:
AI-Driven Service Intelligence, Customer Experience Optimization, Service Intelligence Indices, CX Maturity Models, AI-Based Personalization, Proactive Issue Resolution, Automated Service Provisioning, Data-Centric Service Models, AI in Customer Lifecycle, Service Intelligence Frameworks, NLP in Customer Support, Predictive Service Analytics, CX Transformation Strategy, Intelligent Service Platforms, Service Governance Models, AI Deployment Patterns, Scalable CX Systems, Telecom Service Analytics, AI-Enhanced Customer Support, Experience-Driven Services.Abstract
Artificial Intelligence (AI) is driving a new era of service intelligence, integrating analytics, Machine Learning, and Natural Language Processing with diverse data sources to elevate the customer experience across the entire service life cycle. By enabling data-centric approaches that increasingly replace human-centric ones, AI supports capabilities ranging from large-scale personalization and proactive issue detection and resolution to automated recall provisioning. Realizing these opportunities depends on two critical enablers. The first is the development of service intelligence indices—maturity models that aggregate performance objectives across trusted data and AI platforms, provisioning and support toolsets, and process governance. The second is the adoption of suitable deployment patterns that guide practitioners in implementing service intelligence through incremental, actionable stages.
Motivated by the growing strategic importance of customer experience, this work examines how AI-driven service intelligence can enhance customer experience and proposes a structured approach to guide its practical deployment. Preliminary insights from a large-scale telecommunications use case illustrate the approach in practice.
References
1. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548.
2. Perez-Vega, R., Kaartemo, V., Lages, C. R., Razavi, N. B., & Männistö, J. (2021). Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. Journal of Business Research, 129, 902–910.
3. Pizzi, G., Scarpi, D., & Pantano, E. (2021). Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot? Journal of Business Research, 129, 878–890.
4. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
5. Chen, J.-S. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512–1531.
6. Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 23–34.
7. Følstad, A., & Taylor, C. (2021). Investigating the user experience of customer service chatbot interaction: A framework for qualitative analysis of chatbot dialogues. Quality and User Experience, 6, Article 6.
8. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
9. Huang, G., & Liang, H. (2021). Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach. Journal of Business Research, 126, 1–11.
10. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
11. Zhang, S., Sun, L., & others. (2021). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from big data analytics. Industrial Marketing Management, 98, 207–221.
12. Liang, B., Wu, D., Wu, P., & Su, Y. (2021). An energy-aware resource deployment algorithm for cloud data centers based on dynamic hybrid machine learning. Knowledge-Based Systems, 222, 107020.
13. Loganathan, R., & Kolla, S. H. (2024). Harnessing generative AI for adaptive risk policy generation in multi-regulatory data center environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 505-515.
14. Gan, Y., Liang, M., Dev, S., Lo, D., & Delimitrou, C. (2021). Sage: Practical & scalable ML-driven performance debugging in microservices. In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems.
15. Yang, Z., Du, J.-H., Lin, Y., Du, Z., & others. (2022). Increasing the energy efficiency of a data center based on machine learning. Journal of Industrial Ecology, 26(1), 281–294.
16. Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.
17. Park, B. R., Choi, Y. J., Choi, E. J., & Moon, J. W. (2022). Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system. Journal of Building Engineering, 50, 104167.
18. Ahmed, S., Singh, M., Doherty, B., Ramlan, E., Harkin, K., & Coyle, D. (2022). AI for information technology operation (AIOps): A review of IT incident risk prediction. In 2022 9th International Conference on Soft Computing and Machine Intelligence (ISCMI) (pp. 253–257).
19. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
20. Ahmed, S., Singh, M., Doherty, B., Ramlan, E. I., Harkin, K., & Coyle, D. (2022). Multiple severity-level classifications for IT incident risk prediction. In 2022 9th International Conference on Soft Computing and Machine Intelligence (ISCMI).
21. Aw, E. C.-X., Tan, G. W.-H., Cham, T.-H., Raman, R., & Ooi, K.-B. (2022). Alexa, what's on my shopping list? Transforming customer experience with digital voice assistants. Technological Forecasting and Social Change, 180, 121711.
22. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
23. Ruan, Y., & Mezei, J. (2022). When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type. Journal of Retailing and Consumer Services, 68, 103059.
24. Theodoridis, P., et al. (2022). The role of chatbots in enhancing customer experience: Literature review. Procedia Computer Science, 203, 432–437.
25. Solanke, A. A. (2022). AIOps evolution: From reactive monitoring to predictive service management in hybrid cloud environments. Frontiers in Computer Science and Information Technology, 3(1), 37–59.
26. Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.
27. Ikram, A., Chakraborty, S., Mitra, S., Saini, S., Bagchi, S., & Kocaoglu, M. (2022). Root cause analysis of failures in microservices through causal discovery. Advances in Neural Information Processing Systems, 35.
28. Gao, W., Fan, H., Li, W., & Wang, H. (2022). Crafting the customer experience in omnichannel contexts: The role of channel integration. Journal of Business Research, 126, 12–22.
29. Mahadevan, S. (2024). Clouding the Future: Innovating Towards Net-Zero Emissions. International Journal of Computing and Engineering, 6(2), 17-23.
30. Xie, Y., Zhang, H., & Babar, M. A. (2023). LogSD: Detecting anomalies from system logs through self-supervised learning and frequency-based masking. Proceedings of the ACM on Software Engineering, 1(FSE), 2098–2120.
31. Wang, R., Bansal, C., Ghosh, S., Zimmermann, T., Zhang, X., & Rajmohan, S. (2023). Recommending root-cause and mitigation steps for cloud incidents using large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE).
32. Ahmed, S., Singh, M., Doherty, B., Ramlan, E., Harkin, K., & Coyle, D. (2023). Knowledge-based intelligent system for IT incident DevOps. In 2023 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps).
33. Olujimi, P. A., & Ade-Ibijola, A. (2023). NLP techniques for automating responses to customer queries: A systematic review. Discover Artificial Intelligence, 3, Article 20.
34. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
35. Fan, H., Gao, W., & Han, B. (2023). Are AI chatbots a cure-all? The relative effectiveness of chatbot ambidexterity in crafting hedonic and cognitive smart experiences. Journal of Business Research, 156, 113526.
36. Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., & Lee, C. W. (2023). Influence of customer perception factors on AI-enabled customer experience in the Ecuadorian banking environment. Sustainability, 15(16), 12441.
37. Wang, Y., et al. (2023). Chatbots or me? Consumers’ switching between human agents and conversational agents. Journal of Retailing and Consumer Services, 72, 103264.
38. Peruchini, M., da Silva, G. M., & Teixeira, J. M. (2024). Between artificial intelligence and customer experience: A literature review on the intersection. Discover Artificial Intelligence, 4, Article 4.
39. Wang, P., Li, K., Du, Q., & Wang, J. (2024). Customer experience in AI-enabled products: Scale development and validation. Journal of Retailing and Consumer Services, 76, 103578.
40. Hollebeek, L. D., et al. (2024). Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research. Psychology & Marketing.
41. Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Computer Fraud and Security.
42. Hashemi, S., & Mäntylä, M. (2024). OneLog: Towards end-to-end software log anomaly detection. Automated Software Engineering, 31, Article 37.
43. Wei, X., Wang, J., Sun, C.-A., Towey, D., Zhang, S., Zuo, W., Yu, Y., & Ruan, R. (2024). Log-based anomaly detection for distributed systems: State of the art, industry experience, and open issues. Journal of Software: Evolution and Process, 36(8), e2650.
44. Xie, Y., Zhang, H., & Babar, M. A. (2024). LogSD: Detecting anomalies from system logs through self-supervised learning and frequency-based masking. Proceedings of the ACM on Software Engineering, 1(FSE), 2098–2120.
45. Khan, Z. A., Shin, D., Bianculli, D., & Briand, L. C. (2024). Impact of log parsing on deep learning-based anomaly detection. Empirical Software Engineering, 29, Article 139.
46. Pham, T.-A., & Lee, J.-H. (2024). Lightweight multi-task learning method for system log anomaly detection. IEEE Access, 12, 147739–147752.
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