Citation :

Amit Mangal, 2023. "AI-Driven Enhancements in it Incident Management: Improving Customer Experience through Automation and Streamlined Processes" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 2: 62-72.

Abstract :

Artificial Intelligence (AI) has introduced significant advancements in operational efficiency across various domains, including IT Service Management. A crucial aspect of this field is the resolution process, which involves addressing issue tickets that represent interactions between IT agents and problem holders. Traditionally, these tickets are manually categorized to facilitate continuous improvement and proper escalation within the support team. AI now has the capability to classify tickets based on the initial issue descriptions, thereby eliminating the need for manual classification and enhancing operational efficiency. This case study explores methodologies for enhancing AI classification performance in IT incident management. Using a data set provided by an IT service provider in the nautical tourism sector, various enhancement techniques were applied to measure their impact on AI classification. AI models were developed, tested, and trained on both the original and enhanced data sets. The research findings indicate that improvements in AI performance can be achieved through systematic changes, such as better semantic categorization. These enhancements demonstrate that AI-driven approaches can significantly improve the efficiency and effectiveness of IT incident management. Ultimately, this leads to an improved customer experience through automation and streamlined processes.

References :

[1] Agarwal, S., Aggarwal, V., Akula, A. R., Dasgupta, G. B., &Sridhara, G. (2017). Automatic problem extraction and analysis from unstructured text in IT tickets. IBM Journal of Research and Development, 61(1), 4:41–4:52. https://doi.org/10.1147/jrd.2016.2629318

[2] Baresi, L., Quattrocchi, G., Tamburri, D. A., & Van Den Heuvel, W. (2020). Automated Quality Assessment of Incident Tickets for Smart Service Continuity. In Lecture notes in computer science (pp. 492–499). https://doi.org/10.1007/978-3-030-65310-1_35

[3] Batini, C., Barone, D., Mastrella, M., Maurino, A., &Ruffini, C. (2007). A framework and a methodology for data quality assessment and monitoring. In ICIQ (pp. 333–346).

[4] Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(0), 2. https://doi.org/10.5334/dsj-2015-002

[5] Conger, S., Winniford, M., & Erickson-Harris, L. (2008, August 14-17). Service management in operations. Paper presented at the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada.

[6] Galup, S. D., Dattero, R., Quan, J. J., &Conger, S. (2009). An overview of IT service management. Communications of the ACM, 52(5).

[7] Heinrich, B., Hopf, M., Lohninger, D., Schiller, A., &Szubartowicz, M. (2019). Data quality in recommender systems: The impact of completeness of item content data on prediction accuracy of recommender systems. Electronic Markets, 31(2), 389–409. https://doi.org/10.1007/s12525-019-00366-7

[8] International Organization for Standardization. (2005). ISO/IEC 20000-1:2005 Information technology - Service management - Part 1: Service management system requirements. ISO.

[9] Koehler, J., et al. (n.d.). Towards Intelligent Process Support for Customer Service Desks: Extracting Descriptions from Noisy and Multi-lingual Texts.

[10] Marcuzzo, M., Zangari, A., Schiavinato, M., Giudice, L., Gasparetto, A., &Albarelli, A. (2022). A multi-level approach for hierarchical Ticket Classification. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022) (pp. 201–214).

[11] Reinhard, P., Li, M. M., Dickhaut, E., Peters, C., &Leimeister, J. M. (2023). Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality Enrichment. In Lecture notes in computer science (pp. 279–293). https://doi.org/10.1007/978-3-031-32808-4_18

[12] Revina, A., Buza, K., & Meister, V. G. (2020). IT ticket classification: The simpler, the better. IEEE Access, 8, 193380–193395. https://doi.org/10.1109/access.2020.3032840

[13] Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers.Journal of Management Information Systems, 12(4), 5–33.

[14] Zicari, P., Folino, G., Guarascio, M., &Pontieri, L. (2021). Discovering accurate deep learning based predictive models for automatic customer support ticket classification. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (pp. xx-xx). ACM. https://doi.org/10.1145/nnnnnnn.xxxxxxx

[15] Zschech, P. (2022). Beyond descriptive taxonomies in data analytics: A systematic evaluation approach for data-driven method pipelines. Information Systems and e-Business Management. Advance online publication. https://doi.org/10.1007/s10257-022-00577-0

[16] Amit Mangal, 2021. "Evaluating Planning Strategies for prioritizing the most viable Projects to Maximize Investment Returns" ESP Journal of Engineering & Technology Advancements 1(2): 69-77. [PDF]

[17] Amit Mangal, 2022. "Envisioning the Future of Professional Services: ERP, AI, and Project Management in the Age of Digital Disruption" ESP Journal of Engineering & Technology Advancements 2(4): 71-79. [PDF]

[18] Amit Mangal, 2023. An Analytical Review of Contemporary AI-Driven Hiring Strategies in Professional Services, ESP Journal of Engineering & Technology Advancements 3(3): 52-63. [PDF]

Keywords :

Artificial Intelligence, Incident Management, Automation, Operational Efficiency, IT Service Management, Customer Experience, AI Performance, Streamlined Processes, Continuous Improvement, Service Optimization, Customer Satisfaction.