Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Artificial Intelligence and Machine Learning in Telecommunications Revolutionizing Customer Experience and Enhancing Service Delivery

Document Type : Original Article

Authors
Yaser Issam Hamodi Aljanabi 1
Mohammed Fadhil Mahdi 2
Omorova Aizhan Nurlanovna 3
Saif Kamil Shnain 4
Intesar Abbas 5
1 Al-Turath University, Baghdad 10013, Iraq,
2 Al-Mansour University College, Baghdad 10067, Iraq
3 Osh State University, Osh City 723500, Kyrgyzstan
4 Al-Rafidain University College Baghdad 10064, Iraq
5 Madenat Alelem University College, Baghdad 10006, Iraq
10.22034/jipm.2025.728199
Abstract
ABSTRACT
Background: The telecommunications industry is at the crossroad of change seemingly precipitated by the use of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have yielded new features like network automation, prescriptive analytics, and contextual-consumer engagement, solving traditional dilemmas in service delivery and operationalization.
Objective: The current article seeks to understand how AI and ML has positively affected customer experience and service provision in the telecommunication industry. The research objectives focus on how to increase KPIs to service latencies, network reliability, and customer retention while at the same time establishing the problems associated with big data large-scale implementation.
Methods: Samples were gathered using systematic reviews of the current literature, meta-analysis of case studies, and assessment of industry datasets. This concerned artificial intelligence enabled operations such as dynamic resource management, real-time customer emotions analysis and real-time fault detection. Regression analysis and time series models were used in order for measuring performance indices.
Results: AI and ML integration led to multifaceted advancements: a decrease of average service latency by 55%, reduction of network downtime by 70%, and an increase of maintenance predictions accuracy by 35%. The customer retention rate which had improved to 25% was also credited to better personalization of the services as well as having proper service management. AI-equipped resource allocation also raised efficiency in bandwidth utilization by 60%.
Conclusion: AI and ML are positively disrupting telecommunications as they deliver remarkable enhancements in the caliber of services and client satisfaction. With all the challenges in data governance and interoperability, it is clear that their adoption promises a great chance in enhancing the current standards within the telecommunications field and creating the basis for the development of a more sophisticated environment.
Keywords
KEYWORDS: Artificial Intelligence (AI)
Machine Learning (ML)
Telecommunications
Customer Experience (CX)
Service Delivery
Network Optimization
Predictive Analytics
Resource Allocation
Bandwidth Utilization
Predictive Maintenance

References

Abu Daqar, M. A. M., and Smoudy, A. K. A. (2019). The Role of Artificial Intelligence on Enhancing Customer Experience.  International Review of Management and Marketing, 9 (4), 22-31. https://doi.org/10.32479/irmm.8166
Acharya, R., Aleiner, I., Allen, R., Andersen, T. I., Ansmann, M., Arute, F., Arya, K., et al. (2023). Suppressing quantum errors by scaling a surface code logical qubit.  Nature, 614 (7949), 676-681. https://doi.org/10.1038/s41586-022-05434-1
Ageev, D. V., Al-Ansari, A., Qasim, N. H. (2015). Optimization model for multi-period LTE RAN and services planning with operator profit maximization.  Information Processing Systems, (3), 88-91. http://www.irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe?C21COM=2&I21DBN=UJRN&P21DBN=UJRN&IMAGE_FILE_DOWNLOAD=1&Image_file_name=PDF/soi_2015_3_20.pdf
Ageyev, D., Yarkin, D., and Nameer, Q. (2014). Traffic aggregation and EPS network planning problem. 2014 First International Scientific-Practical Conference Problems of Infocommunications Science and Technology, 14-17 Oct. 2014. https://doi.org/10.1109/INFOCOMMST.2014.6992316.
Aktar, M. (2023). Analyzing the Use of Artificial Intelligence and Machine Learning in Customer Support Systems.  PMIS Review, 2 (1), p.97-120. https://doi.org/10.56567/pmis.v2i1.12
Alnuaemy, L. M. (2023). Peculiarities of using neuro-linguistic programming for the rehabilitation of servicemen who were in armed conflicts.  Development of Transport Management and Management Methods, 3 (84), 40-55. https://doi.org/10.31375/2226-1915-2023-3-40-55
Ameen, N., Tarhini, A., Reppel, A., and Anand, A. (2021). Customer experiences in the age of artificial intelligence.  Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
Anozie, U., Onyenahazi, O., Adekola, A., Ukadike, C., Ukadike, and Oloko, O. (2024). Advancements in artificial intelligence for omnichannel marketing and customer service: Enhancing predictive analytics, automation, and operational efficiency.  International Journal of Science and Research Archive, 12, 1621-1629. https://doi.org/10.30574/ijsra.2024.12.2.1436
Arora, S., & Thota, S. (2024). Using Artificial Intelligence with Big Data Analytics for Targeted Marketing Campaigns.  International Journal of Advanced Research in Science, Communication and Technology, 4 (3), 593-602. https://doi.org/10.48175/ijarsct-18967
Atov, I., Chen, K. C., and Yu, S. (2019). Data Science and Artificial Intelligence for Communications.  IEEE Communications Magazine, 57 (5), 56-56. https://doi.org/10.1109/MCOM.2019.8713799
Balmer, R. E., Levin, S. L., and Schmidt, S. (2020). Artificial Intelligence Applications in Telecommunications and other network industries.  Telecommunications Policy, 44 (6), 101977. https://doi.org/10.1016/j.telpol.2020.101977
Busayo, T., Igbekoyi, O., Oluwagbade, O., Adewara, Y., Dagunduro, M., & Boluwaji, Y. (2023). Artificial Intelligence and Service Quality of Telecommunication Firms in Nigeria.  Journal of Economics, Finance and Accounting Studies 5(3), 203-214. https://doi.org/10.32996/jefas.2023.5.3.16
Chen, Y., and Prentice, C. (2024). Integrating Artificial Intelligence and Customer Experience.  Australasian Marketing Journal, 14413582241252904. https://doi.org/10.1177/14413582241252904
Echegu, D. A. (2024). Artificial Intelligence (AI) in Customer Service: Revolutionising Support and Engagement.  IAA Journal of Scientific Research, 11 (2), 33-39. https://doi.org/10.59298/IAAJSR/2024/112.3339
Ghosh, S., Ness, S., & Salunkhe, S. . (2024). The Role of AI Enabled Chatbots in Omnichannel Customer Service.  Journal of Engineering Research and Reports, 26 (6), 327-345. https://doi.org/10.9734/jerr/2024/v26i61184
Hollebeek, L. D., Sprott, D. E., and Brady, M. K. (2021). Rise of the Machines? Customer Engagement in Automated Service Interactions.  Journal of Service Research, 24 (1), 3-8. https://doi.org/10.1177/1094670520975110
Ibrahim Adedeji, A., Christianah Pelumi, E., Olajide Soji, O., and Angela Omozele, A. (2024). Implementing machine learning techniques for customer retention and churn prediction in telecommunications.  Computer Science & IT Research Journal, 5 (8), 2011-2025. https://doi.org/10.51594/csitrj.v5i8.1489
Kaur, H. (2024). Artificial Intelligence and Commerce: Revolutionizing Marketing, Sales, and Customer Experience.  Innovative Research Thoughts, 10 (2), 51-56. https://doi.org/10.36676/irt.v10.i2.07
Kushwaha, G., Yadav, U. K., Kumar, U., Singh, V. K., and Singh, V. K. (2024). Customer Service Automation Using Machine learning.  International Journal of Innovative Research in Advanced Engineering, 11 (02), 68-72. https://doi.org/10.26562/ijirae.2024.v1102.01
Lutfiyya, H., Birke, R., Casale, G., Dhamdhere, A., Hwang, J., Inoue, T., Kumar, N., et al. (2021). Guest Editorial: Special Section on Embracing Artificial Intelligence for Network and Service Management.  IEEE Transactions on Network and Service Management, 18 (4), 3936-3941. https://doi.org/10.1109/TNSM.2021.3127543
Mushtaq, A.-S., Ali Ihsan, A.-A., and Qasim, N. (2015). 2D-DWT vs. FFT OFDM Systems in fading AWGN channels.  Radioelectronics and Communications Systems, 58 (5), 228-233. https://doi.org/10.3103/S0735272715050052
Nameer, Q., Ali, A.-A., and Moath, T. R. S. (2015). Modeling of LTE EPS with self-similar traffic for network performance analysis.  Information processing systems, (12), 140-144. https://doi.org/10.1109/INFOCOMMST.2015.7357335
Nwokedi, C., & Nwafor, C. (2024). Enhancing customer service and user experience through the use of machine learning powered intelligent chatbots.  World Journal of Advanced Research and Reviews, 23 (02), 181–191. https://doi.org/10.30574/wjarr.2024.23.2.2307
Obiki-Osafiele, A., Agu, E., & Chiekezie, N. (2024). Leveraging artificial intelligence to enhance customer service analytics and improve service delivery.  International Journal of Management & Entrepreneurship Research, 6 (8). https://doi.org/10.51594/ijmer.v6i8.1412
Ochuba, N. A., Okafor, E. S., Akinrinola, O., Amoo, O. O., and Usman, F. O. (2024). ENHANCING CUSTOMER SERVICE IN SATELLITE TELECOMMUNICATIONS: A REVIEW OF DATA-DRIVEN INSIGHTS AND METHODOLOGIES FOR PERSONALIZED SERVICE OFFERINGS.  International Journal of Management & Entrepreneurship Research, 6 (3), 582-593. https://doi.org/10.51594/ijmer.v6i3.846
Qasim, N., Khlaponin, Y., & Vlasenko, M. (2022). Formalization of the Process of Managing the Transmission of Traffic Flows on a Fragment of the LTE network.  Collection of Scientific Papers of the Military Institute of Taras Shevchenko National University of Kyiv, 75, 88–93. https://doi.org/10.17721/2519-481X/2022/75-09
Qasim, N. H., Salman, A. J., Salman, H. M., AbdelRahman, A. A., and Kondakova, A. (2024). Evaluating NB-IoT within LTE Networks for Enhanced IoT Connectivity.  2024 35th Conference of Open Innovations Association (FRUCT), 552-559. https://doi.org/10.23919/FRUCT61870.2024.10516400
Qasim, N. H., Vyshniakov, V., Khlaponin, Y., and Poltorak, V. (2021). Concept in information security technologies development in e-voting systems.  International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 3 (9), 40-54. https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_9_september_2021/15985/final/fin_irjmets1630649545.pdf
Rani, Y. A., Balaram, A., Sirisha, M. R., Nabi, S. A., Renuka, P., and Kiran, A. (2024). AI Enhanced Customer Service Chatbot. 2024 International Conference on Science Technology Engineering and Management (ICSTEM), 26-27 April 2024. https://doi.org/10.1109/ICSTEM61137.2024.10561155.
Reddy, D. (2024). How do customers react to the integration of AI in customer experience.  International Journal For Multidisciplinary Research, 6 (5). https://doi.org/10.36948/ijfmr.2024.v06i05.28954
Sieliukov, A., Qasim, N. H., and Khlaponin, Y. (2022). Conceptual Model of the Mobile Communication Network. TTSIIT- The 1st International Conference on Emerging Technology Trends on the Smart Industry and the Internet of Things, January 19th –
20th 2022. https://www.knuba.edu.ua/wp-content/uploads/2022/11/%D0%97%D0%B1%D1%96%D1%80%D0%BD%D0%B8%D0%BA_Optimized.pdf#page=20https://www.knuba.edu.ua/wp-content/uploads/2022/11/%D0%97%D0%B1%D1%96%D1%80%D0%BD%D0%B8%D0%BA_Optimized.pdf#page=20.
Xavier, K. (2023). A BEHAVIOURAL STUDY ON THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CUSTOMER SERVICES RETENTION IN TELECOM INDUSTRY.  Russian Law Journal, 11. https://doi.org/10.52783/rlj.v11i5s.960

 

 

  • Receive Date 06 August 2025