AI in IT Support Roles: Leveraging Large Language Models in IT Support.
Apanishile, Johnson (2025)
2025
Master's Programme in Computing Sciences and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2025-06-06
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202506066889
https://urn.fi/URN:NBN:fi:tuni-202506066889
Tiivistelmä
The use of AI across various human endeavor is on the rising, as it augment task processes, problems, bugs and more importantly increase efficiency. However, many resentment especially in tech world with the fear of AI replacing people. Hence, the study examined the use of AI in IT support roles to determine the implication of AI adoption in IT support roles. The study examined this through the three objectives; Identifying practical use cases by analysing common IT support tasks and determining where LLMs could have a significant impact based on current advancements in the technology; evaluating the performance of these models, focusing on aspects such as response time, resolution accuracy, and user satisfaction; highlighting potential challenges (such as data privacy and reliability) and limitations (including risks associated with managing high-priority issues).
The study used structured interview and interviewed key managers in IT department. The result revealed that; Key findings indicated that IT support tasks like addressing IT supports complaints and requests which was previous sent through emails or calling in or ticket systems that waste too much time were identified as one of the places AI can be used to aid process.
Evaluating the performance of these models, focusing on aspects such as response time, resolution accuracy, and user satisfaction. The study further revealed that the models were built by through the analyses of historical records of events and processes in the organisations, current knowledge and previous ticket information. So AI was able to provide accurate answers at a very fast time. Lastly, the study revealed that introducing LLMs into customer service operations lessened IT teams’ duties, made answers more accurate and pleased users. Challenges encountered with the use of AI that made users unhappy were the mix of ticket categories, which caused problems in sending tickets to the right team and delayed fixes. Other challenges include: data privacy and reliability, which made the technology to not be introduced in certain part of IT roles. It then revealed that AI in IT support roles can help in augmenting processes and in the future times may take over less technical duties and save time.
The study used structured interview and interviewed key managers in IT department. The result revealed that; Key findings indicated that IT support tasks like addressing IT supports complaints and requests which was previous sent through emails or calling in or ticket systems that waste too much time were identified as one of the places AI can be used to aid process.
Evaluating the performance of these models, focusing on aspects such as response time, resolution accuracy, and user satisfaction. The study further revealed that the models were built by through the analyses of historical records of events and processes in the organisations, current knowledge and previous ticket information. So AI was able to provide accurate answers at a very fast time. Lastly, the study revealed that introducing LLMs into customer service operations lessened IT teams’ duties, made answers more accurate and pleased users. Challenges encountered with the use of AI that made users unhappy were the mix of ticket categories, which caused problems in sending tickets to the right team and delayed fixes. Other challenges include: data privacy and reliability, which made the technology to not be introduced in certain part of IT roles. It then revealed that AI in IT support roles can help in augmenting processes and in the future times may take over less technical duties and save time.