MBIE/PGDBIE2403-8150-1
RENU-106481
Table of Contents
The Impact of Artificial Intelligence (AI) on IT Service Management (ITSM)
Introduction
In this paper, I will explore how Artificial Intelligence is changing the way companies manage their IT systems. As businesses even more and more rely on technology, having reliable IT services become even more important. AI offers more some exciting possibilities for IT services,
like automating tasks, making IT services better, and solving problems faster. But there are also some downsides and things to think about before jumping into AI for ITSM.(Mitra, R., & Tarnach, G., 2022).
Businesses carry out more and more of work online, so their networks and PCs need to be reliable as well as efficient. Businesses use IT service management, or ITSM for short, to ensure that their IT systems function smoothly and are capable of solving any issues that may arise. But with technology changing so quickly, it's growing more difficult for businesses to use traditional ITSM methods to remain current (Belk, R.,2021).
2. Literature Review Analysis
Article Title/ Authors Summary
1. Article title - Artificial intelligence: an overview of research trends and future directions
Authors --(Gursoy & Cai, 2024)
Summary
This article examines looks at how artificial intelligence (AI) is transforming the tourism sector and how it affects customer decision-making, service quality.
2. Machine Learning Based Help Desk System for IT Service Management
(Al-Hawari & Barham, 2021)
Summary
This article explores the use of a machine learning model to create an IT help desk system that is more efficient. The authors recommend an automated system for classifying incoming help desk tickets, which could allow more efficient resource allocation as well as faster response times.
3. Leveraging artificial intelligence in firm-generated online customer communities: a framework and future research agenda
Marti, C. L., Liu, H., Kour, G., Bilgihan, A., & Xu, Y. (2024).
Summary
This article examines how artificial intelligence (AI) could improve online consumer communities which companies develop. The authors provide a framework and some paths for further research on how AI might be used to enhance these communities—for instance, by promoting more in-depth client collaboration and customized communication.
4. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology
Summary
The potential of AI in gastroenterology and hepatology (gut and liver medicine) is examined in this article. It examines potential for using AI to plan treatments, detect illnesses, and provide customized treatment while also outlining the difficulties.
Christou, C. D., & Tsoulfas, G. (2021).
5. Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems Tao, X., Cheng, L., Zhang, R., Chan, W. K., Chao, H., & Qin, J. (2023)
In order to support environmentally friendly transportation systems, this study examines how traffic flow in smart cities can be anticipated by machine learning algorithms.
6. Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval Glinka, K., & Müller-Birn, C. (2023).
This study investigates how AI and art historians can collaborate together to find research picture more quickly and effectively. To fully understand the AI's information and make meaningful findings, still researchers need critical thinking skills.
7. Ethical issues in service robotics and artificial intelligence Belk, R. (2021).
The increasing popularity of machine learning and robotics in customer service raises ethical questions that are addressed in this article. It highlights five main areas where ethical questions are raised by these technologies.
8. Assessing the Return on Investment (ROI) Through Appreciative Inquiry (AI) of Hospital Improvement Programmes Patel, K., & Aylott, J. (2017).
The application of Appreciative Inquiry (AI), a strengths-based methodology, to assess hospital improvement projects' return on investment (ROI) is examined in this article. The authors propose that by highlighting positive experiences and highlighting areas in need of more growth, AI might be a useful tool for assessing program effectiveness.
9. Collaboration in the machine age: Trustworthy human-AI collaboration Razmerita, L., Brun, A., & Nabeth, T. (2022).
Discourse on Implementation
Across AI Lifecycle Stages
Singh, J. P. (2022).
patients and doctors alike.
2.2 Analysis of selected articles
Key Ideas/ Schools of Thought/problems
Studies have shown that AI can enhance IT service management's performance (Zhang et al., 2023). AI may classify support tickets and even identify problems in the future (AI-Hawari & Barham, 2021). Customers may receive faster solutions as a result, and overall service could get better.
Automation and Efficiency Increases: One of AI's primary focuses is its capacity to automate repetitive tasks, which boosts productivity in a variety of sectors. Not on the original list but still necessary, articles by AI-Hawari & Barham (2021) and Khan et al. (2022) address IT service desk ticketing, service request fulfillment, and basic troubleshooting as top candidates for automation in IT support.
Human-Centered AI (HCAI): When implementing AI into various procedures, this new school of thought places an extreme value on giving users' needs and understanding the highest priority. Singh (2022) provides a case study of this idea in connection with AI in healthcare. The design and application of AI is guaranteed by HCAI principles to benefit patients as well as medical professionals.
Christou & Tsoulfas (2021) talk on the potential of AI in healthcare, including topics like disease diagnosis, treatment planning, and patient customisation. But it's important to address moral problems with AI bias and responsible development.
Researchers like Glinka and Müller-Birm (2023) examined into how art historians may use algorithms to find images even faster and more effectively for their research in a recent study. Simply using AI is not enough, researchers need to study the results and secure they place with their access goals. This critical-examination approach is important for getting the most valuable awareness from AI in art history.
It raises a some important questions about the use of AI in the service sector, especially in the issue of privacy. He feels that companies may gather too much of user data and fail to share how it will be used. Consumers may believe that this respects their privacy and gives them little control over the information they provide (Belk, 2021).
Even though, AI is a great at automating tasks and finding patterns in data, there are still some things human do best. Humans are still the best at solving problems that need critical thought, innovation, or quick thinking. To achieve the best results, it's necessary to find a fair balance between the automation skills of AI and human expertise (Muller et al., 2022).
These studies provide important factors for the successful adoption of AI in a variety of fields. Singh (2022) emphasizes the importance of user-centered design, which is similar to human-centered AI. This means offering user needs important during design and implementation, ensuring AI system truly serve those who use them. Additionally, Belk (2021) emphasizes ethical considerations in the service industry, urging careful use of AI to avoid bias and data privacy issues. Responsible AI development is crucial to prevent discriminatory outcomes or manipulating customers. By prioritizing both user needs and ethical practices, we can ensure AI benefits everyone.
2.3 Relevance to the Topic
Understanding and using literature theories of AI tools
By using their idea of consumer-driven design, Offer ITSM related insights, according to their
research on digital voice assistants, user are more likely to accept advancements that are easy to use, meet their needs , and add value to their relationship. In the field ITSM, this means developing AI-driven solution that assist experts in their work, offer accurate guidance, and concentrate solving issues Fernandes & Oliveira (2021).
"According to belk's theory" IT service management is being transformed by AI. Artificial Intelligence is a strong new technology that may be used across a lot of ITSM areas, from incident analysis and expert allocate to issue forecast and reduction through machine learning. Even knowledge foundation are updated for quicker resolutions thanks to it.
Conclusion:
AI may reduce tasks for users, automate processes, and even improve our decision-making. Imagine a system that can detect problems and quickly resolve them before they occur. But there are a few things to remember. AI requires high-quality data in order to operate properly. It's also critical to apply AI ethically and responsibly. Lastly, we have to choose the most effective way for cooperation between humans and AI, with AI serving as a useful ally rather than a replacement.
References:
Gursoy, D., & Cai, R. (2024). Artificial intelligence: an overview of research trends and future directions. International Journal of Contemporary Hospitality Management.
Al-Hawari, F., & Barham, H. (2021). A machine learning based help desk system for IT service management. Journal of King Saud University-Computer and Information Sciences, 33(6), 702-718.
Miklosik, A., Evans, N., & Qureshi, A. M. A. (2021). The use of chatbots in digital business transformation: A systematic literature review. IEEE Access, 9, 106530-106539.
Marti, C. L., Liu, H., Kour, G., Bilgihan, A., & Xu, Y. (2024). Leveraging artificial intelligence in firm-generated online customer communities: a framework and future research agenda. Journal of Service Management.
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Christou, C. D., & Tsoulfas, G. (2021). Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World journal of gastroenterology, 27(37), 6191.
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Belk, R. (2021). Ethical issues in service robotics and artificial intelligence. The Service Industries Journal, 41(13-14), 860-876.
Patel, K., & Aylott, J. (2017). Assessing the Return on Investment (ROI) Through Appreciative Inquiry (AI) of Hospital Improvement Programmes. Why Hospitals Fail: Between Theory and Practice, 37-47.
Razmerita, L., Brun, A., & Nabeth, T. (2022). Collaboration in the machine age: Trustworthy human-AI collaboration. In Advances in Selected Artificial Intelligence Areas: World Outstanding Women in Artificial Intelligence (pp. 333-356). Cham: Springer International Publishing.
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Jaferian, P., Botta, D., Hawkey, K., & Beznosov, K. (2009, July). A multi-method approach for user-centered design of identity management systems. In SOUPS.
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