Publicação
Improving ITIL practices using GenAI for agility and decision-making: A Systematic Review and Research Gaps in ITSM Applications
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Costa, Maria Manuela Simões Aparício da | |
| dc.contributor.author | Santos, Vinícius | |
| dc.date.accessioned | 2025-04-11T15:46:48Z | |
| dc.date.available | 2025-04-11T15:46:48Z | |
| dc.date.issued | 2025-04-10 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Digital Transformation | pt_PT |
| dc.description.abstract | The ITIL framework is a widely adopted standard for IT service management (ITSM), ensuring structured processes and operational efficiency. However, its reliance on manual workflows limits agility and responsiveness in dynamic digital environments. While Artificial Intelligence (AI) has demonstrated potential in ITSM, the specific role of Generative AI (GenAI) within ITIL remains underexplored. Given its capabilities in automation, decision-making, and process optimization, integrating GenAI could transform traditional ITIL practices. This research explores how GenAI can enhance ITIL processes, particularly in incident management and decision-making, by increasing automation and operational agility. Using the PRISMA methodology, a systematic literature review (SLR) is conducted to analyze existing studies on AI applications in ITIL and ITSM, identifying key gaps and opportunities for GenAIdriven automation. The study aims to provide a comprehensive overview of current research while outlining critical areas where GenAI can optimize workflows and improve predictive capabilities. Based on these insights, a theoretical framework is proposed to illustrate how GenAI can enhance ITIL workflows, supporting adaptive decision-making and fostering greater efficiency in IT service management. The expected outcomes include a structured model for AI-driven ITSM and alignment with key Sustainable Development Goals (SDGs), particularly in promoting innovation, improving service efficiency, and ensuring responsible technology adoption. | pt_PT |
| dc.identifier.tid | 203940423 | |
| dc.identifier.uri | http://hdl.handle.net/10362/182178 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Artificial Intelligence | pt_PT |
| dc.subject | GenAI | pt_PT |
| dc.subject | ITIL | pt_PT |
| dc.subject | ITSM | pt_PT |
| dc.subject | Decision Making | pt_PT |
| dc.subject | Predictive Analysis | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 12 - Responsible production and consumption | pt_PT |
| dc.title | Improving ITIL practices using GenAI for agility and decision-making: A Systematic Review and Research Gaps in ITSM Applications | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Transformação Digital | pt_PT |