TY - JOUR
T1 - Implementations of Artificial Intelligence in Various Domains of IT Governance
T2 - A Systematic Literature Review
AU - Hariyanti, Eva
AU - Janeswari, Made Balin
AU - Moningka, Malvin Mikhael
AU - Aziz, Fikri Maulana
AU - Putri, Annisa Rahma
AU - Hapsari, Oxy Setyo
AU - Sutha, Nyoman Agus Arya Dwija
AU - Sinaga, Yohannes Alexander Agusti
AU - Bendesa, Manik Prasanthi
N1 - Publisher Copyright:
© 2023 The Authors. Published by Universitas Airlangga.
PY - 2023/10
Y1 - 2023/10
N2 - Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities.
AB - Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities.
KW - Artificial Intelligence
KW - Deming cycle
KW - Governance
KW - IT Governance domain
KW - Systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85176096021&partnerID=8YFLogxK
U2 - 10.20473/jisebi.9.2.305-319
DO - 10.20473/jisebi.9.2.305-319
M3 - Article
AN - SCOPUS:85176096021
SN - 2598-6333
VL - 9
SP - 305
EP - 319
JO - Journal of Information Systems Engineering and Business Intelligence
JF - Journal of Information Systems Engineering and Business Intelligence
IS - 2
ER -