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Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review

Journal of Information Systems Engineering and Business Intelligence · Vol. 9 No. 2 (2023) · pp. 305–319

DOI
10.20473/jisebi.9.2.305-319
ISSN
2443-2555, 2598-6333
Published
2023-11-01
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

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. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review

Collaboration network of the authors

27 authors · 101 co-authorship links · 32 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Airlangga
  • NHL Stenden University of Applied Sciences
  • Bandung Institute of Technology
  • Shih Chien University
  • National Cheng Kung University
  • National Research and Innovation Agency
  • Universiti Teknologi MARA
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Badrus ZamanUniversitas Airlangga2
Indra Kharisma RaharjanaUniversitas Airlangga2
Kridanto SurendroBandung Institute of Technology1
Indah WerdiningsihUniversitas Airlangga1
Mukhammad Ilham Nur FaizinUniversitas Airlangga1
Eto WuryantoUniversitas Airlangga1
Bagus Puji SantosoUniversitas Airlangga1
Nania NuzulitaUniversitas Airlangga1
Arya PrasetyaUniversitas Airlangga1
Anindya Wita WisesaUniversitas Airlangga1
Aditya NugrohoShih Chien University1
Afifah NurrosyidahNational Cheng Kung University1
Army JustitiaUniversitas Airlangga1
Taufik TaufikNational Research and Innovation Agency1
Faried EffendyUniversitas Airlangga1
Shukor Sanim Mohd FauziUniversiti Teknologi MARA1
Airlangga Dwi SatrioAffiliation unknown1
Vabela Dwi SeravinAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review</dc:title>
	<dc:creator>Hariyanti, Eva</dc:creator>
	<dc:creator>Janeswari , Made Balin </dc:creator>
	<dc:creator>Moningka, Malvin Mikhael</dc:creator>
	<dc:creator>Aziz , Fikri Maulana </dc:creator>
	<dc:creator>Putri, Annisa Rahma </dc:creator>
	<dc:creator>Hapsari , Oxy Setyo </dc:creator>
	<dc:creator>Sutha, Nyoman Agus Arya Dwija</dc:creator>
	<dc:creator>Sinaga, Yohannes Alexander Agusti </dc:creator>
	<dc:creator>Bendesa, Manik Prasanthi </dc:creator>
	<dc:description xml:lang="en-US">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.
Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2023-11-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/48186</dc:identifier>
	<dc:identifier>10.20473/jisebi.9.2.305-319</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 9 No. 2 (2023): October; 305-319</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/48186/26593</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>