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Open access Journal SINTA 2

Exploring the Relationship between Artificial Intelligence and Business Performance

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 1 (2025) · pp. 1–12

DOI
10.22146/ijccs.86697
ISSN
2460-7258, 1978-1520
Published
2025-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The integration of Artificial Intelligence (AI) into business operations has garnered significant attention due to its potential impact on business performance. However, the relationship between AI adoption and business performance remains not fully understood. This article comprehensively analyzes this relationship through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption. In this study, we employ SmartPLS as an analytical tool to evaluate the relationships between identified factors and the impact of AI adoption on business performance. Our findings reveal that several factors influence the adoption and implementation of AI, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations. Moreover, AI adoption significantly influences business performance metrics such as productivity, efficiency, revenue, and customer satisfaction. Nonetheless, challenges arising from AI adoption, including shifts in job roles, data privacy, and security concerns, also require substantial attention. In conclusion, successful AI adoption and implementation necessitate careful consideration of organizational, technical, and ethical factors. This research provides valuable insights for business leaders and researchers seeking a deeper understanding of the relationship between Artificial Intelligence and business performance.

Keywords

Collaboration network of the authors

40 authors · 150 co-authorship links · 29 across different organisations · showing the 40 most connected

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

  • Satya Wacana Christian University
  • Universitas Raharja
  • Universitas Catur Insan Cendekia Cirebon
  • Universitas Buddhi Dharma
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Untung RahardjaAffiliation unknown13
Qurotul AiniUniversitas Raharja12
Danny ManonggaSatya Wacana Christian University9
Hindriyanto Dwi PurnomoSatya Wacana Christian University8
Sutarto WijonoAffiliation unknown5
Theophilus WellemAffiliation unknown4
HenderiAffiliation unknown4
Indri HandayaniUniversitas Raharja3
Fitra Putri OgandaUniversitas Raharja3
Ade IrianiSatya Wacana Christian University3
Nuke Puji Lestari SantosoUniversitas Raharja3
Ivanna K. TimotiusSatya Wacana Christian University2
Hanna Arini ParhusipSatya Wacana Christian University2
Kristoko Dwi HartomoSatya Wacana Christian University2
Hendry HendrySatya Wacana Christian University2
Khairul AnsharAffiliation unknown2
Tundjung MahatmaSatya Wacana Christian University2
Bambang SusantoSatya Wacana Christian University2
Faldy TitaAffiliation unknown2
Aditya MardiansyahAffiliation unknown2
Agung RizkyAffiliation unknown2
Sudaryono SudaryonoUniversitas Raharja2
Firman HanafiUniversitas Raharja2
Alfiah KhoirunisaUniversitas Raharja2
Eka Purnama HarahapUniversitas Raharja2
Sudaryono SudaryonoUniversitas Raharja2
Eko SediyonoSatya Wacana Christian University2
Adam FaturahmanAffiliation unknown2
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon1
Suwandi SuwandiUniversitas Catur Insan Cendekia Cirebon1
Chairun NasUniversitas Catur Insan Cendekia Cirebon1
Benny DaniawanUniversitas Buddhi Dharma1
Dwi ApriliasariAffiliation unknown1
Rusli Ginting MuntheAffiliation 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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Exploring the Relationship between Artificial Intelligence and Business Performance</dc:title>
	<dc:creator>Lutfiani, Ninda</dc:creator>
	<dc:creator>Sembiring, Irwan</dc:creator>
	<dc:creator>Setyawan, Iwan</dc:creator>
	<dc:creator>Setiawan, Adi</dc:creator>
	<dc:creator>Rahardja, Untung</dc:creator>
	<dc:creator>Sulistio, Sulistio</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Artificial Intelligence; SmartPLS; Business Performance</dc:subject>
	<dc:description xml:lang="en-US">The integration of Artificial Intelligence (AI) into business operations has garnered significant attention due to its potential impact on business performance. However, the relationship between AI adoption and business performance remains not fully understood. This article comprehensively analyzes this relationship through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption. In this study, we employ SmartPLS as an analytical tool to evaluate the relationships between identified factors and the impact of AI adoption on business performance. Our findings reveal that several factors influence the adoption and implementation of AI, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations. Moreover, AI adoption significantly influences business performance metrics such as productivity, efficiency, revenue, and customer satisfaction. Nonetheless, challenges arising from AI adoption, including shifts in job roles, data privacy, and security concerns, also require substantial attention. In conclusion, successful AI adoption and implementation necessitate careful consideration of organizational, technical, and ethical factors. This research provides valuable insights for business leaders and researchers seeking a deeper understanding of the relationship between Artificial Intelligence and business performance.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/86697</dc:identifier>
	<dc:identifier>10.22146/ijccs.86697</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 1 (2025): January; 1-12</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/86697/40991</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>