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Enhancing Organizational Efficiency through the Integration of Artificial Intelligence in Management Information Systems

APTISI Transactions on Management (ATM) · Vol. 7 No. 3 (2023) · hlm. 275–282

DOI
10.33050/atm.v7i3.2146
ISSN
2622-6804, 2622-6812
Terbit
2023-09-28
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research delves into AI's role in enhancing Management Information Systems for organizational efficiency. It employs cross-sector case studies to showcase AI's potential in automating tasks, offering predictive insights from historical data, and bolstering decision-making. While AI promises substantial benefits, it also poses technical and ethical challenges during implementation. AI integration emerges as a game-changer, liberating organizations from mundane tasks through automation. Predictive analytics empowers firms to foresee trends, fostering a competitive edge in decision-making. Yet, obstacles include algorithm compatibility with existing systems and the demand for heightened technical proficiency. Ethical considerations loom large, demanding robust privacy and fairness guidelines in AI data usage. This research underscores the importance of employee AI training and multidisciplinary teams for tackling technical hurdles. Ethical principles should permeate AI development and utilization. The study recommends a three-fold strategy: First, prioritize employee AI training for seamless adoption. Second, establish cross-disciplinary teams to navigate technical complexities. Third, embed ethics in every AI facet to maintain trust. In conclusion, a holistic approach allows organizations to seamlessly integrate AI into Management Information Systems, yielding operational efficiencies, superior decision-making, and a competitive edge in a dynamic business landscape.

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17 penulis · 42 tautan kepenulisan bersama · 6 lintas organisasi

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  • Universitas Raharja
  • Universitas Pamulang
  • Universitas Esa Unggul
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Fitra Putri OgandaUniversitas Raharja1
Umi RusilowatiUniversitas Pamulang1
Reymund RahardjaUniversitas Raharja1
Elsevier AimeeAfiliasi tidak diketahui1
Eiser Aaron BeldiqAfiliasi tidak diketahui1
Natasya Aprila YusufUniversitas Raharja1
Brigitta CallulaAfiliasi tidak diketahui1
Eiser Aaron BeldiqAfiliasi tidak diketahui1
Daniel BennetAfiliasi tidak diketahui1
Ridhuan AhsanitaqwimUniversitas Raharja1
Jerry MaratisUniversitas Esa Unggul1
Ahmad RamadanUniversitas Raharja1
Daniel BennetAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ijc.ilearning.co/ATM/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Enhancing Organizational Efficiency through the Integration of Artificial Intelligence in Management Information Systems</dc:title>
	<dc:creator xml:lang="en">Bhima, Bhima</dc:creator>
	<dc:creator xml:lang="en">Rahmania Az Zahra, Achani</dc:creator>
	<dc:creator xml:lang="en">Nurtino, Tio</dc:creator>
	<dc:creator xml:lang="en">Firli, M. Zaki</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Management Information System</dc:subject>
	<dc:subject xml:lang="en">Organizational Efficiency</dc:subject>
	<dc:subject xml:lang="en">Data Analysis</dc:subject>
	<dc:subject xml:lang="en">Decision Making</dc:subject>
	<dc:description xml:lang="en">This research delves into AI's role in enhancing Management Information Systems for organizational efficiency. It employs cross-sector case studies to showcase AI's potential in automating tasks, offering predictive insights from historical data, and bolstering decision-making. While AI promises substantial benefits, it also poses technical and ethical challenges during implementation. AI integration emerges as a game-changer, liberating organizations from mundane tasks through automation. Predictive analytics empowers firms to foresee trends, fostering a competitive edge in decision-making. Yet, obstacles include algorithm compatibility with existing systems and the demand for heightened technical proficiency. Ethical considerations loom large, demanding robust privacy and fairness guidelines in AI data usage. This research underscores the importance of employee AI training and multidisciplinary teams for tackling technical hurdles. Ethical principles should permeate AI development and utilization. The study recommends a three-fold strategy: First, prioritize employee AI training for seamless adoption. Second, establish cross-disciplinary teams to navigate technical complexities. Third, embed ethics in every AI facet to maintain trust. In conclusion, a holistic approach allows organizations to seamlessly integrate AI into Management Information Systems, yielding operational efficiencies, superior decision-making, and a competitive edge in a dynamic business landscape.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2023-09-28</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://ijc.ilearning.co/ATM/article/view/2146</dc:identifier>
	<dc:identifier>10.33050/atm.v7i3.2146</dc:identifier>
	<dc:source xml:lang="en">APTISI Transactions on Management (ATM); Vol. 7 No. 3 (2023): September; 275-282</dc:source>
	<dc:source xml:lang="id">APTISI Transactions on Management (ATM); Vol 7 No 3 (2023): September; 275-282</dc:source>
	<dc:source>2622-6804</dc:source>
	<dc:source>2622-6812</dc:source>
	<dc:source>10.33050/atm.v7i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://ijc.ilearning.co/ATM/article/view/2146/780</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Bhima, Achani Rahmania Az Zahra, Tio Nurtino, M. Zaki Firli (Author)</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>