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Leveraging AI-Powered Automation for Enhanced Operational Efficiency in Small and Medium Enterprises (SMEs)

APTISI Transactions on Management (ATM) · Vol. 8 No. 3 (2024) · hlm. 250–258

DOI
10.33050/atm.v8i3.2363
ISSN
2622-6804, 2622-6812
Terbit
2024-09-30
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study explores the potential of AI-powered automation in enhancing opera- tional efficiency within Small and Medium Enterprises (SMEs). The primary objective is to identify how automation tools driven by artificial intelligence (AI) can streamline business processes, reduce operational costs, and improve productivity. The methodology includes a quantitative analysis of SMEs that have implemented AI-based solutions, supported by qualitative interviews with key stakeholders. The Results indicate significant improvements in operational workflows, particularly in areas such as supply chain management, customer service, and financial operations. The findings demonstrate that SMEs adopting AI technologies experience reduced human error, faster decision-making pro- cesses, and improved customer satisfaction. However, challenges such as initial investment costs and technical expertise remain. The study concludes that with proper implementation and strategic planning, AI-powered automation can be a key driver of success for SMEs in competitive markets.

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  • Universitas Raharja
  • Telkom University
  • Universitas Muhammadiyah Jakarta
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Rekan penulisOrganisasiArtikel bersama
Untung RahardjaUniversitas Raharja1
Adhi PrasetioTelkom University1
Fetty Poerwita SaryTelkom University1
Nidya DudijaTelkom University1
Alfiah KhoirunisaUniversitas Raharja1
Nuke Puji Lestari SantosoUniversitas Raharja1
Kelvin PangaribuanUniversitas Raharja1
Marviola Grace HardiniUniversitas Raharja1
Agnes Novalita SavitriAfiliasi 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">Leveraging AI-Powered Automation for Enhanced Operational Efficiency in Small and Medium Enterprises (SMEs) </dc:title>
	<dc:creator xml:lang="en">Andayani, Dwi</dc:creator>
	<dc:creator xml:lang="en">Indiyati, Dian</dc:creator>
	<dc:creator xml:lang="en">Mayang Sari, Meri</dc:creator>
	<dc:creator xml:lang="en">Yao, Goh</dc:creator>
	<dc:creator xml:lang="en">Williams, Jack</dc:creator>
	<dc:creator xml:lang="id">Yao, Goh</dc:creator>
	<dc:subject xml:lang="en">AI-Powered Automation</dc:subject>
	<dc:subject xml:lang="en">Operational Efficiency</dc:subject>
	<dc:subject xml:lang="en">Small and Medium Enterprises (SMEs)</dc:subject>
	<dc:description xml:lang="en">This study explores the potential of AI-powered automation in enhancing opera- tional efficiency within Small and Medium Enterprises (SMEs). The primary objective is to identify how automation tools driven by artificial intelligence (AI) can streamline business processes, reduce operational costs, and improve productivity. The methodology includes a quantitative analysis of SMEs that have implemented AI-based solutions, supported by qualitative interviews with key stakeholders. The Results indicate significant improvements in operational workflows, particularly in areas such as supply chain management, customer service, and financial operations. The findings demonstrate that SMEs adopting AI technologies experience reduced human error, faster decision-making pro- cesses, and improved customer satisfaction. However, challenges such as initial investment costs and technical expertise remain. The study concludes that with proper implementation and strategic planning, AI-powered automation can be a key driver of success for SMEs in competitive markets.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2024-09-30</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/2363</dc:identifier>
	<dc:identifier>10.33050/atm.v8i3.2363</dc:identifier>
	<dc:source xml:lang="en">APTISI Transactions on Management (ATM); Vol. 8 No. 3 (2024): September; 250–258</dc:source>
	<dc:source xml:lang="id">APTISI Transactions on Management (ATM); Vol 8 No 3 (2024): September; 250–258</dc:source>
	<dc:source>2622-6804</dc:source>
	<dc:source>2622-6812</dc:source>
	<dc:source>10.33050/atm.v8i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://ijc.ilearning.co/ATM/article/view/2363/862</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Dwi Andayani, Dian Indiyati, Meri Mayang Sari, Goh Yao, Jack Williams (Author)</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>