Examining the Factors that Influence the Enculturation of Artificial Intelligence to Enhance the Productivity of MSMEs' Business for Sustainability
Year: 2025
Type: article
Abstract: Micro, Small, and Medium-Sized Enterprises (MSMEs) have revolutionary prospects to improve sustainability and efficiency in the digital age through the development of artificial intelligence (AI). However, due to obstacles including scarce resources, limited technical expertise, and unclear implementation pathways, MSMEs have yet to adopt AI despite its potential. To address this gap, this study develops a Framework for AI-Driven Sustainable Enterprises (F.A.S.E.) to guide MSMEs in integrating AI while aligning with social, economic, and environmental sustainability objectives. A mixed-methods approach was employed, combining qualitative insights from interviews with AI specialists and MSME stakeholders with a systematic literature review on AI and sustainability. The methodology was further validated through quantitative surveys of MSMEs across various industries, assessing outcomes, facilitators, and barriers of AI adoption. The proposed F.A.S.E. framework identifies organizational culture, skills development, financial accessibility, and technological preparedness as critical success factors. Preliminary evidence suggests that MSMEs implementing AI report 20–35% gains in operational efficiency and 15–25% reductions in resource waste, underscoring AI’s role in sustainable growth. This study advances theory by bridging literature on AI adoption and sustainability within the MSME context. Practically, it provides a comprehensive strategy for ethical AI adoption and policy recommendations for institutions and governments to support digital transformation. The study highlights the urgent need for structured AI frameworks tailored to MSMEs to advance the UN Sustainable Development Goals (SDGs) and foster resilient, future-ready enterprises.
Institutions: Universiti Teknologi MARA System, Universiti Teknologi MARA
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4414436858
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