aceAI adoption in small and medium-sized enterprises
Source article: Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the t…
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This peer-reviewed conceptual analysis examines why small and medium-sized enterprises struggle to adopt AI despite its transformative potential. Using the technology-organization-environment framework combined with diffusion of innovations attributes, it identifies ten critical challenges across data access, skills, culture, infrastructure, and governance, and pairs them with context-sensitive solutions.
The contribution matters because SMEs represent a large share of economic activity but lack operational guidance tailored to resource constraints and responsible use demands. The authors propose a six-phase roadmap incorporating responsible governance and open-weight LLMs, but note it remains unvalidated, leaving uncertainty about its practical effectiveness across different sectors and regions.
- Integrates technology-organization-environment framework with diffusion of innovations attributes to examine adoption dynamics through dual structural and perceptual lens.
- Identifies ten critical challenges across TOE dimensions including data access, skill shortages, cultural resistance, infrastructure limitations, and weak governance practices.
- Expands framework to include responsible AI governance and democratized access to generative AI, specifically open-weight LLMs such as LLaMA, DeepSeek-R1, Mistral, and FALCON.
- Proposes six-phase roadmap methodology aligned with technological, organizational, and strategic readiness, described as conceptual and not yet field-validated.
A structured six-phase roadmap methodology guides SMEs through AI adoption by pairing each barrier with actionable, context-sensitive solutions and incorporating responsible AI governance and open-weight LLMs.
Small and medium-sized enterprises continue to face significant challenges in effective AI adoption, with ten critical barriers across technology, organization, and environment including data access, skill shortages, cultural resistance, infrastructure limitations, and weak governance.
The rundown
The study integrates the TOE framework with DOI attributes and incorporates sectoral and regional empirical insights to analyze adoption dynamics. It expands the analysis to include responsible AI governance and democratized access to open-weight generative models including LLaMA, DeepSeek-R1, Mistral, and FALCON.
Each of the ten challenges is paired with actionable, context-sensitive solutions, culminating in a six-phase roadmap intended to align with technological, organizational, and strategic readiness. The authors position the work as bridging theoretical insight and implementation strategy for sustainable and inclusive innovation.
The proposed six-phase roadmap is conceptual and has not been validated through field data, limiting evidence of effectiveness in real SME settings.
Sources
- Peer-reviewedApplied Sciences2025-06-09
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