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Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
Citation. Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges. Applied Sciences, 15(12), 6465. https://doi.org/10.3390/app15126465
Summary. This descriptive article synthesizes recent empirical studies and cases on small- and medium-sized enterprise AI adoption through the technology–organization–environment (TOE) framework supplemented with diffusion-of-innovations constructs. It develops a challenge-and-response roadmap focused on resource constraints, technical expertise, data and infrastructure, organizational culture, and external support. The article does not estimate AI’s net performance effects directly.
Key claims
- SME AI adoption is shaped jointly by technological readiness, organizational capacity, environmental conditions, and perceived innovation attributes such as compatibility, complexity, and trialability.
- Resource constraints, skills gaps, uncertain returns, data-quality and governance problems, integration requirements, and limited infrastructure can hinder early AI implementation in SMEs.
- A staged adoption approach that identifies use cases, builds capabilities and governance, and aligns implementation with organizational context is presented as a practical response to these constraints.
Evidence & limitations
- The paper is a descriptive synthesis drawing on recent empirical studies and regional or sectoral cases rather than a new primary survey or causal evaluation.
- Its proposed TOE–DOI roadmap is useful as an organizing framework, but its broad cross-context recommendations should not be treated as direct estimates of adoption or performance effects for a specific SME population.