## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Embedding Responsible AI Practices and Human-Centered Governance  
This subsection addresses the growing challenge of embedding responsible AI practices into SME operations--an issue increasingly critical within the organizational and environmental dimensions of the TOE framework. As regulatory frameworks evolve and stakeholder expectations increase, AI adoption must go beyond technical performance to uphold principles such as fairness, transparency, accountability, and inclusion [67].  
Recent studies emphasize that most SMEs lack the internal structures, awareness, or ability to apply these principles consistently [67; 112]. AI is often adopted reactively or without ethical safeguards, creating risks related to compliance, trust, and long-term value alignment. Ref. [67] recommend lightweight but effective governance mechanisms that SMEs can implement to ensure ethical deployment. These include risk assessment protocols, explainability practices, and stakeholder engagement strategies - all of which are essential to align Gen-AI tools with broader business and social values.  
This governance gap exposes SMEs to reputational, operational, and compliance risks, especially as governments introduce AI-specific regulations and consumers increasingly demand ethical and transparent digital practices.  
Therefore, responsible adoption of AI should not be seen as a barrier, but as a strategic enabler of trust, innovation, and market legitimacy in the evolving digital economy.  
To foster responsible and human-centered AI in SMEs, the following practices are recommended:  
* **Integrate Ethical Considerations into AI Planning** Begin AI projects with structured reflection on potential impacts, such as bias, discrimination, or data misuse. Embed human rights principles into AI decision-making from the outset, even in pilot projects.
* **Establish Lightweight Governance Structures** While SMEs may lack formal ethics boards, simple mechanisms, such as designating an AI responsibility lead or incorporating stakeholder reviews, can support ethical oversight and build internal accountability.
* **Promote Transparency and Explainability** Ensure that AI-generated outputs (e.g., decisions, recommendations) are explainable to nontechnical users and customers. Use interfaces that allow humans to override, question, or validate AI suggestions.
* **Strengthen Data Protection and Consent Protocols** Adhere to national and international data privacy laws. Where possible, ensure that AI systems use consent-based anonymized data and that data handling policies are clearly communicated.
* **Engage Stakeholders in Responsible Innovation** Involve employees, users, and partners in discussions about AI risks, values, and priorities. This participatory approach fosters shared responsibility and aligns AI solutions with organizational values.  
In summary, SMEs must view responsible AI as a core component of digital transformation, not an afterthought. By embedding ethical awareness, lightweight governance, and transparent practices into their AI journey, SMEs can improve trust, ensure regulatory compliance, and futureproof their innovation strategies.