## 7 Discussion: Lessons Learned, Limitations, and Future Research Directions
### Lessons Learned  
Building on the analysis of adoption barriers and implementation strategies, this section presents ten key lessons for SMEs, policymakers, and researchers, structured around the TOE-DOI framework.  
1. _AI presents transformative opportunities for SMEs._ AI enables automation, smarter decision-making, customer personalization, and new value creation pathways. When aligned with the lean structures and constraints of SMEs, these tools can significantly enhance competitiveness and innovation (TOE: Technological).
2. _Adoption lags due to multi-dimensional barriers._ Despite its potential, AI uptake in SMEs remains slow. Challenges include technological complexity, financial constraints, lack of digital culture, and limited external support. The TOE-DOI framework helps identify and address these systematically at the structural and perceptual levels.  
3. _Adoption strategies must reflect SME-specific realities._ SMEs operate with unique restrictions, limited resources, agile decision-making, and informal structures. Effective adoption of AI requires tailored and scalable approaches sensitive to these characteristics (TOE: Organizational).
4. _AI must be embedded in strategy--not treated as a side project._ AI should be part of the firm's core strategic planning, not an isolated technology experiment. This requires long-term vision, structured roadmaps, and alignment of leadership. Our six-phase implementation model offers practical guidance for SMEs to embed AI into their growth strategies (TOE: Organizational + Technological).
5. _Organizational culture is a key enabler._ A culture that supports experimentation, learning, and inclusive change management promotes smoother adoption of AI. Overcoming fear and resistance requires leadership to create safe spaces for adaptation (TOE: Organizational).
6. _Talent development and upskilling are essential._ Technical and managerial knowledge of AI must be built internally. Upskilling existing staff and reducing dependence on external consultants enhances sustainability and internal capability (TOE: Organizational).
7. _Accessible, modular, and open-weight solutions can democratize adoption._ Cloud-based platforms, plug-and-play tools, and open-weight LLMs--such as LLaMA, DeepSeek-R1, Mistral, and Falcon--offer SMEs scalable, transparent, and cost-effective alternatives to proprietary systems. These models support self-hosted, hybrid, or cloud-based deployment, enabling greater control over data, compliance, and customization. By lowering technical and financial barriers, open-weight solutions empower SMEs to adopt AI in ways that align with their strategic goals and operational realities (TOE: Technological + Organizational).
8. _Data quality and governance underpin successful AI._ Reliable, accessible, and clean data is the foundation for effective AI. SMEs must invest in data infrastructure, governance protocols, and integration tools such as APIs to ensure actionable insights (TOE: Technological).
9. _Ethical and responsible AI must be integrated from the start._ As SMEs adopt powerful AI tools, governance mechanisms around fairness, transparency, and data protection are essential. Lightweight and actionable frameworks can help SMEs ensure trust and compliance (TOE: Organizational + Environmental).
10. _Public-private ecosystems should support SME AI adoption._ Collaboration with universities, governments, tech providers and AI computation centers can provide SMEs with access to expertise, infrastructure and financial support, especially in early adoption phases (TOE: Environmental).  
These lessons provide a solid roadmap for SMEs to pursue AI not just as a technology trend, but as a strategic enabler for sustainable growth, inclusion, and resilience.