## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Strengthening Public-Private Collaboration to Support AI Adoption  
This subsection addresses the challenge of insufficient public-private collaboration, situated within the environmental dimension of the TOE framework. Despite growing interest in AI, small companies often lack access to well-structured and inclusive ecosystems that connect them to public institutions, tech providers, and knowledge centers. This disconnect can hinder the diffusion of AI knowledge, tools, and funding, especially in regions where digital infrastructure and innovation policies are underdeveloped. As [97] highlights, government incentives, cross-sector alliances, and institutional frameworks are critical enablers to overcome environmental barriers to AI adoption. Their findings reinforce the need for coordinated action between governments, academia and industry to bridge the resource and knowledge gaps facing SMEs. To overcome this barrier, a multi-stakeholder approach is needed to build a supportive environment where SMEs can access the expertise, infrastructure, and capital required for sustainable AI adoption. Key strategies to strengthen public-private collaboration include the following:  
* **Develop Inclusive Innovation Ecosystems** Foster structured partnerships among governments, technology providers, universities, and research centers. These ecosystems should offer shared resources, such as cloud access, computing infrastructure, training programs, and co-investment platforms. * **Establish Collaborative Spaces for SMEs and Tech Stakeholders** Create physical and virtual environments, such as AI hubs, open innovation labs and online platforms, where SMEs can co-develop AI solutions, share experiences, and get advice from technical experts and researchers. * **Implement Tailored Financial Incentives** Design public funding mechanisms such as grants, tax credits, and subsidized loans specifically targeted at SMEs seeking AI adoption. Encourage private sector co-financing through equity partnerships or innovation vouchers. * **Promote Knowledge Exchange and Joint Capacity Building** Launch joint research initiatives, AI-focused training programs, and cross-sector mentorships that connect SMEs with academic and corporate expertise. These initiatives can reduce skill gaps and accelerate learning. * **Introduce Adaptive and Innovation-Friendly Regulation** Simplify regulatory procedures and promote standards that support experimentation and reduce compliance burdens. This fosters a more agile environment for testing and adopting AI technologies. In summary, public-private collaboration is a critical enabler of AI adoption in SMEs, yet current efforts often lack structure, accessibility, and strategic alignment. By building integrated, resource-rich ecosystems supported by financial incentives, shared knowledge, and flexible regulation, stakeholders can unlock the full potential of AI for SMEs and drive inclusive innovation at scale. Strategically Leveraging Generative AI to Democratize Capabilities and Drive Innovation: Toward Open-Weight LLM Adoption  
This subsection addresses the dual challenge and transformative opportunity of strategically integrating generative artificial intelligence (Gen-AI) into SME operations. Located within both the technological and organizational dimensions of the TOE framework, this challenge concerns not only access to cutting-edge tools but also the strategic alignment, governance, and scalability of those tools within SME contexts. Gen-AI is revolutionizing access to intelligent automation, enabling SMEs to perform tasks previously reserved for large enterprises. As demonstrated by [119], tools such as Microsoft Copilot, Jasper, and Canva allow smaller firms to automate content creation, enhance customer interaction, and support strategic planning--all with minimal infrastructure and low financial barriers. These tools democratize innovation, allowing SMEs to overcome longstanding resource constraints. Similarly, ref.