## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Closing the Knowledge Gap and Technical Expertise  
This subsection addresses one of the most fundamental barriers to AI adoption in SMEs: the lack of internal knowledge and technical capabilities. Within the TOE framework, this challenge cuts across both technological and organizational dimensions. Technologically, SMEs often lack familiarity with data infrastructure, machine learning, and AI tools. Organizationally, they frequently operate without in-house expertise or structured training programs to close this gap. This deficiency can lead to ineffective decision-making, failed implementation attempts, and under-utilization of the potential of AI. For SMEs, where resources and specialization are typically limited, targeted solutions are needed to empower staff and reduce dependency on high-level technical talent. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Core Challenge** & **Strategic Solution** & **TOE Dimension** \\ \hline
1. Knowledge and Expertise Gaps & Lack of internal technical capabilities to manage AI & Internal training, low-code tools, external partnerships & Technological/ Organizational \\ \hline
2. Scalability Constraints & High risk and complexity of scaling AI solutions & Modular deployment, agile methods, cloud platforms & Technological \\ \hline
3. Financial Limitations & High upfront and ongoing costs for AI systems & Flexible financing, pilot testing, government incentives & Organizational \\ \hline
4. Data Deficiency & Insufficient, unstructured, or poor-quality data & Data governance, real-time collection, cleaning, API integration & Technological \\ \hline
5. Infrastructure and Integration Issues & Incompatible legacy systems and lack of AI-ready infrastructure & Cloud-based tools, APIs, AI computation centers & Technological \\ \hline
6. Cultural Resistance to Change & Organizational fear, inertia, and lack of innovation mindset & Inclusive leadership, innovation culture, pilot-driven learning & Organizational \\ \hline
7. Human–AI Productivity & Anxiety about automation and job displacement & Augmentation approaches (co-intelligence, centaur models) & Organizational \\ \hline
8. Weak Public-Private Collaboration & Limited access to shared knowledge, funding, or platforms & Innovation ecosystems, public-private partnerships, shared spaces & Environmental \\ \hline
9. Limited Access to advanced AI such as Gen-AI & Barriers to using state-of-the-art AI such as Gen-AI & Plug-and-play Gen-AI tools, augmentation use cases, strategic embedding, toward open-weight LLM adoption & Technological/ Organizational \\ \hline
10. Lack of Responsible AI Governance & Absence of ethical practices and transparency in AI deployment & Lightweight governance, transparency, stakeholder engagement, data protection & Organizational/ Environmental \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of AI adoption challenges and actionable solutions in SMEs. As noted in [97], one of the main organizational constraints that affects the implementation of AI in small businesses is the shortage of skilled personnel, especially in data-driven roles. Ref. [112] emphasize that limited technical expertise and lack of internal champions are among the most cited barriers in the reviewed studies. Similarly, ref. [100] identify training deficiencies as a primary obstacle to realizing AI benefits in marketing-oriented SMEs in emerging economies. On a broader level, ref. [115] argue that without appropriate training and awareness programs, even well-intentioned AI pilots are likely to perform poorly or completely stall. These findings underscore the importance of investing in customized capacity-building strategies. Blended approaches, including continuous learning platforms, collaborations with research institutions, and low-code AI tools, can significantly improve SMEs' ability to absorb and apply AI technologies over time.