## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Ensuring Data Availability, Quality, and Governance for AI Success  
This subsection addresses one of the most critical technological challenges in AI adoption for SMEs: the lack of sufficient, high-quality, and well-integrated data. Within the technological dimension of the TOE framework, data is a foundational input to develop and deploy effective AI systems. However, many SMEs face limitations in data availability, integration, and governance that compromise AI performance, reliability, and ethical use.  
As highlighted by [112], inadequate data infrastructures and fragmented legacy systems are the key technological barriers for SMEs to implement AI. Ref. [115] emphasize that poor data standardization and lack of interoperability often delay the development and decision-making of AI models. Furthermore, ref. [119] argue that without access to robust and well-governed data streams, SMEs cannot benefit from generative AI tools or real-time analytics. From a governance perspective, ref. [67] underscore that data quality and transparency are not only technical requirements but also ethical imperatives to ensure responsible AI results, especially in environments with increasing regulatory scrutiny. Taken together, these findings suggest that SME AI readiness must be rooted in comprehensive data strategies, including collection, cleaning, integration, and governance protocols.  
To overcome this challenge, SMEs should adopt a strategic approach built around five interrelated actions:  
* **Establish Robust Data Collection Systems**:  
Identify key internal and external data sources and implement continuous real-time data collection mechanisms. This improves the quantity and relevance of the data for model training and decision-making.
* **Improve Data Quality through Cleaning and Validation**:  
Invest in systematic data cleaning processes, removing duplicates, correcting errors, and filling gaps, to ensure the integrity and usability of datasets. High-quality data are essential to generate accurate and reliable AI output.
* **Facilitate Data Integration across Systems**:  
Use APIs and cloud platforms to unify data from disparate systems, including legacy platforms. Integrated data ecosystems prevent information silos and support scalable AI solutions that align with firm operations.
* **Leverage Data Enrichment and Analytics Tools**:  
Apply data mining, augmentation, and visualization tools to expand the scope and depth of available data. This enhances the insight generation capacity of AI models and supports more informed business decisions.
* **Implement Strong Data Governance Practices**:  
Define and enforce policies on data access, privacy, storage, and compliance. Aligngovernance with national and international regulations (e.g., GDPR) to ensure ethical, transparent, and secure data usage.  
In summary, SMEs must view data not only as a technical requirement but also as a strategic asset. By building structured data pipelines, improving quality, enabling integration, and ensuring governance, SMEs can significantly enhance their AI readiness and secure long-term value from AI adoption.