## 4 Empirical Insights from Sectorial and Regional Studies
### AI Adoption in Manufacturing and Retails SMEs  
This subsection focuses on manufacturing and retailing for SMEs with two case of analysis, AI-driven smart manufacturing, and AI and IoT integration in retail and manufacturing SMEs. 1.1 AI-Driven Smart Manufacturing: Technological and Organizational Lessons from Customization Models [109]  
[PERSON] et al. [109] provide a comprehensive review of AI-driven smart manufacturing systems, focusing on custom manufacturing factories that integrate AI with the Internet of Things (IoT), cyber-physical systems (CPS), and cloud computing. Although the study does not focus solely on SMEs, it offers valuable insight into the structural and implementation challenges SMEs may face when trying to adopt such advanced systems. Technologically, the study emphasizes that AI-enabled manufacturing relies heavily on data fusion, distributed decision-making, and predictive analytics, capabilities that often exceed the technical maturity of typical SMEs (TOE: Technological). Organizationally, successful implementation requires strong coordination across supply chains, cross-functional collaboration, and highly skilled staff, factors that align with the DOI constructs of complexity, observability, and trialability. [PERSON] et al. [109] highlight practical adoption hurdles such as fragmented data architectures, cybersecurity risks, and lack of standardization, which resonate with many of the barriers. For SMEs, these challenges are magnified due to budget constraints and limited IT infrastructure. The paper also notes that while large firms can internally develop smart factory capabilities, SMEs often depend on external partnerships or modular platforms, reinforcing the importance of ecosystems and scalable cloud-based solutions discussed throughout this study. Quantitatively, the review cites improvements of up to 20-30% in operational efficiency, defect reduction, and time-to-market among early adopters of AI-driven customized manufacturing. These performance outcomes reinforce the ROI potential of AI adoption when technological and organizational readiness align. In general, this study supports the call for phased low-risk AI deployment in SMEs, such as through simulation tools, AI-as-a-Service models, or open source toolkits, validating both the scalability and strategic alignment principles embedded in our proposed framework. 1.2 AI and IoT Integration in Retail and Manufacturing SMEs: Operational Efficiencies and Strategic Challenges [110]  
[PERSON] and [PERSON] [110] present a comprehensive analysis of how the synergy between AI and the IoT transforms operational strategies in retail and manufacturing SMEs. Their findings align with several dimensions of the TOE-DOI framework, particularly within the technological and organizational domains. In the retail sector, the deployment of IoT sensors and AI algorithms facilitates personalized customer experiences and predictive inventory management. Quantitative results highlight improvements such as up to 20% increases in sales through AI-driven personalization and significant reductions in stock shortages and overstocking. In manufacturing, the integration of AI-enabled predictive maintenance and real-time quality control using IoT data reduced downtime by up to 30% and defect rates by 25%, reflecting measurable ROI and operational efficiency. Despite these benefits, the study underscores key adoption challenges: high initial infrastructure costs (TOE: Technological), a shortage of skilled personnel (TOE: Organizational), and persistent cybersecurity concerns (TOE: Environmental). Notably, the authorsadvocate for cloud-based and edge computing solutions to reduce latency and improve scalability, which mirrors the challenge-solution logic proposed in this paper. The study further reinforces the importance of modular and democratized AI deployments, contributing empirical validation to the strategic actions outlined in Section 4.