## 3 Results
### AI in Regional Contexts: Business, Industry, and Agriculture  
Regional coverage: This sub-section includes 33 studies. The majority of cases pertain to Asia (\(n=6\); e.g., China, India), with additional case studies identified in Africa (\(n=5\); e.g., Tunisia, Morocco, South Africa), Europe (\(n=4\); e.g., Italia), North America (\(n=1\), USA), and Latin America (\(n=1\)). Sixteen studies adopt a global perspective (\(n=16\)). The application of artificial intelligence in regional contexts is becoming a critical factor for sustainable development across various sectors--from business and agri-food production to urban and industrial planning. AI contributes to the transformation of traditional economic models by enhancing adaptability, decision-making precision, and resource-use efficiency. Recent studies highlight how digital technologies--including machine learning, IoT, digital twins, and cloud platforms--are being integrated into business operations, agriculture, and infrastructure management at both local and regional levels. The use of AI enables the consideration of spatial and socio-economic disparities, the identification of hidden dependencies, and the formulation of comprehensive solutions aimed at increasing the resilience and competitiveness of local ecosystems. The adoption of AI in small- and medium-sized enterprises (SMEs) and industrial systems plays a vital role in enhancing sustainability, environmental performance, and digital maturity. As noted in [116], AI and cloud computing offer significant potential for SMEs, particularly in supply chain management, improving business model transparency, and optimizing resource use. The study emphasizes the importance of technological transformation in SMEs as a pathway to sustainable development and balanced regional growth. The study in [117] examines digital transformation across Moroccan industries, identifying a lack of managerial knowledge and the need to develop flexible innovations, ethical AI frameworks, and workforce training programs. These findings are supported by earlier research in [118], which highlights key barriers to AI adoption in Africa, including data scarcity, limited funding, and infrastructural fragmentation. AI integration is viewed as a driver of sustainable economic growth for local enterprises and ecosystems. Strategicapproaches to AI adoption in developing economies are further explored in [119], where the authors propose a conceptual framework aimed at reducing social inequality, fostering inclusivity, and promoting sustainable growth through business model adaptation and job creation. In [120], AI is employed to analyze the activities of over 1200 companies in the Piedmont region of Italy, using text-mining algorithms on LinkedIn profiles to map the diffusion of AI and digital technologies. This methodology enables the assessment of AI's potential as a tool for monitoring regional innovation ecosystems and supporting decision making for sustainable technological development. The study in [121] presents an innovative combination of website text analysis from metallurgical companies with remote sensing data to detect discrepancies between stated environmental initiatives and actual pollution levels. This approach offers a valuable instrument for addressing greenwashing and advancing standards for sustainable reporting. Artificial intelligence is also actively utilized in applied business processes. In [122], examples of logistics optimization through AI are explored, with a focus on adaptability to market conditions within specific local contexts. The study in [123] applies clustering algorithms (K-means) for customer segmentation and inventory optimization in the retail sector, demonstrating AI's potential to increase profitability and reduce costs by accounting for regional consumer preferences. The development of artificial intelligence has a profound impact on local agricultural and agri-food systems, promoting their sustainability, digital transformation, and climate resilience.