Search and rescue (SAR) operations are often hindered by limited coverage, slow response times, and operational risks, making rapid and reliable victim detection a critical challenge. To address these limitations, this study presents an AI-driven UAV framework that integrates a simulated multi-UAV routing with a YOLOv8-based human detection model. A region-specific aerial dataset consisting of 2430 images and 2831 annotated human instances was collected across diverse terrains in Jordan in collaboration with the Jordan Design and Development Bureau (JODDB). After preprocessing and mosaic augmentation, the dataset expanded to nearly 6000 training samples, enabling robust model fine-tuning. YOLOv8, initialized with VisDrone weights, achieved 97.0% precision, 97.6% recall, and 98.4% mAP@0.50. A multi-UAV routing algorithm based on a lawnmower pattern ensured 100% coverage of a 17.6 km² pilot area using 16 UAVs with balanced mission durations. The results demonstrate that combining UAV clusters with AI-based detection significantly enhances scalability, coverage efficiency, and recall, reducing the risk of life-critical false negatives. While the system shows strong potential, challenges remain regarding communication constraints, latency, and environmental robustness. Overall, this work provides a validated framework for AI-supported UAV SAR operations and offers a foundation adaptable to broader disaster-response scenarios worldwide.

This paper explores the role of Artificial Intelligence (AI) in supply chain risk management (SCRM) in the context of international entrepreneurial projects across diverse industries. Using a qualitative multiple case study design, the research draws on 20 semi-structured interviews conducted across 10 projects to examine how AI technologies support risk management in dynamic and uncertain environments. Template analysis revealed that entrepreneurial ventures typically adopt a five-stage risk management process: risk mentoring, risk identification, risk analysis, risk mitigation, and continuous adaptation. AI tools facilitate each stage by enabling real-time monitoring (through the Internet of Things), predictive risk identification (via Machine Learning and Natural Language Processing), secure mitigation strategies (using Blockchain and Robotic Process Automation), and adaptive learning (through reinforcement learning). These capabilities allow entrepreneurs to proactively respond to external disruptions, foster operational flexibility, and continuously refine their approaches. As a result, AI emerges as a transformative enabler of agile, data-driven SCRM practices tailored to the unique vulnerabilities of entrepreneurial projects. This study provides a practical framework to guide decision-makers in selecting and implementing AI solutions strategically across the SCRM lifecycle. It offers actionable insights to strengthen resilience, minimize disruptions, and enhance competitiveness in increasingly volatile global supply chains.