Artificial intelligence is advancing rapidly and drawing wide attention. However, bringing AI into the real world remains a major challenge. Real-world data (RWD) typically exhibits multi-source heterogeneity, scarcity, and imbalance, usually with missingness, noise, biases, and limited labels, and constraints from privacy, compliance, and operations. These realities make reliability and maintainability central concerns. The First International Workshop on Artificial Intelligence for Real-world Challenges takes RWD as its starting point and focuses on how to build, deploy, monitor, and continually improve AI systems in real-world scenarios. We welcome diverse approaches to these challenges, including few-/zero-shot learning, data-centric AI, continual learning, etc. We also invite knowledge-driven methods, as well as retrieval-augmented generation and large language models (LLMs), and agent theory and applications that operate under real-world constraints. The workshop provides an interdisciplinary forum for researchers and practitioners worldwide to share cutting-edge research, innovative methods, and practical experience. Through keynotes, technical presentations, and panel discussions, it will foster interaction between AI researchers and domain experts, cultivate long-term collaborations, and inspire new research directions toward sustainable, impactful AI in real environments.