Data scarcity presents a significant challenge in artificial intelligence implementation across industries, constraining organizations from deploying effective machine learning solutions. This article explores strategic approaches that transform limited datasets from barriers into competitive advantages through methodological innovation. By examining transfer learning mechanisms that leverage pre-existing knowledge, data augmentation techniques that artificially expand available examples, few-shot and zero-shot learning paradigms that function with minimal labeled instances, and active learning strategies that optimize annotation resource allocation, a framework emerges for maximizing model performance under severe data constraints. These complementary strategies, when thoughtfully integrated, enable high-accuracy AI models in domains previously considered impractical due to insufficient training data. The economic, regulatory, and practical implications extend beyond technical performance enhancement to fundamentally alter the feasibility landscape of AI adoption, particularly in specialized domains where data collection faces inherent limitations such as healthcare, manufacturing, and low-resource languages.
Keywords: active learning, data augmentation, few-shot learning, minimal data learning, transfer learning
