As AI systems increasingly rely on external memory to augment reasoning, Retrieval-Augmented Generation (RAG) has become the default approach for enhancing large language models (LLMs). However, RAG falls short in tasks requiring conceptual understanding, meaning-based association, and scalable retrieval. In this article, we argue that while RAG offers surface-level similarity, it lacks the structured, layered memory organization that human cognition relies on. We introduce SHIMI (Semantic Hierarchical Memory Index)—a retrieval framework inspired by human memory that organizes knowledge into semantic hierarchies. SHIMI enables faster, cheaper, and more accurate retrieval by narrowing search paths based on meaning, not keywords. This marks a shift toward AI systems that think and remember more like humans—structured, abstract, and context-aware.