The field of applied generative AI (GenAI) for enterprises is advancing with methods like GraphRAG and agentic search, offering improved accuracy and depth in data retrieval. GraphRAG enhances data reliability by using knowledge graphs that define explicit pathways among entities, reducing ambiguity and increasing control over data. This method is particularly beneficial for sectors requiring high accuracy and traceability, such as finance and healthcare, although it involves higher costs and maintenance efforts. Conversely, agentic search employs AI agents for iterative searches, dynamically connecting scattered or ambiguous information without needing a pre-built database, which makes it more flexible and faster to implement. While this method provides richer context and understanding, it can incur higher computation costs and potential latency issues. Both approaches reflect the evolving landscape of enterprise GenAI, promising transformative applications across various industries while emphasizing the importance of staying updated with emerging retrieval systems to maximize potential benefits.