Generative AI systems hold significant transformative potential for company operations but are heavily reliant on the quality and accessibility of data they use. Organizations face challenges in connecting to and retrieving data that varies in quality, format, and structure, which is crucial for maximizing the benefits of AI systems like retrieval-augmented generation (RAG). Establishing clear use cases for RAG systems and tackling data quality issues through preprocessing and cleaning are vital steps in overcoming these challenges, as poor data quality can undermine the effectiveness and trustworthiness of AI models. Solutions such as embeddings, data connectors, and reranking can help improve data retrieval and interpretation. Managing data stored in various formats and locations requires careful consideration of data connectors and metadata enrichment, which enhance the precision and relevance of AI-generated responses. Additionally, balancing data costs at scale involves optimizing retrieval systems and employing strategies like compressed embeddings and chunking to reduce computational and storage demands. Continuous refinement of data handling and infrastructure is essential for organizations seeking to leverage AI effectively and align with strategic goals.