The enterprise AI boom is characterized by companies eagerly adopting LLM copilots and agentic workflows, but many face challenges due to outdated data infrastructures, inconsistent data, and a lack of readiness for scalable AI applications. The TDWI Data Points Report highlights that only a small percentage of organizations possess a data architecture capable of supporting multiple AI applications, while most are bogged down by manual data processes, leading to ineffective AI projects that are not scalable or governed. The pressure to implement AI remains high, with over 80% of companies using or experimenting with AI, though only a fraction are effectively integrating generative AI with their proprietary data. This lack of integration results in "shadow AI" projects that often produce unreliable outputs and damage user trust. Achieving AI readiness involves automating data integration, ensuring data quality and standardization, and building a unified analytics platform that supports real-time data processing for both structured and unstructured data. Fivetran offers a solution by providing automated data movement and integration tools to help organizations focus on developing AI-enabled capabilities rather than maintaining data pipelines.