AI projects often fail because data teams lack modern integration foundations, with 80% never reaching production. Success requires centralized, clean, compliant, and fresh data from diverse sources, supporting structured and unstructured formats. Traditional AI relies on predictive modeling, GenAI uses retrieval-augmented generation with vector databases, and agentic AI adds automation to execute workflows. Best practices include ELT over ETL, automating pipelines, governing access, and prompt engineering to improve outputs. Companies like Sennder, HubSpot, and NAB demonstrate AI value when supported by automated integration. Fivetran enables this with no-code pipelines, dbt packages, and RAG-ready data.

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