How poor data foundations can undermine AI success

The excerpt advises organizations on managing data privacy and access when adopting AI and large language models, recommending human-in-the-loop validation, careful consideration of where LLM processing occurs, use of smaller models or constrained prompts to reduce data exposure, and treating AI data readiness as an ongoing governance discipline to ensure secure, auditable, and interoperable data use.