AI projects don't fail because of algorithms or technology choices; they fail because their data is in no shape for AI, often resulting in "garbage in, danger out"! At Swisscom, we invested in data architecture, management, and the operationalization of our data foundation to enable AI readiness at scale. We have worked on governance patterns that accelerate rather than block AI initiatives, and we have created autonomous, interoperable data products that significantly reduce AI implementation time through automation. This session will cover Swisscom's specific data management practices that transformed our legacy data landscape into a foundation that supports AI, as well as how we implemented data mesh with people (and AI) for people!