AI pilots are easy to start and hard to scale. This article looks at why the architecture beneath AI — data models, metadata, access and lineage — determines whether AI can be trusted in production.
This article is in preparation. The full perspective will be published here soon. In the meantime, we are glad to discuss the topic directly.
“AI-ready” is used loosely. We outline the practical characteristics — quality, context, ownership, accessibility and governance — that make enterprise data usable by AI systems.
Functional design gets the attention, but data often decides the timeline. A look at the data challenges that surface late in S/4HANA programs and how to address them early.
An executive-level view of where Microsoft Fabric fits in an enterprise data strategy, the architectural patterns it supports and the governance questions to settle before scaling it.
Tell us what you are working on and we will share a practical perspective.