Practical, vendor-neutral thinking on enterprise architecture, SAP, integration, data, business architecture and Enterprise AI. The focus is not technology for its own sake. It is the quality of the decisions that technology forces an enterprise to make.
Why enterprises need to move beyond storing and transporting data toward preserving business meaning, context and decision relevance.
A standard platform can reduce complexity, but architecture still has to decide boundaries, exceptions, lifecycle ownership and where native capability is materially better.
Enterprise AI succeeds or fails on data, semantics, integration, governance, operating model and architectural discipline long before model selection becomes the main issue.