Practical perspectives on enterprise AI — strategy, agentic & RAG platforms, governance, and getting from pilot to production.
AI isn’t just another component in enterprise architecture — it changes what software is. Why applications, documents, and code may become implementation details.
STRATEGY“Should we train our own model?” is usually the wrong question. The value lives in retrieval, tools, and human-in-the-loop — not in custom weights.
PLATFORMSThe structural pillars govern how code connects. AI-native systems need a second, behavioral layer to stay trustworthy as they change.
PLATFORMSThe pillars still hold after two decades — but the security model, trust boundaries, and execution environment beneath them have changed completely.
GOVERNANCEThe hardest AI privacy problems show up after launch — not in the model, but in the plumbing around it.
GOVERNANCEYou rarely get burned because the model failed — but because you assumed intelligence alone was enough. Four controls that keep AI fast and the business in control.
Seven layers, structural and behavioral first principles, and six governance domains — a vendor-neutral blueprint for trustworthy, enterprise-ready AI.
CHECKLISTPressure-test whether your AI is safe to put into production across seven control areas.
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