Enterprise AI Architecture
Building trustworthy enterprise AI requires business context, evaluation, governance, and workflows that connect answers to decisions.
Beyond the technology.
Begin with the decision
A useful enterprise AI project begins with a decision or workflow, not a model selection. Who needs an answer, what will they do with it, and what would a wrong answer cost?
Make context available
Enterprise knowledge is fragmented across systems, documents, definitions, and people. AI agents need the right context, permissions, and evidence to produce answers people can trust.
Evaluate the whole system
Test factual accuracy, authorization, latency, cost, and how the AI experience supports actual decisions. A compelling demo is not proof of a dependable production system.
Explore related thinking in my selected essays ↗.
Semantic Layers & Enterprise Ontology ↗
A consistent business vocabulary connects analytics, applications, and AI agents to the same trusted enterprise meaning.
Modern Data Architecture ↗
Open and governed data foundations should help organizations adapt to new workloads while improving how people work with data.