Enterprise AI Architecture Standards: Architecting AI For Global Enterprises

Three real production failures — multi-channel retail, property & life insurance, and automotive. Not toy demos, not hypotheticals.

For each: what the system was meant to do, where the architecture missed, and why the model itself was never the root cause.

Then the counterfactual — which principles, applied at design time, would have caught the failure before it shipped.

A principle is a guideline at its core — a rule, not an implementation. What makes it enforceable is the scaffolding built around it.

That scaffolding — rubric, gates, evidence requirements, reference implementation — is what an ARB and a CI pipeline can actually act on.

The workshop covers that anatomy and how to apply it in planning, design reviews, and engineering — for RAG, prompts, evals, and agents.

Building a standards catalogue from scratch is an 18-month detour. Standing on AWS GenAI Lens gives you a credible spine on day one.

Each standard is written platform-agnostic — the same spec ships on AWS, Azure, GCP, or self-hosted. Lens is the anchor, not the cage.

Each standard adds the specificity Lens stops short of — concrete spec, rubric, gates — so reviewers can actually decide pass/fail.

Architecture Review Board process — a gated design review that uses the rubric to score a design before any code is written.

Automated gates at the code layer — pre-commit hooks, CI/CD checks, and AI-based code scanners flag principle violations before merge.

A decision framework for sequencing principle adoption — failure-mode mapping, regulatory must-haves, and an effort × impact lens.