1
Assess
Readiness Audit
Inventory what exists: dashboards, definitions, lineage, trust. Map where AI helps now and where it will hallucinate. Two weeks to an honest answer.
Approach
AI doesn’t fail at the model. It fails at the layer beneath it.
Ask an LLM a business question and it will answer — fluently, confidently, and from whatever definitions it happened to find. The method below exists so that what it finds is governed.
4 stages·assess → govern → deploy → sustain·evaluation is the deliverable
The architecture
Three layers. One of them is usually missing.
Layer 3
AI Systems
Copilots · workflow automation · executive briefings
Reliable only because of what's beneath
Layer 2
The Governed Layer
Metric registry · glossary · data dictionary · ownership · lineage · trust scoring · evaluation
The layer most companies skip
Layer 1
Data Foundations
Warehouse · models · pipelines · BI tools
Necessary. Not sufficient.
This diagram runs. The Working Model is the same architecture as a live instrument — governed layer on, governed layer off, same question.
Without the governed layer
- The copilot answers from ambiguity — confidently
- Two dashboards disagree; the AI picks one at random
- Nobody can say why a number moved, including the AI
- The pilot demos well and quietly dies in production
With it
- Answers cite approved definitions and their owners
- “Cannot answer safely” is a designed behavior, not a failure
- Every AI response is evaluated against a test harness
- Executives act on the answer without calling an analyst first
The method
Assess. Govern. Deploy. Sustain.
1
Assess
Readiness Audit
Inventory what exists: dashboards, definitions, lineage, trust. Map where AI helps now and where it will hallucinate. Two weeks to an honest answer.
2
Govern
Knowledge Layer
Build the layer most companies skip: metric registry, glossary, data dictionary, ownership, lineage, definitions-as-code, trust scoring. This is what makes AI reliable.
3
Deploy
Copilot & Automation
Only now: grounded AI answers, analyst workflow automation, executive briefings — each with guardrails, human review, and an evaluation harness that proves behavior.
4
Sustain
Enablement & Fractional Architect
Teams trained on production-grade AI patterns. A standing senior architect keeping governance, roadmap, and builds honest as the system grows.
Operating principles
Governance before generation
We won't ship an AI system on a layer we haven't governed. That order is the method — reversing it is how pilots become incidents.
Evidence over assertion
Every AI system leaves with an evaluation set and a test harness. If we can't measure that it answers correctly, we don't call it done.
Humans stay in the loop
Review workflows, escalation paths, and “cannot answer safely” behaviors are designed in — not bolted on after the first bad answer.
Your team gets faster, not smaller
The goal is analysts doing analysis instead of serving as human APIs. Enablement is part of every build.
LLMs don't fix messy metrics. They amplify them.
That's why the middle layer exists. Amplification is only a gift when what's amplified is governed.
The method starts with a two-week honest answer.
The Readiness Audit maps your environment against this architecture — and tells you exactly where AI helps now versus where it will hallucinate.