MPBP Framework — Operationalizing AI Governance

PATTERN 01

Map: What Already Exists

Before building governance, understand what you're governing. Identify which AI tools are already in use, which decisions they support, and what data they access. This creates the foundation everything else builds on.

In practice Conduct an inventory of current and planned AI use across each department — including vendor-provided tools with AI capabilities that may not be labeled as "AI."

Without mapping: Frameworks get built for theoretical scenarios while real AI use grows ungoverned.

PATTERN 02

Prioritize: By Impact

Not all AI applications carry the same risk. An AI tool that automates scheduling is fundamentally different from one that supports eligibility determinations. Prioritize which use cases need robust governance first.

In practice A three-tier classification: routine automation (lightweight governance), decision support (moderate governance), and citizen- or customer-facing decisions (robust governance across all principles).

Without prioritization: Governance becomes so comprehensive that nothing gets implemented — driving agencies to adopt AI informally.

PATTERN 03

Build: On Tested Frameworks

NIST AI RMF, OECD AI Principles, and the EU AI Act risk classification have all been tested at scale. They offer structures, terminology, and assessment methods that can be adapted — freeing energy for jurisdiction-specific questions no external framework can answer.

In practice Benchmark your governance pillars against OECD principles and NIST AI RMF functions. Adapt internationally tested structures while focusing original effort on the organization-specific questions no external framework can answer.

Without leverage: Teams reinvent what others have already solved, consuming time on solved problems instead of organization-specific ones.

PATTERN 04

Pilot: Govern Around Real Deployments

Governance frameworks developed in isolation almost always require significant revision once they meet actual workflows. A framework developed alongside a real pilot deployment embeds practical wisdom from the start.

In practice Each department identifies one well-scoped AI pilot — a practical deployment where the governance framework is built and tested in real time, producing evidence alongside documentation.

Without pilots: Frameworks don't survive contact with reality — producing documents that sit on a shelf instead of governance that works.