Operating models

Operating model

The Usable Decision-Rights Model for AI

A governance operating model that gives executives usable decision rights over AI adoption rather than adding abstract policy layers: establishing structured intake, clarifying accountability, connecting AI use cases to data quality requirements, and maintaining explicit human control points throughout.

The model

Step by step

The sequence below is the operating model in practice. Each step is designed to be executed — not aspirational.

  1. Establish structured intake

    Create a repeatable process for evaluating AI use cases that clarifies the problem, the data involved, the accountability structure, and the evidence expectations before adoption proceeds.

  2. Classify by risk tier

    Separate advisory, operational, customer-facing, and control-affecting use cases into appropriate risk buckets so governance effort matches the decision stakes.

  3. Connect to data quality

    Link AI adoption decisions to the quality and control status of the underlying data, ensuring governance extends to the foundations rather than stopping at the model layer.

  4. Maintain human control points

    Define explicit human decision points, escalation routes, and evaluation criteria so accountability remains clear as AI use cases mature.

Evidence

Where this model has been applied

Each case study below shows the framework in a real operating context.

2026-04-15 / case study

AI governance working group operating model

A case study on creating practical AI governance rhythms for use-case intake, executive oversight, and regulated adoption.

Read case study