AI Governance Model
Purpose
This document describes how a company governs AI initiatives so that they deliver impact, do not create uncontrolled risk, and follow a clear path from idea to operation.
The AI governance model is embedded in the conveyor: checks of data, security, architecture, risks, and impact are performed not separately from the process, but at the stage gates of an initiative.
Core ideas
- Governance starts at the idea stage. If security, data, and architecture are only brought in right before launch, the conveyor turns into a factory of late blockers.
- Risk should shape the route. Simple automation and a model that drives customer-facing decisions require different levels of control.
- Not everything is decided by a committee. Simple checks should be expressed as platform rules, while contested decisions should be escalated to accountable owners.
- The decision trail matters as much as the decision itself. For an audit you need to understand who let an initiative move forward, on what basis, and with what risk.
- Impact is part of governance. An initiative without a confirmed result should not be considered successful merely because the solution has been launched.
How it works
The AI governance model consists of five loops:
| Loop | What it controls | Where it appears in the conveyor |
|---|---|---|
| Decisions | who moves an initiative forward and why | stage gates, transition history, rejection reasons |
| Data | sources, quality, access, confidentiality | assessment, delivery, data owner sign-offs |
| AI risks | result quality, reliability, reproducibility, human in the loop | delivery, awaiting impact, support |
| Architecture | integrations, operation, fault tolerance, observability | delivery and launch |
| Impact | baseline metrics, calculation methodology, result confirmation | assessment and awaiting impact |
Related sections: stage gate model, decision framework, data governance, AI risks, architecture governance, value realization.
Initiative classes by level of control
Not all initiatives require the same weight of governance.
| Class | Example | Minimum control |
|---|---|---|
| Low risk | an internal knowledge-search assistant without sensitive data | owner, product, basic data and impact checks |
| Medium risk | automation of an internal process involving personal data | access check, security, architecture, support plan |
| High risk | a solution affecting customers, money, risk, compliance, or legally significant actions | extended sign-off, independent review, decision log, human in the loop |
The level of control is set at the assessment stage and revisited before delivery. Stage-by-stage checks in the funnel are in the stage gate model; details for each loop are in the related sections above.
What counts as good governance
Good AI governance:
- accelerates strong initiatives;
- stops weak ones early;
- makes risks visible before delivery;
- does not require excessive sign-offs for low risk;
- records decision owners;
- ties launch to measurable impact;
- leaves a clear trail for audit.
Bad AI governance:
- turns every initiative into a committee;
- demands documents that have no effect on the decision;
- blocks delivery at the last moment;
- fails to distinguish low risk from high risk;
- does not know which initiatives actually delivered impact.