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: 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 should be determined at the assessment stage and revisited before delivery.
Minimum checks by stage
New
Checked:
- whether there is a clear business problem;
- whether there is an initiator;
- whether the expected impact can be articulated;
- whether the idea is an obvious duplicate.
Assessment
Checked:
- whether a suitable AI product has been chosen;
- whether the data is available;
- whether there are preliminary security constraints;
- whether the owner of the future impact is clear;
- whether an extended risk review is needed.
Delivery
Checked:
- whether the product's requirements are met;
- whether data and access have been agreed;
- whether there is an architectural solution;
- whether the operating mode is clear;
- whether a human in the loop is defined, if the solution affects significant actions.
Awaiting impact
Checked:
- whether the review date has arrived;
- whether actual data is available;
- whether the impact is confirmed;
- whether new risks have emerged after launch.
Support
Checked:
- who maintains the solution;
- how quality is tracked;
- when the solution is reviewed;
- what the condition for stopping or rolling back is.
Connection to the platform
The platform supports the governance model through:
- roles and access rights;
- the business funnel and delivery tracks;
- configurable transition rules;
- mandatory fields;
- checks for similar initiatives;
- preliminary security review;
- tasks and accountable owners;
- analytics on risky initiatives;
- an AI assistant that helps assemble the brief, estimate impact, prepare documents, and suggest the next move.
In a mature setup, the AI assistant must not bypass governance rules. If an agent creates an initiative, advances a stage, or selects a product, that action must be subject to the same rights, rules, and audit as a user's action.
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.