What does practical AI governance actually look like?

What is happening

Many organizations respond to AI with either a one-page prohibition or a lengthy framework nobody operates. Neither changes behavior. What changes behavior is a small number of clear rules attached to a working process.

The working parts

1. An inventory

A maintained list of AI tools and AI-enabled features in use, what business process each supports, what data it touches, who owns it, and when it was last reviewed. Without this, every other control is theoretical.

2. Roles and decision rights

• Who approves a new AI use case, and at what risk tier.
• Who owns the data classification decision.
• Who reviews vendor terms, including training-data and retention clauses.
• Who is accountable for an output that reaches a customer.

3. An acceptable-use standard people can follow

Written in the language employees use. It should state what data may never be entered, what uses require review, what uses are pre-approved, and the expectation that a human remains accountable for any output that is acted on.

4. Risk-tiered review

Drafting an internal summary is not the same risk as screening applicants or generating customer-facing statements. Tier by impact, data sensitivity, and whether a human decision sits between the output and the consequence. Reserve heavy review for the top tier.

5. Monitoring and change

Vendors change models and terms without notice. Governance needs a periodic re-review, an incident route for AI-related issues, and a way to retire a tool.

What not to do

• Do not copy a framework wholesale without mapping it to your actual decisions and roles.
• Do not create an approval process with no service level — slow governance is bypassed governance.
• Do not place accountability with a committee that meets monthly for decisions that occur weekly.
• Do not write policy that assumes AI only enters through procurement.

Questions to ask

• If an employee wants to use a new AI tool tomorrow, what exactly do they do?
• How long does approval take, and who is on the critical path?
• What is our list of AI in use, and when was it last accurate?
• Which uses could affect a person's employment, credit, care, or legal position — and what review applies to those?
• What contractual commitments have we made to clients about AI use?

Warning signs

• The policy exists but the inventory does not.
• Approvals happen by email with no record.
• Teams describe governance as something to work around.
• No one has reviewed vendor terms for data retention or model training.

When internal teams can handle it

Organizations with mature risk and security governance can usually extend existing structures to AI rather than building something separate — which is generally the better path.

When outside help may be appropriate

When governance must be established quickly across functions that do not currently coordinate, when there is regulatory or contractual exposure, or when previous policy attempts have not changed practice.