An AI Governance Checklist for Business Owners

Put ownership, permissions, testing, monitoring, and staff training around business AI. A practical checklist connected to the G.A.M.E. framework.

AI governance means deciding who is accountable for an AI system, what it may do, how its results are checked, and when a person must intervene. A practical starting point is an inventory of your systems, a named owner for each one, explicit permissions, and a repeatable review process. The policy matters when it changes what happens during real work.

Start with an inventory of actual use

List the AI tools employees already use, including features inside existing software. Record the business purpose, users, connected systems, information involved, and whether outputs reach customers. A writing assistant with public information and a tool that changes customer records should not receive identical treatment.

Talk to the people doing the work. Ask which tasks they use AI for and what they do when an answer looks wrong. Make it straightforward to report uncertain uses. An inventory built only from software invoices can miss informal workflows and built-in features that affect the business.

Apply the four G.A.M.E. questions

Governing the AI Machine, co-authored by Billy Sticker and Lenon Magwenjere, presents G.A.M.E. as Guardrails, Authority, Monitoring, and Enablement. The checklist below applies those four headings to an operating discussion; it is not a certification or a substitute for requirements specific to your organization.

About Governing the AI Machine and the G.A.M.E. framework

Guardrails: what are the boundaries?

• Identify which information the system may use and which information must stay out.
• Separate permitted reading, drafting, sending, and record-changing actions.
• Define prohibited uses and an escalation path for requests outside the approved purpose.

Write the boundary as an instruction someone can follow. For example: the assistant may prepare a response from approved policy documents, but a staff member must approve it before sending. Broad instructions such as use AI responsibly leave too much interpretation at the moment of action.

Authority: who owns the decision?

• Name the business owner, technical contact, and person who approves material changes.
• Specify who can grant access, pause the system, and authorize a restart.
• Keep approval responsibility with a person whose role includes understanding the outcome.

An approval step is useful only if the reviewer has enough context and time. Show the source material, the proposed action, and any unresolved questions together. A queue of unexplained approve buttons can create the appearance of oversight without meaningful review.

Monitoring: how will you know it is working?

• Test normal requests, exceptions, ambiguous inputs, and attempted out-of-scope actions.
• Track quality, corrections, escalations, access failures, and completed outcomes.
• Review again when models, prompts, integrations, policies, or source documents change.

Decide what evidence to retain and who can see it. Logs may contain sensitive information, so retain only what the review requires under your organization's policies. Define an incident process that includes stopping affected actions, preserving appropriate evidence, correcting the result, and reviewing the cause.

Enablement: can the team use it well?

• Train people on approved tasks, limits, review duties, and how to ask for help.
• Make current instructions easy to find inside the working process.
• Allocate time for maintenance and learning after the launch.

Use a recognized risk framework as a reference

NIST's AI Risk Management Framework and companion Playbook provide voluntary guidance organized around Govern, Map, Measure, and Manage. They are a separate framework from G.A.M.E. and can help teams structure a more detailed risk assessment. Neither following a checklist nor citing a framework establishes legal compliance.

Reference: NIST AI RMF Playbook

The first review meeting

Bring one real workflow to the meeting. Walk through its purpose, information, permissions, evaluation results, and failure procedure. Leave with a named owner and a dated list of unresolved issues. Repeat at an interval matched to the risk and pace of change. The goal is a system the business can explain, supervise, and improve.

Choose a manageable first AI pilot

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