What the client receives.

Direct answer

Before approving internal generative AI use, the board should require a defined governance process, not just an acceptable-use statement. At minimum, management should establish an AI use-case register, risk classification, accountable owners, data-use rules, vendor review, staff guidance, monitoring, incident escalation, and periodic reporting to the board or risk committee. Approval without these structures transfers accountability to the board without the operational controls to discharge it.

Assumptions

• No formal AI governance framework is currently in place.
• Staff are using, or intend to use, commercially available generative AI tools such as Microsoft Copilot, ChatGPT, Google Gemini, or equivalent.
• The question relates to internal productivity use, not customer-facing, clinical, financial-advice, or regulated AI deployment.
• Australian jurisdiction applies; requirements may differ materially in other regulatory environments or sectors.

Evidence basis

The recommendation is grounded in recognised AI governance and risk-management frameworks addressing accountability, risk classification, data governance, human oversight, monitoring, transparency, and continual improvement.

Governance analysis

The primary governance risk is uncontrolled adoption. Without a formal approval path, the organisation loses visibility over which tools are used, what data is entered, which outputs are relied upon, and who is accountable for errors. Before approving any deployment, the board should require management to demonstrate that AI use is visible, owned, classified, monitored, and escalated as risk increases.

Recommended board conditions before approval

1. Create an AI use-case register covering tool name, business owner, purpose, data type, user group, risk level, approval status, and review date.
2. Classify use cases by risk so low-risk productivity use is separated from high-impact, customer-facing, rights-impacting, or regulated use.
3. Assign accountable owners for each approved use case, including business ownership, technology ownership, risk oversight, and escalation responsibilities.
4. Define data-use rules that restrict confidential, personal, privileged, commercially sensitive, or regulated data from being entered into unapproved tools.
5. Review vendors and tool settings including data retention, training-on-inputs, access control, audit logs, contractual terms, and security posture.
6. Issue staff guidance and training covering acceptable use, verification of outputs, prohibited uses, privacy expectations, and responsibility for final work product.
7. Require human oversight so AI outputs are not treated as final advice, final decisions, or approved organisational positions without review by an accountable person.
8. Establish incident and escalation triggers for data leakage, harmful output, hallucinated content, inappropriate reliance, or policy breach, each with a named escalation path.
9. Set board reporting metrics such as number of approved use cases, high-risk use cases, incidents, exceptions, training completion, vendor status, and overdue reviews.

Implementation considerations

• Risk classification should distinguish productivity tools (low risk) from tools used for customer communications, regulated outputs, or rights-impacting decisions (high risk). Two tiers are sufficient to start; complexity can be added once the register is operational.
• The AI use-case register does not require a system. A controlled spreadsheet with defined fields (tool name, owner, purpose, data type, risk level, approval status) is enough for an initial governance baseline, and it can be migrated to a proper system later.
• Staff training should be completed before access is formally approved, not deployed after an incident prompts a retrospective review.
• Vendor governance reviews should be conducted at procurement stage. Reviewing data retention, training-on-inputs, and security posture before contract provides more leverage than after the tool is deployed.
• Board reporting metrics should be defined before the governance process launches. Retrospectively constructing what to report limits the board's ability to identify trends and escalate early-stage concerns.

Limitations

This is general AI governance guidance. It is not legal advice, privacy advice, cybersecurity incident-response advice, or a certified compliance opinion. The final control design should be adjusted for jurisdiction, sector, existing policies, data sensitivity, vendor contracts, and organisational risk appetite.

Confidence

High. The answer is within scope and the recommended controls align with multiple recognised AI governance, risk-management, and responsible AI frameworks. Confidence would reduce if the proposed use involved regulated decisions, sensitive personal information, safety-critical systems, or insufficient vendor transparency.