An AI governance policy should help useful AI move safely. If it only says "be careful with AI", it will not guide decisions. If it requires every low-risk use case to go through the same committee as a high-risk customer or employee decision, people will route around it.
The useful policy is practical. It tells teams which AI uses are allowed, which need review, which are prohibited, who owns the decision, what evidence must be kept, and how the organisation monitors the system after it goes live.
For ExIQ, AI governance sits beside AI strategy and advisory because the policy should reflect the organisation's actual roadmap. Governance should not be a document written after experimentation has already spread beyond control.
Start with accountability, not wording
The first policy question is who is accountable. Australian guidance points strongly toward clear ownership, capability, risk management, data governance, transparency, human oversight, and ongoing monitoring. Those ideas only become operational when they are translated into roles and decisions.
A practical policy names an executive sponsor, a policy owner, use-case business owners, technical owners, risk or privacy reviewers, and the approval pathway for each risk tier. It also states who can pause, change, or retire an AI use case if the operating evidence becomes weak.
The policy should require a use-case register
A use-case register is the operating backbone of AI governance. It prevents the organisation from losing track of small tools, pilots, embedded vendor features, automations, and agent workflows that may otherwise spread quietly across teams.
Each entry should describe the business purpose, user group, data used, system or vendor involved, risk tier, human oversight, approval owner, launch date, monitoring rhythm, known limitations, and current status.
- Low-risk entries might include internal summarisation, drafting, meeting notes, or knowledge retrieval with no sensitive decision impact.
- Medium-risk entries might include customer service drafting, document extraction, workflow routing, staff triage, or reporting support.
- High-risk entries might include decisions affecting access, eligibility, pricing, safety, employment, health, finance, legal exposure, or vulnerable people.
Use risk tiers instead of one approval path
The most common governance failure is treating every AI use the same. That slows safe low-risk work and under-controls high-risk work. Risk tiers allow the organisation to move with proportionate control.
Low-risk use cases may need user guidance, approved tools, data rules, and manager acknowledgement. Medium-risk use cases may need a workflow review, privacy check, test evidence, human review rules, and monitoring. High-risk use cases need deeper assessment, executive approval, legal or risk review, auditability, and a clear escalation and remediation pathway.
A policy needs vendor and embedded AI review
Many organisations do not build their own AI systems first. They inherit AI through SaaS products, productivity tools, CRMs, service platforms, analytics tools, contact-centre systems, and automation suites. The policy has to cover those embedded features too.
Vendor review should ask what data is processed, where it is stored, whether customer or employee information is used for training, how outputs are generated, what audit logs exist, how the feature can be disabled, and what contractual responsibilities sit with the vendor.
The minimum policy structure
- Purpose: why the organisation uses AI and what outcomes it is trying to improve.
- Scope: which AI tools, embedded features, automations, agents, and third-party systems are covered.
- Roles: accountable executive, policy owner, use-case owner, technical owner, risk/privacy reviewer, and approver.
- Allowed, restricted, and prohibited uses: clear examples people can understand without legal interpretation.
- Risk tiers and approval paths: practical thresholds for low, medium, and high-risk use.
- Data and privacy rules: what information can be used, protected, retained, or excluded.
- Human oversight: what people must review, approve, challenge, or escalate.
- Testing and monitoring: evidence required before launch and review rhythm after launch.
- Records: use-case register, decision log, vendor review, testing evidence, incidents, and changes.
Governance should create a monthly operating rhythm
A policy that is never reviewed is a PDF, not governance. The operating rhythm can be simple: review new use cases, approve or reject risk tiers, monitor live use cases, look at incidents or near misses, review vendor changes, and decide what needs to be scaled, paused, or retired.
That rhythm is where AI strategy becomes real. It turns scattered enthusiasm into a managed portfolio of use cases, each with value, ownership, risk control, and a decision about what happens next.