A Practical AI Policy for Growing Businesses

A practical AI policy is a short, plain-English document that says which AI tools your people may use, what information must never go into them, how a new use gets approved, and who is accountable when the output is wrong. For a growing Australian business it does not need to be a comprehensive legal document. It needs to be read, understood and followed, and it needs an owner.

Why you need one now, not later

Your staff are already using AI. Someone in sales is drafting proposals with it, someone in finance is summarising contracts, and someone has pasted a customer list into a free tool to "tidy it up". None of this is malicious. It is people doing their jobs faster with whatever is in front of them. Without a policy, the real policy is whatever each person decides at their desk, and nobody can tell you what information has already left the building.

The obligations have not waited either. The Privacy Act still applies to personal information whether it sits in a spreadsheet or a chat window, and the National AI Centre has published practical guidance on what responsible use looks like. Customers, insurers and tender panels are starting to ask the question directly. A one-page answer beats an awkward silence.

What a practical policy actually covers

Most AI policies fail by trying to cover everything. The useful ones cover five things and stop:

• Approved tools: which AI products staff may use for work, and which are off limits. Keep the list in a separate procedure so it can change without re-approving the policy.
• The data rule: what must never be entered into an AI tool. Customer data, personal information, commercially sensitive material and anything under a confidentiality obligation.
• Approval for new uses: how someone proposes a new way of using AI, who says yes, and what has to be true before it goes into a real process.
• Human accountability: a named person checks AI output before it goes to a customer, into a decision, or into the books. The tool is never the one responsible.
• Transparency: when customers and staff are told AI was involved, and who they can ask about it.

Everything else, from prompt tips to vendor settings, belongs somewhere other than the policy.

Keep it to one page

A policy is only as good as the number of people who can repeat its rules without looking them up. A twelve-page document, drafted from a template and signed during onboarding, protects nobody. A single page that every manager can explain in two minutes changes behaviour. You get a policy people follow, not one they signed and forgot.

A policy nobody reads is not a policy. It is a liability with a signature on it.

The test is simple. Hand it to a new starter, give them five minutes, and ask them what they are allowed to do with a customer's email in an AI tool. If they cannot answer, the policy is too long or too vague.

The data rule is the one that matters most

If you only get one thing right, make it this. Public AI tools are built to learn from what goes into them, and their free tiers are rarely designed with your confidentiality obligations in mind. The practical line is clear: no customer data, personal information or commercially sensitive material goes into a public tool. Work that genuinely needs that information moves to an approved tool whose contract keeps your data out of the provider's training and inside the right jurisdiction.

This is also where the policy earns its keep with your lawyers and insurers. A documented data rule, a list of approved tools and a record of who approved what is the difference between "we take AI seriously" and being able to show it.

Give it an owner and a review date

A policy without an owner drifts within months, because the tools change faster than any document. Someone has to own it: approve new uses, keep the tool list current, watch the regulatory picture and report to the board or leadership team on what is actually happening. In a growing organisation this often sits naturally with a fractional CIO, who already carries governance and vendor accountability. A fractional CTO tends to own the build side, deciding which AI capabilities go into your own products and platforms, with the policy setting the boundaries for both.

If you are starting from nothing, an AI Governance & Compliance Strategy engagement assesses where you stand against Australian obligations and gives you the policy and the roadmap in one pass. Once it exists, AI Governance Advisory keeps it current and reported, so the document does not quietly age into irrelevance.

The bottom line

A practical AI policy is one page, five rules and one accountable owner. It names the tools, draws a hard line around your data, sets a path for approving new uses, keeps a human responsible for every output, and says when people are told. You get staff who use AI confidently inside clear limits, not a quiet free-for-all you discover after the fact.

Frequently asked questions