AI governance: can you demonstrate what your organisation is doing?

When a customer, board or procurement team asks how your organisation manages artificial intelligence, an AI policy is only as good as the evidence you can provide. You need to explain where AI is used, who is responsible and what supports the decisions being made.

Consider a team using AI to prepare customer communications. Who approved the tool? Does the team know what information it can enter? Who checks the response before it goes to a customer?

These are pretty reasonable questions and answering them should be part of how the organisation manages its data to day work.

AI governance, using frameworks or agreed processes gives your AI supported work structure and human oversight. It connects the organisation’s expectations with what people do each day and gives leaders a way to identify gaps, assign responsibility and keep humans in control.

1. Start with how people actually use AI

A useful starting point is to find out where AI is already being used.

That includes tools the organisation has purchased, AI features within existing software and public tools staff may use to help complete their work. Looking only at formally approved systems can leave an incomplete picture - not everyone knows they are using an AI powered product.

Speak with the people doing the work. Find out what they use, what information they put into those systems and what happens to the output. An employee using AI to draft an internal agenda raises different questions from a team using it to help shortlist job applicants.

The level of review should reflect those differences.

A simple register can help bring the information together. For each use, record:

• What the tool is used for and by which team.

• What information it receives.

• Whether its output influences a decision or is shared externally.

• Who is responsible for its use and checks the output.

• What needs further attention and when it will be reviewed.

Choose the right tools to make it manageable. Someone needs to maintain the register and use it when decisions are made.

2, Give people clear responsibility

An AI policy might say that staff must use approved tools and check their outputs. People still need to know who approves a tool, what a suitable check involves and where to take a concern.

For the customer communications example, a team manager might oversee everyday use. The person responsible for privacy might review how customer information is handled. Someone with the appropriate authority needs to decide whether any remaining risk is acceptable.

Those responsibilities should be clear to everyone involved.

They also need to survive a change in staff. If the only record of an approval is someone’s recollection of a conversation, the next person has little or nothing to work with.

Recording the decision, the reasons and any conditions gives the organisation something it can refer to later.

3. Understand what compliance requires in your circumstances

AI governance supports compliance by helping an organisation identify its obligations, put controls in place and keep evidence of what it has done.

The requirements will depend on the organisation, its sector, the information involved and how the technology is used.

Privacy is a practical example. For organisations covered by the Privacy Act, using AI does not remove existing responsibilities for personal information. The Office of the Australian Information Commissioner recommends assessing privacy risks before using commercially available AI products and taking particular care with personal information entered into public tools. Its guidance on privacy and AI products is a useful starting point.

It also helps to distinguish between a legal obligation, a customer’s contractual requirement and an internal policy. All three may need attention, but the source of the requirement is important when deciding what to do and how you can effectively demonstrate it.

The National AI Centre’s AI adoption implementation guidance provides a structure for more complex or higher risk AI use. It covers accountability, impacts, risk management and ongoing oversight.

Following a framework can help organise this work. However, it does not, on its own, establish that every applicable obligation has been met.

4. Be able to support your answers

When completing an assessment, it is easy to answer yes because a policy exists or because the team believes a process is being followed.

The question you should be asking is what supports the answer you provided?

If staff are expected to check AI generated customer communications, useful evidence might include the checking procedure, a regular team briefing and examples of completed reviews. Together, those records help explain how the requirement works in practice.

The evidence you provide should always relate to the question being asked. Adding a large folder of documents can make a review harder if nobody can tell which record supports which answer - being explicit will save everyone time.

A useful approach is to link the relevant record, include its date or version and briefly explain why it supports the assessment framework question. Where evidence is incomplete or lacking it is important that you record that too.

In SignalStamp’s AI governance assessment, the practice rating and supporting evidence confidence are assessed separately. This helps teams see where a practice is established and where the information supporting their answer still needs attention.

5. Turn findings into work people can complete

A structured and repeatable framework assessment should give the organisation a clear view of what to do next.

Suppose the review finds that staff are unsure whether they can enter customer information into an AI tool. The action needs to be specific enough for someone to complete and another person to review without creating extra work.

That could involve agreeing on permitted information, documenting the rule, briefing the team and checking that people understand what is being said. Assign an owner and a due date, then keep an evidence record of the steps you took.

This makes progress easier to report. It also gives the person reviewing the action a basis for deciding whether the original gap has been addressed.

Some findings may require an immediate change to how a tool is used. Others may involve improving documentation or planning further training. The response you provide should reflect the significance of the issue.

6. Review the decisions as AI use changes

An AI governance framework and assessment record is a snap shot of the organisation's position at a particular point in time. How you use this information is as important as the assessment process itself.

A team may start using a tool for a different purpose. A supplier may introduce new features. Information that was previously kept out of a system may become part of a proposed workflow.

Daily and fast moving changes can affect the original decision.

Set a regular review date and make clear which changes should trigger an earlier review. Keep previous results so the organisation can explain what changed, what it considered and why it reached a different conclusion.

Over time, this creates a more useful record of how AI is being managed and used.

7. Keep the work together

Assessment questions, supporting records, actions and reports are easier to manage when they are connected.

An online framework tool can give teams a consistent way to complete reviews and give leaders visibility of outstanding work. People can see the question being assessed, the evidence behind the response and who is responsible for the next step.

That is the work SignalStamp brings together. Organisations can use its AI governance assessment or manage other assessment areas through frameworks, modules and templates. Our Knowledge Centre explains how to use the platform.

The organisation remains responsible for its decisions and the quality of the information it records. The tool provides a structure for managing that responsibility.

A practical place to begin is one AI use already operating in your organisation. Review its purpose, the information involved, who is responsible and what evidence supports its approval.

Work through the gaps, then apply what you learn more widely. That gives you something useful to show when the next customer, board member or procurement team asks how you manage AI.