Executive summary

An AI mistake may first appear as an ordinary question from a customer or a concern raised by a member of staff. What happens next matters. Someone needs to protect people while the facts are checked, correct what has already happened and decide whether the service can safely continue. This paper helps leaders prepare for that moment, so that a small warning does not become a larger problem and recovery means more than switching the tool back on.

Imagine working through a letter to a resident when something catches your eye. The draft says they are not eligible for assistance, but the condition it relies on changed months ago. You correct it before sending. Then you remember that the AI assistant helped prepare yesterday’s letters too.

This is a hypothetical example, but the dilemma is familiar. You have caught one mistake. You do not yet know how many others there are, whether anyone relied on them or whether the next draft will repeat it. Should you stop the work? Who needs to know? What do you tell the person who may already have received the wrong answer?

Those are governance questions in their most practical form. They concern the organisation’s ability to respond when its usual arrangements have not worked as expected. For a regional service with a small team, answering them before an incident can make an uncertain morning considerably easier.

Make it easy to raise the first concern

The first person to notice a problem may know very little about AI. They may simply know that an answer contradicts the file, a familiar exception has disappeared or a customer is being sent in circles. They should not have to diagnose a technical fault before someone takes them seriously.

A useful reporting route therefore begins with the work: what happened, who might be affected and where the relevant evidence can be found. Staff need to know whom to contact and what they can pause while waiting. Customers need a usable way to question an answer, including when they do not know AI was involved.

Not every awkward draft needs a formal investigation. Consequence matters. An incorrect word caught during editing differs from advice that could cause someone to miss essential support. Repeated small errors may also reveal a weakness that deserves attention. The person receiving the concern needs room to make that judgement, rather than forcing every report into the same process.

Australia’s voluntary Guidance for AI adoption recommends investigating incidents and using the findings to improve systems and risk management. [1] That becomes meaningful when the person who reports a concern hears what happened next. Otherwise, reporting can feel like sending work into a void.

Protect people while you find out

In our example, waiting for a complete technical explanation would leave more letters exposed to the same mistake. The service could temporarily stop using the assistant for this task and give staff a checked manual response. It could preserve the relevant drafts and begin finding the letters already sent.

The right restriction depends on the risk. Pausing one workflow may be enough; closing the whole service might create problems of its own. Where serious harm is possible and the facts are uncertain, a cautious pause may be warranted. Someone must have the authority to make that decision, with a deputy available if needed.

The board should know when a concern requires its attention, but it should not be the first place a frontline employee must turn. An operational lead can coordinate the immediate response, bringing in privacy, legal, security or professional expertise as the circumstances require. A supplier contact should be reachable too, rather than discovered for the first time during the incident.

Meanwhile, the resident still needs an answer. Correcting the information, explaining what is being checked and providing a route to reconsideration may matter more to them than the cause of the software error. The organisation should also look for people who have not complained. Some may have accepted the wrong advice and gone no further.

Understand how the mistake got through

Once people are protected, the investigation can follow the error back through the work. Was the assistant using an old document? Did a supplier update change its behaviour? Did the review process make the mistake difficult to see?

BRAIN suggests looking through five overlapping lenses: the system, the interaction between people and AI, governance arrangements, the supplier and the wider institution. These are prompts for investigation, not a validated classification. They help avoid blaming the last employee who touched the letter when outdated information, workload and unclear responsibility may all have contributed.

For example, a reviewer may have been expected to check every answer but given too little time or access to the source material. Calling that “human oversight” does not explain whether it could work. Equally, changing the prompt will accomplish little if nobody owns the document the assistant relies on.

Keep enough evidence to understand the event, including the relevant version, output and human action, without collecting unnecessary personal information. Security incidents may need additional expertise; ASD’s guidance on engaging with AI describes that security perspective. [2] Other incidents may principally concern service quality, privacy or professional judgement. Reporting duties must be assessed under the rules that actually apply, not assumed from a general AI framework.

Decide what would make restarting reasonable

There is often pressure to return to normal once the immediate problem has been contained. Before doing so, ask what has changed and what evidence shows the change works.

In the letter example, replacing the outdated source is a beginning. The service would still need to test whether the assistant now uses the correct condition, whether reviewers can detect a similar mistake and whether the affected residents have received a correction. Someone should decide whether those checks justify renewed use and what closer monitoring is needed initially.

NIST’s voluntary AI Risk Management Framework connects incident response with recovery, communication and continuing improvement. [3] It offers useful guidance, not a universal Australian reporting obligation. A dependable recovery may involve a revised tool, a narrower use or a decision to continue without it.

The same care applies to near misses. The letter caught before sending is evidence that a safeguard worked on that occasion. It is also a chance to ask whether the organisation is depending too heavily on a single attentive person. A quiet incident register cannot answer that question.

Rehearse one ordinary mistake

Leaders can start with a short exercise using a real workflow and a hypothetical error. Ask a staff member who would listen, who could restrict the use and how the organisation would find earlier affected cases. Follow the response as far as the person waiting for help, then ask what would be needed before restarting.

Any uncertainty gives the team something useful to fix. Paper 10 explores how these lessons remain effective as systems and working practices change. Carefully protected lessons could also help other regional organisations, provided that permission, confidentiality and the risk of identifying people from local context are considered before sharing. BRAIN could support that learning; responsibility for the response remains with the institution.

For the resident, the final measure is simple: did the wrong answer get corrected, and could they obtain the help or reconsideration they needed? A sound incident response keeps that question in view from the first warning to the final check.

About BRAIN

The Ballarat Region Artificial Intelligence Network (BRAIN) is a regional institution focused on understanding the impact of artificial intelligence across Ballarat and surrounding communities and strengthening the capability required to respond. Through research, publications and regional collaboration, BRAIN examines how AI can contribute to regional prosperity and wellbeing while helping institutions navigate its risks and wider consequences.

About the Author

Matt Bowd is Co-Founder and Chief Executive Officer of the Ballarat Region Artificial Intelligence Network (BRAIN). His work focuses on AI governance, institutional capability and the implications of artificial intelligence for regional organisations and communities.

Sources

[1] National AI Centre, Guidance for AI adoption: foundations. Essential practice 3, incidents and learning; voluntary guidance.

[2] Australian Signals Directorate, Engaging with artificial intelligence. 2024. Security risks and safeguards.

[3] NIST, AI Risk Management Framework 1.0, Core. 2023, MANAGE 4.1–4.3; voluntary framework.

Sources checked 21 October 2026.