AI Mistakes Australian Emergency Services Should Avoid

AI tools offer significant benefits for emergency services — better dispatch, improved fire prediction, faster damage assessment. But emergency services organisations that adopt AI without careful governance and oversight are creating risks in an environment where mistakes can cost lives. These are the most common AI mistakes in Australian emergency services — and how to avoid them.

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Mistake 1: Over-Relying on AI During Active Incidents

AI tools can provide valuable information and recommendations during active incidents, but they can also be wrong. A dispatch system that recommends the wrong resource, a fire behaviour model that underestimates spread, or a flood prediction that is inaccurate can lead to poor decisions.

Emergency services personnel who treat AI recommendations as definitive rather than as one input among many are taking a risk.

How to avoid it: Train personnel to understand the limitations of AI tools and to apply their own judgement. AI recommendations should be one input into decision-making, not the only input. Experienced incident commanders should be empowered to override AI recommendations when their judgement differs.

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Mistake 2: Failing to Maintain Manual Backup Systems

AI-powered dispatch and coordination systems depend on communications and computing infrastructure that may be damaged or degraded during major disasters — exactly the situations where emergency services need them most.

Organisations that have become entirely dependent on AI-powered systems without maintaining manual backup processes are vulnerable.

How to avoid it: Maintain manual backup processes for all critical functions. Regularly test these backup processes to ensure personnel can use them effectively. Do not allow AI systems to atrophy manual skills.

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Mistake 3: Using AI Tools That Are Not Fit for Purpose

General-purpose AI tools — ChatGPT, Claude, Microsoft Copilot — are useful for administrative tasks, but they are not appropriate for operational decision-making during active incidents. They are not designed for real-time emergency response, they do not have access to real-time operational data, and they can produce inaccurate information.

How to avoid it: Be clear about which AI tools are appropriate for which tasks. General-purpose AI writing tools are for administrative work. Operational AI tools — dispatch systems, fire behaviour models, flood forecasting — are purpose-built for specific operational functions.

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Mistake 4: Entering Sensitive Information into Unapproved Tools

Emergency services deal with sensitive information — personal information about victims and patients, operational information about incidents, information about infrastructure vulnerabilities. Entering this information into AI tools that have not been approved for those purposes creates privacy and security risks.

How to avoid it: Develop clear guidelines for what information can be entered into which AI tools. Personal information about victims, patients, or suspects must never be entered into public AI tools. Use only agency-approved tools for work involving sensitive information.

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Mistake 5: Ignoring Data Quality Problems

AI tools are only as good as the data they are based on. Emergency services organisations that deploy AI tools without addressing underlying data quality problems will not get the expected benefits — and may get misleading outputs.

Common data quality problems include: incomplete or inconsistent incident records, outdated mapping data, inaccurate resource status information, and gaps in historical data.

How to avoid it: Invest in data quality before deploying AI tools. Identify the data sources that AI tools will rely on and assess their quality. Address data quality problems as part of the AI implementation process.

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Mistake 6: Deploying AI Without Adequate Training

AI tools are only useful if personnel know how to use them effectively. Emergency services organisations that deploy AI tools without adequate training risk poor adoption, misuse, and frustration.

This is particularly important for operational AI tools — dispatch systems, fire behaviour models — where misuse can have serious consequences.

How to avoid it: Invest in training before deploying AI tools. Include AI tool use in initial training for new personnel and in refresher training for existing personnel. Develop clear guidelines for how each tool should be used.

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Mistake 7: Neglecting Cybersecurity

AI systems are software systems, and like all software systems, they are vulnerable to cyberattacks. An emergency services organisation whose dispatch system is compromised by a cyberattack during a major incident faces a serious operational problem.

The Australian Cyber Security Centre (ACSC) has noted that critical infrastructure, including emergency services, is a target for cyberattacks.

How to avoid it: Apply the same cybersecurity standards to AI systems as to other critical IT systems. Follow ACSC guidance on cybersecurity for critical infrastructure. Ensure that AI systems are included in incident response plans.

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Mistake 8: Failing to Address Privacy and Civil Liberties Concerns

Some AI applications in emergency services — social media monitoring, facial recognition, real-time tracking — raise significant privacy and civil liberties concerns. Organisations that deploy these technologies without adequate governance and oversight risk public trust and legal liability.

How to avoid it: Conduct privacy impact assessments before deploying AI tools that collect or process personal information. Develop clear policies on the use of surveillance and monitoring technologies. Ensure that these policies are publicly available and subject to appropriate oversight.