Overview
Emergency management is under increasing pressure, more frequent natural disasters, more complex incidents, and rising expectations for faster, coordinated response. At the same time, agencies are sitting on more data than ever but often lack the capability to turn that data into timely, actionable decisions.
AI is starting to shift this.
Across ANZ in 2026:
- Many agencies are exploring or piloting AI for operational or planning use cases
- Public expectations for modernised, data-enabled emergency response are rising
- Investment is accelerating in simulation, digital twins, and predictive modelling to improve preparedness and coordination
But adoption is uneven.
Most agencies are still:
- Experimenting in pockets (e.g. reporting, mapping, admin tasks)
- Struggling with data quality, integration, and trust in AI outputs
- Unclear on how to safely apply AI in real operational decision-making
This program cuts through hype.
It focuses on how AI can actually support emergency management workflows from planning and preparedness through to response and recovery without compromising human judgement, safety, or accountability.
This is not a technical course.
It’s a practical, operations-focused program designed to help emergency management professionals:
- Use AI to enhance situational awareness and decision-making
- Reduce administrative burden and free up operational capacity
- Apply AI safely in high-risk, high-pressure environment
Course Inclusions
Practical Takeaways
- A “safe use” checklist for operational teams (what you can/can’t do with AI during incidents)
- A prompt pack for emergency management (sitrep, briefing note, intel summary, stakeholder update)
- A lightweight evaluation rubric (accuracy, timeliness, bias risk, traceability, approval thresholds)
- A 30/60/90-day trial plan (use case, data inputs, guardrails, measures of success)
Who Should Attend?
Professionals directly involved in emergency preparedness, response and coordination, including: Police, Fire & Rescue, Ambulance and State Emergency Services (SES) | National and state emergency management agencies | Local councils with emergency management and disaster response responsibilities Also relevant for Professionals supporting emergency management through planning, coordination and operational support functions, including: Environment and land management | Infrastructure, utilities and transport | Health services (e.g. ambulance, hospital operations, emergency planning) | Government coordination and recovery functions | Public information, media and stakeholder liaison roles (particularly those involved in crisis communication and real-time updates)
Incident Management Team (IMT) roles (e.g. Incident Controllers, Operations, Planning, Logistics)
Duty Officers / State Duty Officers
Operations Managers and Regional Commanders
Intelligence and Situational Awareness Analysts
Emergency management planners and resilience teams
Learning Outcomes
By the end of this course, you will be able to:
Identify high-value, low-risk AI use cases across emergency management
Apply AI to improve situational awareness, including data synthesis, intelligence summaries, and real-time inputs
Use AI to support resource allocation, risk assessment and scenario planning
Critically evaluate AI outputs understanding limitations, hallucinations, bias and reliability risks
Use structured prompting patterns for operational artefacts (sitrep, briefings, logs)
Align AI use to operational command structures, approvals, and information management requirements
Apply ethical, legal and operational guardrails, including privacy, accountability, and human-in-the-loop decision-making
Integrate AI into existing workflows, without disrupting operational command structures
Build a clear, practical action plan to trial AI within their team or function
Online Training
AI in Emergency Management: Decision Support for Preparedness, Response & Recovery
Session details
It’s a practical, operations-focused program designed to help emergency management professionals:
- Use AI to enhance situational awareness and decision-making
- Reduce administrative burden and free up operational capacity
- Apply AI safely in high-risk, high-pressure environment
Some familiarity with topic is recommended
Key Sessions
Welcome and Introductions
Module 1 — From Reactive to Proactive: The Role of AI in EM
- How AI supports preparedness, response and recovery
- Moving from data overload to decision support
- Real examples: policing, fire, ambulance, and multi-agency coordination
Module 2 — High-Value Use Cases (Where AI Actually Works)
- Administrative automation (reports, logs, briefings)
- Intelligence synthesis and situational awareness
- Risk identification and early warning signals
- Mapping, geospatial insights and data consolidation
Module 3 — Decision Support (Not Decision Making)
- Using AI in high-pressure environments
- When to trust AI — and when not to
- Avoiding over-reliance and automation bias
- Maintaining command and control integrity
Module 4 — Data, Inputs & Verification (Making AI Reliable Under Pressure)
- Why AI fails: poor data, missing context, fragmentation
- Structuring inputs for better outputs
- Reducing hallucinations and improving accuracy
- Practical input frameworks
- “Confidence checks” (cross-checking, triangulation, escalation triggers)
Module 5 — Ethics, Risk and Operational Guardrails
- Bias, liability and accountability in EM
- Privacy and sensitive data constraints
- Human-in-the-loop decision-making
- Safe vs unsafe use cases (real scenarios)
Module 6 — Real-World Use Cases (What’s Working Now)
- Police: reporting, intelligence summaries, operational support
- Fire & SES: planning, resource deployment, risk modelling
- Health & Ambulance: continuity, triage support, documentation
- Cross-agency coordination and information sharing
Module 7 — Hands-On: Using AI for Operational Tasks
- Drafting situation reports, briefings and updates
- Summarising large volumes of information quickly
- Structuring outputs for operational use
- Prompting techniques for accuracy and clarity
Module 8 — Scenario Workshop: Applying AI in EM
- Participants work through realistic scenarios:
- Incident response and escalation
- Resource allocation decisions
- Multi-agency coordination challenges
Focus:
- What AI can support
- What must remain human-led
Module 9 — Embedding AI into Your Team
- Identifying practical use cases in your function
- Managing resistance and building trust
- Starting small: low-risk, high-impact applications
- Measuring value and ROI (critical for funding approval)
Module 10 — Action Planning
- Define 1–2 AI use cases to trial
- Identify risks, guardrails and success measures
- Build a simple implementation plan
Reflections and Closing Remarks
End of Day 2
Meet Your Facilitator
Brent Lefebure
Operational AI Specialist | Emergency Management & Law Enforcement
Brent spent a decade in law enforcement, working across frontline operations, investigations and the technology programs reshaping how modern policing gets done. He has since worked in emergency preparedness and training, and as an AI domain expert contributing directly at the model level on police supervision and operational decision-making.
Register Today
Join this training for professionals working within the Public Sector
Extra Early Bird
Ends 2 Jun
$A 795
per person + tax $400 savingEarly Bird
Ends 10 Jul
$A 995
per person + tax $200 savingRegular
Ends 25 Aug
$A 1195
per person + taxFor group or payment enquiries or custom training solutions, please contact [email protected]
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