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Quest Training

AI for Crisis & Emergency Management Training Course

Explores uses of AI in crisis management, from assessing emerging threats and situational awareness to resource allocation, under human oversight.

ER · Emergency Response & Crisis ManagementAll Levels#2818ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Health, Safety, Environment & SustainabilityCategories:Emergency Response & Crisis Management

Course Overview

Artificial intelligence is transforming how organizations anticipate crises, assess emerging threats, coordinate emergency responses, and support time-critical decisions. By analyzing large volumes of data, identifying unusual patterns, and generating actionable insights, AI can help crisis management teams improve situational awareness, allocate resources more effectively, and respond to rapidly changing conditions. However, its value depends on data quality, human oversight, system reliability, and responsible implementation. This course explores practical applications of AI in crisis and emergency management, including predictive analytics, early warning systems, incident data analysis, emergency resource planning, scenario modeling, and AI-assisted communication. Participants examine how machine learning, generative AI, geospatial analysis, and automated monitoring can support preparedness, response coordination, and recovery planning while addressing uncertainty, misinformation, privacy, and operational risks. Through applied exercises and realistic emergency scenarios, participants develop a structured approach to integrating AI into crisis management workflows. The programme emphasizes human-led decision-making, verification of AI-generated insights, responsible data use, and the development of practical response plans that strengthen organizational resilience and emergency preparedness.

Learning Objectives

  • Explain the role and limitations of AI in crisis and emergency management.
  • Identify suitable AI applications across preparedness, response, and recovery.
  • Use data-driven approaches to support risk assessment and early warning.
  • Evaluate AI-generated forecasts, alerts, and recommendations critically.
  • Apply AI tools to incident analysis, scenario planning, and emergency reporting.
  • Improve resource allocation and coordination using data-supported insights.
  • Use generative AI to support crisis communication and situation reporting.
  • Identify risks related to data quality, bias, privacy, cybersecurity, and misinformation.
  • Establish human oversight and verification procedures for AI-assisted decisions.
  • Develop an AI-supported crisis management improvement plan.

Who Should Attend

Crisis and emergency management professionals. Disaster response and civil protection personnel. Risk management and business continuity specialists. Security, safety, and emergency planning managers. Government and public sector officials responsible for emergency preparedness. Operations and incident response coordinators. Business continuity, resilience, and organizational risk professionals. Digital transformation, data analytics, and AI implementation teams supporting emergency functions.

Learning Outcomes

  • Explain how AI supports crisis anticipation, situational awareness, and emergency response.
  • Identify appropriate AI use cases for different emergency management stages.
  • Interpret AI-supported risk assessments, alerts, and predictive outputs.
  • Develop structured prompts for AI-assisted analysis and emergency reporting.
  • Evaluate the reliability and limitations of AI-generated information.
  • Apply scenario analysis and data-driven planning to emergency preparedness.
  • Improve information coordination and resource planning using AI-supported insights.
  • Identify ethical, legal, privacy, and cybersecurity considerations.
  • Establish human review and escalation procedures for AI-assisted decisions.
  • Prepare an implementation roadmap for responsible AI adoption in crisis management.

Course Outline

Course Outline

Day 1

AI Fundamentals & Crisis Management Applications

  • Core AI concepts, machine learning, generative AI, and predictive analytics.
  • The crisis management lifecycle: prevention, preparedness, response, and recovery.
  • Mapping AI capabilities to emergency management requirements.
  • Data sources for crisis analysis, including incident records, weather information, sensors, and public reports.
  • Understanding data quality, uncertainty, and limitations in emergency environments.
  • Human oversight, accountability, and responsible AI use in high-stakes decisions.
  • Practical Exercise: Map a selected emergency scenario to potential AI applications, required data sources, expected benefits, and key limitations.
Day 2

Predictive Analytics, Risk Assessment & Early Warning Systems

  • Using historical and real-time data to identify emerging risks and unusual patterns.
  • Predictive modeling for incident likelihood, demand forecasting, and resource requirements.
  • AI-supported early warning systems and alert prioritization.
  • Integrating geospatial information and time-series data into risk assessment.
  • Interpreting confidence levels, false alarms, and missed detections.
  • Establishing alert thresholds, escalation rules, and verification procedures.
  • Practical Exercise: Analyze a simulated emergency dataset to identify risk indicators, assess alerts, and recommend appropriate escalation actions.
Day 3

AI-Assisted Emergency Response & Operational Coordination

  • Using AI to summarize incident reports and consolidate information from multiple sources.
  • Supporting situational awareness through automated data classification and analysis.
  • Resource allocation, staffing requirements, and response prioritization.
  • Applying AI to scenario modeling and evaluating alternative response options.
  • Coordinating information between emergency teams and relevant stakeholders.
  • Monitoring response performance and updating decisions as conditions change.
  • Practical Exercise: Use a simulated multi-incident scenario to develop a response prioritization plan, allocate resources, and prepare an operational situation report.
Day 4

Generative AI, Crisis Communication & Information Integrity

  • Using generative AI to draft emergency messages, briefings, and situation reports.
  • Tailoring communication for authorities, response teams, employees, and the public.
  • Verifying AI-generated content before operational use or publication.
  • Identifying misinformation, manipulated content, and unverified incident reports.
  • Protecting sensitive information and personal data when using AI tools.
  • Establishing approval workflows and consistent messaging during emergencies.
  • Practical Exercise: Develop an emergency communication package using AI assistance, then verify the content, correct inaccuracies, and prepare approved messages for different audiences.
Day 5

AI Governance, Simulation & Implementation Planning

  • Evaluating AI systems for reliability, explainability, security, and operational suitability.
  • Defining human decision authority and escalation requirements.
  • Managing model errors, data gaps, bias, and technology dependencies.
  • Establishing governance, documentation, auditability, and performance monitoring.
  • Designing AI-supported exercises, simulations, and after-action reviews.
  • Developing an implementation roadmap with measurable objectives and risk controls.
  • Practical Exercise: Prepare an AI-enabled crisis management implementation plan covering a selected use case, data requirements, human oversight, performance indicators, and a phased deployment approach.

Upcoming Dates

Selected date

Bangkok · 30 Nov – 4 Dec 2026 · Classroom

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CityDatesPrice per ParticipantEarly Bird
AbujaScheduled
2 – 6 Nov 2026€5,100—
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AccraScheduled
9 – 13 Nov 2026€5,100—
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AmmanScheduled
15 – 19 Nov 2026€3,100—
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AmsterdamScheduled
23 – 27 Nov 2026€5,400€4,860until 2 Nov
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BangkokScheduled
30 Nov – 4 Dec 2026€6,100€5,490until 9 Nov
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BarcelonaScheduled
30 Nov – 4 Dec 2026€5,400€4,860until 9 Nov
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BerlinScheduled
7 – 11 Dec 2026€5,800€5,220until 16 Nov
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CairoScheduled
14 – 18 Dec 2026€3,300€2,970until 23 Nov
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Cape TownScheduled
21 – 25 Dec 2026€5,100€4,590until 30 Nov
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CasablancaScheduled
28 Dec 2026 – 1 Jan 2027€4,300€3,870until 7 Dec
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DammamScheduled
4 – 8 Jan 2027€4,200€3,780until 14 Dec
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DohaScheduled
11 – 15 Jan 2027€4,400€3,960until 21 Dec
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DubaiScheduled
17 – 21 Jan 2027€4,400€3,960until 28 Dec
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FrankfurtScheduled
18 – 22 Jan 2027€5,800€5,220until 28 Dec
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HamburgScheduled
25 – 29 Jan 2027€5,800€5,220until 4 Jan
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IstanbulScheduled
1 – 5 Feb 2027€4,700€4,230until 11 Jan
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JeddahScheduled
7 – 11 Feb 2027€4,200€3,780until 18 Jan
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KigaliScheduled
15 – 19 Feb 2027€5,100€4,590until 25 Jan
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Kuala LumpurScheduled
22 – 26 Feb 2027€4,400€3,960until 1 Feb
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LondonScheduled
1 – 5 Mar 2027€5,600€5,040until 8 Feb
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LisbonScheduled
1 – 5 Mar 2027€5,400€4,860until 8 Feb
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MadridScheduled
8 – 12 Mar 2027€5,400€4,860until 15 Feb
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ManamaScheduled
15 – 19 Mar 2027€4,700€4,230until 22 Feb
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MilanScheduled
22 – 26 Mar 2027€6,000€5,400until 1 Mar
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MuscatScheduled
29 Mar – 2 Apr 2027€5,300€4,770until 8 Mar
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OnlineScheduled
4 – 8 Apr 2027€1,700€1,530until 15 Mar
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RomeScheduled
12 – 16 Apr 2027€5,800€5,220until 22 Mar
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ParisScheduled
12 – 16 Apr 2027€5,700€5,130until 22 Mar
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TunisiaScheduled
19 – 23 Apr 2027€3,800€3,420until 29 Mar
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ViennaScheduled
26 – 30 Apr 2027€5,700€5,130until 5 Apr
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Available in: Istanbul · Dubai · London · Doha · Amsterdam

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