AI Playbook for Emergency Management Resource Allocation
A state emergency management agency is responding to a major hurricane that has affected 14 counties. Available state resources must be allocated across the 14 counties. Needs assessments are incomplete for 5 counties. The governor's office wants an allocation decision in 4 hours.
When to use this playbook
- Use this playbook when the decision looks like the situation above: A state emergency management agency is responding to a major hurricane that has affected 14 counties.
- It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Emergency Management Resource Allocation".
- Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.
What you'll need
- Initial damage assessment data (available for 9 of 14 counties)
- Available state resource inventory (personnel, equipment, medical supplies)
- Population and demographic data for all 14 counties
- Federal FEMA resource request procedures
- Prior hurricane response lessons-learned
Attachments: Documents (Documents)
The Prompt
You are a state emergency management director making resource allocation decisions during an active hurricane response. I am attaching: Work only from the attached source files. If a conclusion is not supported, say so. Produce: 1. Prioritize the 14 counties by need severity using available data and population-based proxies for the 5 counties without complete assessments. 2. Allocate available state resources: match resource types to highest-priority needs by county. 3. Identify the resource gaps: what is needed that the state doesn't have, and what federal request should go to FEMA immediately? 4. Design the resource reallocation trigger: when should resources move from a stabilized county to an emerging need? 5. Give me the 4-hour decision brief: what I can decide now, what requires more information, and the default action for undecided questions. Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.
What to expect
- County prioritization with needs estimates
- Resource allocation matrix by county and type
- Federal FEMA request list
- Reallocation trigger criteria
- 4-hour decision brief with known/unknown/default framework
Review before you act
- Validate this output against source files before relying on it: Prioritize the 14 counties by need severity using available data and population-based proxies for the 5 counties without complete assessments.
- Validate this output against source files before relying on it: Allocate available state resources: match resource types to highest-priority needs by county.
- Validate this output against source files before relying on it: Identify the resource gaps: what is needed that the state doesn't have, and what federal request should go to FEMA immediately?.
- Validate this output against source files before relying on it: Design the resource reallocation trigger: when should resources move from a stabilized county to an emerging need?.
- Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
- Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
- Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.
Why compare models on this
For Emergency Management Resource Allocation, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface county prioritization with needs estimates; resource allocation matrix by county and type; federal fema request list; reallocation trigger criteria. Those are comparison artifacts — they only exist if more than one model runs. Models disagree on exemption applicability, IG finding risk, and how aggressive a budget narrative can be. Those disagreements belong with counsel and the authorizing official.
See governed multi-model AI on your own prompt
Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.

