AI and the Future of Emergency Management - RAND Corporation

As hazards and disasters increase in intensity and frequency, demands on emergency managers and others who support emergency management (EM) functions often outpace their capabilities and capacity. This report presents a landscape assessment of artificial intelligence–enabled products available to support EM work, including the products identified, what it takes to adopt and diffuse those products, and pathways to support adoption and diffusion. AI and the Future of Emergency Management Market Supply and Adoption Pathways Jessica Jensen , Jessie Riposo , Leah Dion , Glen L. Woodbury Research Published Aug 4, 2026 Download PDF Share on LinkedIn Share on X Share on Facebook Email Although many publications describe what artificial intelligence (AI) could do for emergency management (EM), almost none describe what AI-enabled products are available or what it would take to adopt and diffuse those products. This information gap matters: Emergency managers and others who support EM functions are making procurement decisions in a crowded technology marketplace with limited time, technical capacity, and standardized information about how AI-enabled products perform against the realities of their work. As hazards and disasters increase in intensity and frequency, the demands on the resources of the EM community often outpace existing capabilities and capacity. AI-enabled products could step in to meet this need. The Markle Foundation commissioned this landscape assessment as part of its AI for Disasters and Emergencies (AIDE) Initiative to give emergency managers and others who support EM functions, along with technology companies, funders, policymakers, and researchers, a picture of the AI-enabled product landscape and the conditions that will shape how those products can support EM. The authors identified 1,179 AI-enabled products with potential relevance to EM, characterized the products they identified, described what it takes to adopt and diffuse those products, and identified what stands between the availability of those products and broad adoption and diffusion. This report is intended to give the EM community, technology companies, funders, policymakers, and researchers a reliable picture of available AI-enabled products so that they can act on what the landscape suggests for their own decisions, investments, and further work. Key Takeaways The AI-enabled product landscape is substantial but unevenly distributed among EM functional areas. Approximately half of the identified products are either purpose-built for EM or have documented use in EM contexts; more than 40 percent are general-purpose solutions with no EM-specific design. Most products address narrow slices of EM work and would require stacking with other products to meet the full scope of a task area. Most products do not facilitate coordination across organizations’ essential EM task areas or even most EM task areas. One in five products depends on another product to function. Most products require technical integration that presupposes information technology (IT) capacity, ongoing data inputs, and continuous internet connectivity. Pricing is largely opaque: Most technology companies do not publish prices, and assembling reliable cost information across the product landscape was not feasible even with substantial research effort. Privacy risk is near-universal across the landscape; legal and cyber concerns affect a meaningful minority of products, and verifiable AI governance assurance is rare. Adoption and diffusion conditions for AI in EM are in an early emergence stage. Funding, staffing, IT capacity, and procurement infrastructure are the most persistent constraints, and they echo barriers that have been documented across decades of EM research. Adoption across organizations that are adjacent to EM, such as utilities, nonprofits, and public health agencies, appears further along than in EM offices but is uneven and concentrated in administrative and communication uses. Recommendations Emergency managers and others who support EM functions, technology companies, policymakers, and researchers should pursue sequenced action agendas that detail things each stakeholder group can do in the next 12 months, the next one to three years, and on an ongoing basis to support the adoption and diffusion of AI-enabled products to support EM. These stakeholders should also consider establishing a standing cross-sector alliance to focus sustained attention on AI-enabled product adoption and diffusion, anchored by members with the capacity to financially support and sustain the alliance. These stakeholders should consider launching a national-level EM AI readiness and adoption accelerator to function as an independent, Consumer Reports–style clearinghouse and provide product evaluations, standardized buyer guides, model procurement and contracting language, peer-learning cohorts, and practical deployment templates for low-capacity jurisdictions. These stakeholders should consider developing and sustaining a national, freely accessible education and training program designed as a tiered learning pathway to build AI awareness and capability among emergency managers and others who perform EM functions. These stakeholders should consider funding the critical outstanding research questions identified in this report—including whether large language models can effectively support EM tasks and whether national AI governance, procurement, and IT policies create systematic barriers to adoption—so that the answers can inform pilot projects, alliance work, and policy decisions. Related Content Read More Topics Access to Technology Artificial Intelligence Community Resilience Disaster Recovery Operations Emergency Services and Response Emerging Technologies Natural Hazards Qualitative and Mixed Methods Science and Technology Legislation Science, Technology, and Innovation Policy Document Details Availability: Web-Only Year: 2026 Pages: 148 DOI: https://doi.org/10.7249/RRA4625-1 Document Number: RR-A4625-1 Citation RAND Style Manual Jensen, Jessica, Jessie Riposo, Leah Dion, and Glen L. Woodbury, AI and the Future of Emergency Management: Market Supply and Adoption Pathways, RAND Corporation, RR-A4625-1, 2026. As of August 4, 2026: https://www.rand.org/pubs/research_reports/RRA4625-1.html Copy Text Chicago Manual of Style Jensen, Jessica, Jessie Riposo, Leah Dion, and Glen L. Woodbury, AI and the Future of Emergency Management: Market Supply and Adoption Pathways. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA4625-1.html. Copy Text BibTeX RIS Research conducted by RAND Homeland Security Research Division This research was funded through a generous grant from the Markle Foundation and conducted within the RAND Homeland Security Research Division . This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. 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