{"resourceId":"rand-emergency-ai-market-adoption-2026","versions":[{"version":"external-7769a94aa957c4f322eecec2293c3437c9ebb7c5e0b8d4c02aae922b362b9391","resource":{"id":"rand-emergency-ai-market-adoption-2026","title":"RAND distinguishes emergency-management AI supply from demonstrated adoption and resilience","organization":"RAND; Jessica Jensen, Jessie Riposo, Leah Dion and Glen L. Woodbury","sector":"Emergency management and disaster readiness","geography":"United States, including state, local, tribal and territorial organizations","publishedAt":"August 4, 2026","publicationDate":"2026-08-04","eventDate":null,"sourceName":"RAND","sourceLabel":"Independent landscape research funded by the Markle Foundation","sourceUrl":"https://www.rand.org/content/dam/rand/pubs/research_reports/RRA4600/RRA4625-1/RAND_RRA4625-1.pdf","evidenceClass":"independent-research","outcomeClass":"mixed","topics":["knowledge-work","developers-agents","infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"RAND identifies a substantial product landscape but separates availability from validated performance and adoption.","sledRelevance":"Interpretation: Agencies can use the landscape to structure discovery, not as an approved-products list.","evidence":"Mixed-method research collected public information from October 2025 through March 2026, identifying 1,179 products. Discovery and characterization used LLM/API tools with automated and human verification. Integration capacity, connectivity, opaque pricing and limited assurance emerge as barriers; no direct product performance evaluation was conducted.","architectureImplications":"Interpretation: Map product dependencies and test complete tasks during network loss. Compare cloud, local and hybrid approaches against actual data and support requirements.","governanceImplications":"Interpretation: Use independent local evidence gates for each proposed task, including general-purpose copilots and agents.","securityPrivacyImplications":"Interpretation: Inspect actual data flows, retention, model-training use and permissions. Landscape risk coding does not prove a specific product is unsafe or secure.","caveats":"Public-information sample favors visible products; some classifications are inferred. Historical market snapshot, not a census or catalog of successful deployments. Detailed product rows were not independently reproduced.","streamIds":["emergency-services"],"roles":{"sales":"Interpretation — Ask the emergency manager, IT lead and procurement team which concrete task consumes scarce capacity and whether a product can complete it with existing data and staff. Include accessibility and community-service stakeholders when outputs affect residents. Offer a bounded readiness assessment with one candidate workflow and a documented baseline. The value hypothesis is identifying a feasible, supportable use before scarce funds are committed. Do not present a large product count as adoption evidence or assume an advertised capability delivers disaster resilience. Qualify recurring support, integration and training costs before proposing broader deployment; low-capacity agencies may need shared services or process changes first.","engineering":"Interpretation — Draw the dependency chain for the selected task, including identity, data preparation, external APIs, model services and human approval. Require current technical documentation and authorized test data. Test connectivity loss, service throttling, stale inputs and incomplete outputs across the full chain. For a document copilot, verify citations and instruction isolation; for agents, constrain tools and prohibit unapproved operational actions. Compare local, hosted and hybrid options using measured task performance and recovery effort. Validate security claims directly. The landscape informs questions to ask but supplies no product-specific benchmark, so the proof of value must establish local fit independently.","delivery":"Interpretation — Name an emergency-management process owner with IT, procurement and training counterparts. Baseline the task, inventory dependencies, negotiate support and exit terms, and train staff before a limited pilot. Dependencies include maintained data, available reviewers and capacity to recover when services fail. Governance checkpoints should approve information handling, scope changes and any increase in agent permissions. Proposed acceptance includes completed end-to-end tasks, documented human review, a successful offline or manual fallback exercise and measured staff effort against baseline. These are proposed tests. Track hidden integration work and recurring costs; the principal risk is buying capability that the organization cannot sustain during a disruption."},"retrievedAt":"2026-09-11T03:04:27Z","enrichedAt":"2026-09-11T03:07:08Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: Budget frontline learning, accessible outputs, technical support and sustained evaluator capacity.","procurementImplications":"Interpretation: Obtain complete cost and support terms for dependencies; require exit rights, evidence access and service continuity.","operatingModelImplications":"Interpretation: The emergency manager owns task priorities, IT owns dependencies and service recovery, and procurement owns enforceable obligations.","updateExplanation":"New archive source, newly relevant through September 10 secondary coverage. Original research date remains August 4; no September 10 research release is claimed.","sourceVerification":{"openedUrl":"https://www.rand.org/content/dam/rand/pubs/research_reports/RRA4600/RRA4625-1/RAND_RRA4625-1.pdf","referenceExcerpt":"We do not rank or evaluate specific","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}