{"id":73536,"date":"2025-11-06T15:34:23","date_gmt":"2025-11-06T20:34:23","guid":{"rendered":"https:\/\/statescoop.com\/ai-bridges-data-sharing-gaps-for-faster-disaster-response\/"},"modified":"2025-11-06T15:34:23","modified_gmt":"2025-11-06T20:34:23","slug":"ai-bridges-data-sharing-gaps-for-faster-disaster-response","status":"publish","type":"post","link":"https:\/\/statescoop.com\/ai-bridges-data-sharing-gaps-for-faster-disaster-response\/","title":{"rendered":"AI bridges data-sharing gaps for faster disaster response"},"content":{"rendered":"\n

When torrential rains struck central Texas last July, flash floods overwhelmed rural communities in hours \u2014 and responders lacked the complete view they needed to act quickly. Local sheriffs had one set of reports, state emergency managers had another, while federal weather and relief agencies had different data. By the time situational updates reached incident commanders, much of the information was already too late to be actionable.<\/p>\n\n\n\n

That lag in disaster response\u2014between when critical data is collected and when it can be acted upon\u2014can cost lives and delay urgent relief efforts. A new generation of technology solutions, such as AI-enabled data fabrics, is helping close that gap by unifying disparate data streams and bringing decision-making closer to the point of need.<\/p>\n\n\n\n

Tackling interoperability<\/strong><\/p>\n\n\n\n

Despite improvements in forecasting, predicting the local impact of disasters and coordinating responses remains a challenge. A long-standing reason is the extent to which jurisdictions rely on different radios, databases, and reporting formats.<\/p>\n\n\n\n

In Texas, after-action reports from both floods and hurricanes highlighted how responders often couldn\u2019t even talk to each other over radio networks. The same fragmentation affects data and applications: state police, public works, and local sheriffs often maintain separate systems that do not connect.<\/p>\n\n\n\n

Compounding this is how information has traditionally flowed: 911 calls, sensors, social media, and field reports move up to a command hub before filtering back down to teams. That model creates bottlenecks and delays decision making.<\/p>\n\n\n\n

\u201cA gigantic complaint in the recent flooding was, \u2018We\u2019re not hearing from the people we need to hear from the way we need to hear from them.\u2019 And it’s all because they [federal, state and local authorities] have different technologies,\u201d said Joshua Hoeft, director of state and local government initiatives at General Dynamics Information Technology (GDIT).<\/p>\n\n\n\n

This is where the concept of a data fabric becomes crucial. Data fabrics streamline information from different sources, making it easier to pull from diverse formats and feed them into a common operating model, explained Steven Switzer, Director of AI for Federal Civilian at GDIT. \u201cInstituting a data fabric helps all that data flow freely,\u201d he said.<\/p>\n\n\n\n

AI-enabled decision making<\/strong><\/p>\n\n\n\n

What\u2019s changing is the ability to use purpose-built artificial intelligence (AI) models to access and synthesize information from spreadsheets, databases, documents, and real-time sensor feeds. These models help establish consistency and a common picture for all stakeholders.<\/p>\n\n\n\n

By decentralizing information flow, AI-enabled data fabrics also put immediate analysis into the hands of those directly on the mission, whether it\u2019s identifying affected areas, locating survivors, or staging resources.<\/p>\n\n\n\n

\u201cInstead of sending everything up to a centralized command center, the goal now is to bring decision-making as close to the point of mission as possible,\u201d said Switzer. \u201cThat means putting intelligence directly into the hands of first responders, where reaction time is critical.\u201d<\/p>\n\n\n\n

Beyond faster reactions, AI is also being applied to predict how wildfires or floods might unfold. Models can scan satellite imagery for heat signatures that suggest wildfire ignitions or forecast flood zones based on rainfall and terrain. That enables agencies to pre-stage supplies, reinforce infrastructure, or even avert disasters before they escalate.<\/p>\n\n\n\n

Switzer cited work piloted in California, where infrared imaging and AI detection identified wildfire hotspots before they spread into populated areas. \u201cIt\u2019s about getting \u2018left\u2019 of the event,\u201d he said. \u201cWith the correct predictive models, you can plan for disasters instead of being surprised by them.\u201d<\/p>\n\n\n\n

Lessons from the defense sector<\/strong><\/p>\n\n\n\n

The foundation for linking diverse communication and data systems with AI started nearly ten years ago in the defense sector. Recognizing the need for soldiers to act locally in disconnected, degraded, intermittent, and limited (DDIL) environments<\/a>, the Department of War invested heavily in secure, interoperable systems.<\/p>\n\n\n\n

Systems integrators play a critical role. Rather than asking agencies to rip and replace IT, integrators can build data fabrics that translate and connect disparate formats into a common operating framework.<\/p>\n\n\n\n

\u201cYou don\u2019t want agencies spending all their time wrangling data,\u201d Switzer explained. \u201cAI can summarize, contextualize, and give them what they need so they can focus on the mission \u2014 saving lives. Our role as a system integrator is important because we see the full picture \u2014 the full enterprise and all those disparate systems. Our goal and our job is to make sure that those work together.”<\/p>\n\n\n\n

The takeaway for civilian agencies is that AI isn\u2019t a stand-alone tool but part of an integrated process combining software, security, infrastructure, and training. \u201cThe sooner states integrate AI into their existing IT and operations practices, the more successful they\u2019ll be,\u201d Switzer said.<\/p>\n\n\n\n

Hardware platforms are also evolving to support this shift. GDIT\u2019s Raven mobile command vehicle<\/a>, for example, brings satellite-connected AI and communications interoperability directly into disaster zones.<\/p>\n\n\n\n

\u201cIf a flood knocks out power and internet across a region, you can drive Raven in and reestablish critical operations,\u201d Hoeft said. From drone management to secure data collection, such mobile edge hubs enable responders to operate independently of traditional infrastructure.<\/p>\n\n\n\n

Staying flexible<\/strong><\/p>\n\n\n\n

As with all IT developments, successful implementations depend on a longer-term vision, even amid unpredictable events. Sustaining AI systems requires regular updates, governance agreements, and shared funding. Predictive models, in particular, must be retrained continuously as conditions evolve.<\/p>\n\n\n\n

Cost-sharing is another factor. State, local, and federal agencies must collectively invest in interoperable platforms rather than each experimenting in isolation. That requires champions at both executive and mission levels. \u201cIf it\u2019s only driven from the top, you risk building tech without a mission,\u201d Switzer said. \u201cIf it\u2019s only bottom-up, you risk roadblocks and limited reach. You need both.\u201d<\/p>\n\n\n\n

The urgency has only grown. As disasters intensify, delays in coordination can multiply costs. At the same time, advances in AI accessibility have made it feasible to embed intelligence across many missions \u2013 not just IT.<\/p>\n\n\n\n

\u201cThis is what\u2019s different today than even a year ago,\u201d Switzer said. \u201cAI isn\u2019t just in the hands of technical organizations anymore. It\u2019s moving into non-technical missions, where it can directly augment decision-making.\u201d<\/p>\n\n\n\n

Learn more about how GDIT deploys AI capabilities<\/a> that bring what matters into focus.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

AI-powered \u2018data fabrics\u2019 bring advanced capabilities to disaster response by unifying disparate data streams and bringing decision-making closer to where it\u2019s needed.<\/p>\n","protected":false},"author":629,"featured_media":73558,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"disable_grayscale_images":true,"grayscale_contrast":0,"sponsored_content":true,"display_author_bio":true,"story_type":"","footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[4835,23780],"tags":[280,383,528,1200,5244,5272,5364,25271,25965,25966,25967,25968,25969],"people":[],"special-report":[],"authors":[23938],"class_list":["post-73536","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sponsored-content","category-insights","tag-first-responders","tag-sponsored-content","tag-predictive-analytics","tag-department-of-defense","tag-executive-perspective","tag-artificial-intelligence-ai","tag-data-sharing","tag-gdit","tag-data-fabric","tag-decision-making","tag-disaster-planning","tag-gdit-2025-2","tag-texas-floods","author-scoop-news-group"],"yoast_head":"\nAI 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