When an emergency occurs, the magnitude of its potential damage and disruption can only be mitigated by a swift and organized response composed of qualitative decisions made with accurate information.
To deliver such a response, your organization must be prepared with an effective emergency management plan, incident action plans, and an incident management system (IMS) built to account for your organizations’ physical and digital spaces. To execute these actions, both the members of your response teams and emergency workers on the front lines, should they be needed, must be trained and ready to quickly and ably fulfill their roles.
But for all the planning and training that informs your resilience posture, emergency situations are dynamic environments with multiple factors in flux and a continuous stream of new information. With the constant threat of changes to the environment, or the introduction of further unforeseen circumstances, it’s hard to maintain flawless situational awareness, and therefore, to always be making the most informed decisions at every possible turn.
This is where digital tools with integrated elements of artificial intelligence (AI) and machine learning (ML) can help augment your organization’s ability to prepare and respond to emergencies. AI and ML technologies can help model potential outcomes, actively monitor active emergency situations, and continuously incorporate new information into dynamic yet accurate situational awareness reports to inform incident commanders or unified command.
Before we touch on the present state of AI in emergency response management software, let’s get into the role of AI in emergency management as well as applications and related benefits of incorporating AI- and ML-driven technologies into your emergency response.
The role of AI in emergency management
The most important thing to know about AI-driven technologies in emergency management — or really, for any discipline where AI has practical applications — is that they don’t operate autonomously unless enabled. For emergency management, AI tools are designed with a human-in-the-loop approach, in that a human is always present to interact, intervene, and use their critical judgment to control or change any part of the process or its outcome as needed.
With that in mind, there are some considerations when incorporating AI- and ML-driven technologies into an emergency management plan, incident action plans, and an IMS.
While AI-enabled tools for emergency management aren’t autonomous, they can perform tasks for which humans were previously responsible on an automated basis. To enable this, you would need to grant security permissions to these tools that are commensurate with the tasks you’d like it to handle.
Therefore, to best protect your organization and any data for which you have the responsibility of security, it’s incumbent on you to perform your due diligence when evaluating AI-integrated emergency management software, just as you would with any other third-party tool or solution.
Additionally, your response teams must keep the inverse principle in mind — that AI is merely a functional part of a tool designed to enhance their emergency management response capabilities but shouldn’t be expected to run the whole operation. Your teams still bear the responsibility of making every decision, critical or otherwise, until the full extent of the damage is stemmed, the disruption is repaired, and normal operations have been restored.
The applications and benefits of AI in emergency response
As a topic, AI has dominated the tech conversation for the last 12 to 18 months, especially given the emergence of multiple large language model (LLM) platforms, chatbots, and other AI-driven tools with which people have increasingly interacted. But regarding emergency management and emergency response planning, much of the broad practical involvement of AI remains theoretical as of publication, with only some verified use cases available.
But as AI technology is continually improving, a breakthrough can propel rigorously tested and ready-to-go functionality into the emergency management and emergency response space at any moment. So, let’s talk through some of the specific applications and relevant benefits of predictable scenarios for when this technology rolls out more widely for use in this sector:
Crowds and traffic
One of the primary objectives of emergency response is to get as many people as possible to clear the area quickly, both to relocate them to safe environments and to make room for emergency personnel to perform their tasks. However, finding a safe pathway for people gathered in a crowd can be difficult — and if a crowd is beyond a certain size, sending it down some avenues may introduce new dangers, such as a potential crowd crush.
Vehicles within an emergency environment present similar difficulties. Depending on the type of emergency, some roadways may be unexpectedly obstructed or completely blocked, leaving fewer avenues for pedestrian vehicles to clear the area and emergency vehicles to enter. Most importantly, the people within those vehicles must get to safety as fast as possible, but without creating a potential choke point or jamming the roadways altogether.
When granted access to cameras, AI-enabled emergency response software can help assess crowd size and crowd density and recommend safe pathways for crowd dispersal to avoid creating crush situations, leading to safer crowd dynamics on the ground as the area is cleared. Similarly, AI analysis can assess which roadways are no longer safe or available for use and recommend which ones teams can use to guide pedestrian vehicles away safely.
Digital mapping of low-visibility environments
Visibility is an essential attribute of emergency assessment, helping response teams evaluate an environment’s magnitude of damage, areas of safety, and its most pressing needs. Some emergencies may occur in areas where weather patterns or other external factors limit visibility or may have visibility limitations that can make them more difficult to assess due to the nature of the emergency itself, such as a smoke-filled home or building.
For situations like this, AI-enabled emergency response software can assess floor plan data from a combination of sources, such as CAD files, PDF documents, and photographs, and quickly generate a 3D interactive indoor map that’s accessible on an array of smart devices. These models help emergency response teams identify safe entrances and exits, giving them vital spatial intelligence to move into dangerous environments.
Language detection and translation
Another essential attribute of emergency assessment is accounts from those affected on the ground. But there’s no guarantee that witnesses who come forward to offer vital information to emergency response teams and the teams themselves speak the same language, creating the possibility that a language barrier could prevent those teams from getting the information they need for situational awareness as fast as possible.
Thankfully, AI-enabled platforms can offer real-time language detection and translation services that are scalable across multiple devices, so different team members taking accounts from witnesses speaking different languages can do so at once. Gaining access to valuable information about an emergency environment by enabling witnesses to speak in their native languages, thus improving information quality, helps response teams make better decisions.
Sound filtering
During an emergency, the ability of critical data to flow where it’s needed relies on the dependability, usability, and accessibility of the communication channels that carry it. This extends to the sound quality of audio messaging and radio traffic originating within the emergency environment, where many factors can inhibit sound quality with interference, distortion, or high volumes of background noise.
To combat this, AI-enabled emergency response platforms can apply filters to incoming or outgoing audio that diminishes background noise and isolates principal messages, enabling response teams to hear vital sounds amid chaotic environments, such as mayday calls. This keeps communication between emergency response team member clearer and shortens the amount of time before emergency teams can hear a distress call, thus potentially saving lives.
Video analysis
Modern cities have fleets of surveillance cameras from which emergency response teams can draw feeds to observe emergencies as they progress, assess situations, and determine next steps. But the given the sheer volume of available footage, it can be hard to parse vital information by looking through each feed at a time, gathering helpful data, and analyzing it against such data from each successive feed until a clear picture emerges over time.
This is where the analytics capability of AI-enabled emergency response software can prove its value. The AI-powered elements of these tools can continuously perform algorithmic analysis across an entire network of cameras at once, detecting individuals, identifying key factors, isolating precise locations, and recommending the dispatch of emergency services as they’re needed, helping response teams to find and treat people in need of assistance more quickly.
Reports and documentation
Emergency response teams are trained to frequently compile available data into detailed reports that inform the common operating picture (COP) upon which an incident commander or the unified command of an Emergency Operations Center (EOC) relies for situational awareness. These reports are critical to assisting those atop the chain of command in making informed decisions, as well as documentation that accounts for all decisions made in the course of incident response, measuring their efficacy, and making adjustments in the future.
Presently, emergency response management software with AI and ML elements can quickly and accurately assess troves of data from multiple sources, synthesize them with running news feeds and other info streams, and synopsize them into clear, accurate captures of the site as events unfold. This way, incident commanders can share a unified COP with all team members while the team continuously meets all standards for transparency and accountability.
How AI-enabled emergency response management software helps today
Since its use can materially impact life-and-death circumstances, AI-enabled emergency response and emergency management technology is being rigorously tested and deployed to small-market departments and teams before rolling out more broadly, albeit in a slow and deliberate manner. The companies that produce these platforms are exhibiting the utmost caution and responsibly releasing features that they’re certain will improve response efficacy.
For instance, integrated resilience software providers only include AI-enabled reporting and documentation assistance as of publication, but with plans to expand into other AI-driven capabilities that can help your organization bolster your resilience posture and be ready to assess and mitigate the damage and disruption of your next emergency as it arrives.
With a seamless AI integration, these solutions lets your emergency response teams quickly generate summaries or other data outputs like:
- safety alerts, to inform those directly affected or potentially in harm’s way
- current COPs, to inform critical decision-making
- executive briefs, to inform stakeholders as events unfold
- Business Impact Analyses (BIAs), to assess any further risks of disruption
- incident action plans, for uniform direction across team members
- synopses of actions taken during incident response, for full accountability
- summaries of outcomes, to measure the efficacy of actions taken
The difference between your emergency response team acting and acting quickly can be the difference between safety and danger, disruption and regular order, and possibly life and death. By generating clear, accurate content in real time for a variety of vital functions, software like Noggin enables your team to make informed decisions faster, communicate more effectively, and keep all parties apprised of the situation as needed, with transparency where it counts.
Don’t wait until the next emergency strikes — request a demo of Noggin today and see for yourself.
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