Getty Images/iStockphoto


How AI adds value to crisis communications systems

AI provides key enhancements to existing emergency notifications systems that can reduce the amount of time a business needs to effectively prepare for and respond to a crisis.

Crisis communications have come a long way from call trees and text chains. Today's emergency notification systems and cloud-based notification services are far more effective than relying on employees to call each other.

However, these developments have not made crisis communications foolproof. For example, if emergency messages never reach their intended recipients, the sender might not get a notification of the message delivery failure. If a reply message is not generated, an organization's emergency teams could be facing an incident that escalates into a full-blown crisis due to the lack of clear communication.

Artificial intelligence (AI) and machine learning (ML) are highly proficient in capturing a wide variety of data inputs and then making predictions and emergency recommendations. Organizations can use these technologies for identification and classification of emergency tasks, as well as to provide communications and intelligence at the right time and to the right people. AI has a role to play in the future of crisis communications, and it's only just getting started.

What does AI bring to the table?

AI and ML can provide additional value to emergency notification system (ENS) technology. Today, ENSes are generally programmed to disseminate a variety of message types, such as email, text and SMS, to preset lists of individuals. While some more traditional systems can request replies from message recipients, AI-enhanced systems can do that and more.

AI crisis communications systems can use multiple channels of information to provide value to emergency message delivery. These channels can include weather forecast data or drone-generated video, among others. An AI-enabled ENS, for example, can take weather data generated by the National Oceanic and Atmospheric Administration and translate it into forecast data that can then be formatted into a series of alert messages helping people to prepare for an impending hurricane or other severe weather.

AI crisis communications systems can use multiple channels of information to provide value to emergency message delivery.

Another example of AI-enhanced crisis communications is using the system to ask specific questions about a situation, such as the likelihood of tornadoes or other natural disasters forming. The system can examine multiple resources to provide message recommendations and other analyses.

Inclusion of AI and ML technology is increasingly found in ENS offerings from traditional vendors as well as messaging system vendors. It is up to the user to determine which AI-enabled capabilities will be best suited to the organization and how it will add value to corporate ENS requirements. Non-AI systems will still provide rapid dissemination of emergency messages, and many can support reply messages, so at that point the added edge -- and expense -- of AI becomes a business decision.

AI-enhanced vs. traditional ENS

Earlier ENS technology was largely on site, with a server designated to provide ENS functions connected to either landlines from the local telephone company or via the internet to deliver messages. Figure 1 depicts how a traditional premises-based ENS uses the internet to deliver messages.

Diagram of a non-hosted ENS
Figure 1. A non-hosted emergency notification system.

By contrast, today's systems are often hosted by a specialized ENS vendor, with the technology in the cloud. All resources are located with the vendor, and access is as simple as using a laptop or smartphone. Figure 2 depicts a hosted ENS configuration. Users are completely dependent on the ENS vendor to deliver emergency messages when the system launches.

Diagram of a hosted ENS
Figure 2. A hosted emergency notification system.

When AI and ML are in the mix, the configuration is largely unchanged except for the added capabilities of the ENS when AI and ML are implemented. Figure 3 shows a possible configuration of an AI-enabled, cloud-based ENS.

Diagram of an AI- and ML-enabled ENS
Figure 3. An AI- and ML-enabled emergency notification system.

Traditional ENS message delivery and reply features are enabled, and AI capabilities add value by using a variety of other resources.

Market options and pricing

Prices for standalone crisis communications systems can range from under $5,000 to well over $200,000.

Managed ENS offerings usually require payment of a monthly fee for the service. This is typically based on the number of contacts in the database, the features being used and the network transport services that deliver the messages. There can also be activation fees when the system is used in a disaster, and some systems will have setup fees. Monthly fees can range from under $500 to over $25,000, depending on the system configuration.

Hosted ENS tools require no physical space for equipment, there are minimal or no upfront installation fees, and customers can discontinue service with minimal technical effect on the organization. The inclusion of AI features will vary by vendor, and organizations should carefully research all options before making a buying decision.

Organizations that already use emergency notification systems will need to evaluate the added value versus the cost to upgrade their existing tool to an AI-enabled one. For example, an existing system might not be upgradeable to one with AI, and a replacement would be needed.

There are several crisis communications vendors that offer AI- or ML-enabled platforms and products. Vendor options in this market include the following:

  • Omnilert started as the developer of a campus emergency communications system. Current hosted products use AI to detect, analyze and visualize emergency situations through intelligent data capture and analysis, and the products offer an easy-to-use interface. Omnilert offers a free trial; check with the vendor for more pricing information.
  • Quiq provides an AI-enabled messaging platform that businesses can adapt to different situations, such as customer order placement and customer service inquiries. Although ENS is not specifically listed as an application, the Quiq platform is easily adaptable to crisis communications applications. Pricing begins at $12,000 per year.
  • OnSolve offers a variety of hosted ENS tools. It also has an AI engine to provide emergency intelligence that businesses can use for decision-making. Pricing ranges from a basic system for under $2,000 to more complex systems with a variety of pricing plans.
  • Everbridge offers numerous ENS options for many different applications and uses AI functionality to analyze data from multiple sources to provide intelligence for emergency management. The company offers on-site as well as managed emergency notification services, with fixed and monthly pricing plans.

Dig Deeper on Disaster recovery planning and management