In an era in which disasters are increasingly complex, and technology’s role is ever-expanding, the landscape of emergency management is undergoing a seismic shift. Emergency managers face challenges ranging from escalating natural hazards and cyberthreats to pandemics that cripple nations. Leaders grapple with a lack of resources, an overload of incomplete and constantly changing information, and an ever-expanding scope of responsibilities. Amid this uncertainty, the emergence of powerful artificial intelligence (AI) tools generates excitement and apprehension, raising profound questions about the future of emergency response.
Countless articles and conference sessions describe the benefits and challenges of AI in emergency management, many of which describe a path forward for emergency managers who choose to explore these technologies. The imperative to embrace AI is no longer a choice but a necessity. The tipping point is here, and the consequences of inaction are too significant to dismiss. Emergency managers, already adept at coordinating human teams, increasingly must manage human-computer and computer-computer interactions in addition to traditional human-to-human collaborations. To successfully integrate AI into response operations, they must move away from simplistic viewpoints of human-machine interaction and instead look at how humans and AI agents can work together using a Joint Cognitive System (JCS) approach.
Work as a Cognitive Process
Before examining the role of computers, it is essential to understand the nature of work itself. Any work, even seemingly physical tasks, involves cognitive processes such as perception, decision-making, and problem-solving. Emergency management is no exception. Emergency managers must constantly assess situations, make decisions, and solve problems, often under intense pressure. They face a constant influx of information, needing to distinguish meaningful signals from noise and make decisions under conditions of uncertainty.
This cognitive work becomes even more demanding in large-scale distributed systems like those encountered in modern emergency management, where information is dynamic and often incomplete. Emergency situations often necessitate coordination between multiple emergency operations centers at different government levels. This collaboration extends to supporting nonprofit and private-sector organizations as well as dispersed teams and command posts in the field. In these environments, stakeholders must coordinate their actions and maintain a shared understanding of the situation, a concept known as common ground. The increasing speed and scale of operations further complicate these coordination demands on human cognition.
Rapid advancements in computing power and AI offer a range of tools to support and enhance cognitive work. However, the key lies in designing these systems to amplify human capabilities, not replace them. For instance, the development of the radio amplified an emergency manager’s ability to communicate with first responders in the field. AI can be a powerful partner, augmenting human expertise and judgment, not supplanting it. AI-driven data analysis can help emergency managers identify patterns in disaster response, and automated monitoring systems can provide real-time alerts, all while ensuring human oversight and strategic decision-making remain paramount.
The Joint Cognitive System
The concept of the JCS is the idea that humans and machines are not separate entities working in isolation. Instead, they are fundamentally interconnected and work together to perform tasks. Within a JCS, humans and machines form an integrated system, each contributing their unique capabilities to the collective effort.
The theoretical foundation for JCS can be found in the work of Erik Hollnagel and David Woods, luminaries in the field of resilience engineering. Both described the concept in their 1983 article, “Cognitive Systems Engineering: New Wine in New Bottles.” To successfully design and manage complex systems, Hollnagel and Woods argue that humans and machines must be considered an integrated whole.
In the context of emergency response, JCS means that emergency managers and AI agents work together, each contributing their unique capabilities to the collective response effort. AI agents, which are computer systems that can independently perform tasks and make decisions, can rapidly process and analyze large quantities of data, providing real-time situational awareness and predictive capabilities. Emergency managers, in turn, bring their expertise in decision-making, critical thinking, and leadership. Achieving this would require the reinvention of roles, with a heightened focus on responsibilities that demand a trusted human presence. Other high-risk professions that have successfully integrated automation while preserving the criticality of human-directed work offer valuable insight. For instance, despite the advanced capabilities of autopilot systems, passengers still anticipate being greeted by a reassuring captain upon boarding their flight.
AI-Driven Flood Response: A Glimpse into the Future?
In a future where emergency managers fail to rethink how to interface with computers, AI systems may operate independently, making critical decisions and disseminating information at speeds that outpace human response. This disjointed approach can lead to misaligned actions, confusion, and unintended consequences:
It is a stormy evening in a small suburban community. An advanced AI-powered forecasting system detects rapidly escalating rainfall intensity in the region. Within minutes, it predicts flash flooding in low-lying neighborhoods, pinpointing streets most likely to be inundated. The system issues an urgent alert directly to users’ smartphones, providing estimated timelines and localized flood maps.
Simultaneously, a real-time navigation platform integrated with the forecasting system updates its app with evacuation routes that avoid flooded roads and areas at risk. These routes are dynamically adjusted based on live data from the AI system and user-reported hazards.
However, this all happens before public officials have had the chance to issue an official warning. Emergency managers, still convening to assess the situation, realize that residents are already evacuating based on the AI-driven guidance. This premature movement creates both opportunities and challenges, as officials scramble to synchronize their messaging, verify the accuracy of AI-generated routes, and ensure that response efforts align with official safety protocols. Without a structured human-AI collaboration framework, emergency response efforts risk becoming fragmented, potentially leading to inconsistent communication, resource misallocation, and public confusion.
Now imagine a different future—one in which emergency managers embrace a Joint Cognitive Systems approach, fully integrating human expertise with AI capabilities to create a well-coordinated response effort. In this future, humans proactively interface with AI technology, ensuring that its outputs align with established emergency protocols and best practices for alerting and evacuation. Emergency managers work alongside AI systems to validate predictions, refine evacuation plans based on ground truth, and coordinate clear, consistent communication with the public.
By maintaining an active role in the decision-making process, emergency managers can harness the speed and precision of AI while applying human judgment to address uncertainties and unique community needs. This collaboration ensures a more adaptive and resilient response system—one that is proactive, data-driven, and ultimately more effective at protecting lives and property.
Challenges and Considerations for Designing Effective Joint Cognitive Systems
While AI integration holds great promise, it also presents critical challenges. One well-known issue is data quality and bias, as AI systems rely on the accuracy and comprehensiveness of their training data. Flawed data can lead to unreliable outputs, emphasizing the need for continuous efforts to ensure high-quality datasets and mitigate biases. By using the JCS approach, humans are more aware of how the entire system operates and, therefore, can better identify weaknesses.
Another challenge is system complexity, which increases the likelihood of unexpected failures. Emergency managers must understand the limitations of AI systems and their potential failure modes while maintaining the ability to perform tasks manually or with alternative methods when necessary. Despite significant progress toward “on-device AI,” many of the most robust systems rely on network connectivity to a cloud-based service.
The success of JCSs also depends on effective human-computer interaction design. Interfaces should be designed to be intuitive and user-friendly and provide clear feedback to the user. This requires a deep understanding of how humans perceive and process information, especially under stress. Additionally, these systems must support different levels of expertise, allowing users to interact with the system in a way that aligns with their experience and training.
Supervisory control and explainability are equally important to retain the ability to monitor and, when necessary, intervene in AI-powered systems. This requires appropriate levels of transparency in AI systems so that human operators can understand how decisions are being made and the rationale behind recommendations. Explainable AI, a growing area of research, focuses on making AI systems more transparent and understandable to humans, enabling trust and facilitating appropriate oversight.
What Should the Emergency Manager Do?
To fully utilize AI, strategic leaders should actively shape its integration by focusing on two critical areas: workflow redesign and workforce reskilling.
Workflow redesign involves a comprehensive analysis of existing processes, breaking them down into their individual components. This deconstruction allows for the identification of bottlenecks, inefficiencies, and tasks that are particularly well-suited for AI augmentation or automation. For instance, AI excels at rapidly processing large datasets, making it ideal for tasks like risk assessment, predictive modeling, and resource allocation. Automating routine tasks, including report generation or data entry, can free up personnel for more strategic and people-facing activities.
Workforce reskilling should focus on building human knowledge to work alongside AI, especially as some of their existing daily activities are eliminated. This includes fostering technical proficiency on new platforms alongside developing softer skills like critical thinking and decision-making in AI-augmented environments. Reskilling initiatives should also emphasize building trust in AI systems, ensuring that personnel are comfortable with technology and understand its capabilities and limitations. Prioritizing workforce development can ensure that teams remain agile and prepared to effectively navigate the complexities of JCS environments, especially given the historical lack of adequate training for the range of technologies already available to them. Comprehensive training would empower them to harness AI’s potential while maintaining control over critical decision-making processes.
Adapting to the Evolving Landscape
A recent announcement of a new AI model elaborated on the role that model played in pushing AI development into the “agentic AI” era, where AI can independently perform tasks and make decisions. This underscores the need for emergency managers working in a JCS to have a clear understanding of AI’s capabilities and limitations and to be prepared to intervene when necessary. For example, NOAA’s Next Generation Fire System (NGFS) leverages artificial intelligence to autonomously detect fires from geostationary satellite observations and rapidly communicate critical information to human forecasters and land managers, enhancing response times when swift initial action is crucial. Embracing a collaborative and responsible approach to AI integration can enhance effectiveness and improve outcomes in times of crisis.
Emergency managers can work effectively with helpful automation by adopting the joint cognitive systems paradigm. This approach offers a new way of thinking about the work strategic leaders perform. However, it remains to be seen whether they will adopt this mindset shift to address the unparalleled challenges confronting them.
For a deeper dive into JCS, check out more works by Erik Hollnagel and David Woods: Joint Cognitive Systems: Patterns in Cognitive Systems Engineering and Joint Cognitive Systems: Foundations of Cognitive Systems Engineering.

Justin Kates
Justin Kates serves as the senior business continuity advisor for Wawa, Inc. In this role, he is responsible for architecting a new business continuity program for Wawa’s expanding footprint of over 1,000 convenience stores with over 45,000 associates across nine states and Washington D.C. He is a Master Business Continuity Professional affiliated with Disaster Recovery Institute International. Kates attained a B.A. with a concentration in emergency management and public administration from the University of Delaware and an M.A. in security studies from the Naval Postgraduate School's Center for Homeland Defense and Security. He currently serves as a board member for the National Alliance of Public Safety GIS Foundation and is a member of the FEMA National Advisory Council.
- Justin Kates#molongui-disabled-link

Emily Martuscello
Emily Martuscello is a certified emergency manager and is currently the director of emergency management for Nashua, New Hampshire, where she leads city-wide preparedness, response, and recovery initiatives. With extensive experience in strategic leadership and crisis management, she holds a bachelor’s degree in political communications and emergency health services from George Washington University and an M.S. in executive leadership from Champlain College. Emily has been recognized for her innovative contributions to the field, including the integration of emerging technologies in emergency management and fostering cross-sector collaboration to enhance community resilience. She is a subject matter expert for the Center for Homeland Defense and Security Executive Education Program and is a member of Executive Leaders Program Cohort 2401.
- Emily Martuscello#molongui-disabled-link