What Physical AI Could Mean for SLED

Over the last few years, AI has been the hot topic of conversation in SLED IT. We’ve seen AI move from theoretical to actual implementation. SLED agencies are now deploying AI across a wide range of use cases including citizen services, workforce productivity, document processing, case management, fraud detection, and analytics and decision support, among others. While many of these address public sector challenges in the digital world, SLED governments also oversee the delivery of physical tasks, such as maintaining roads and infrastructure, waste management and disaster response. 

The rapid evolution of Generative AI has led SLED leaders to rethink their technology adoption strategy and become more innovative and flexible. As we move into a technology-driven future, the next iteration of AI and efficiency has the potential to help solve these day-to-day challenges in real-world environments.

From Generating Content to Taking Action

Across the SLED market, we are seeing continuing advancement of AI models, robotics, and simulations. These technologies aren’t new, but the intelligence behind them is evolving in a way that allows for more environmental variability. Physical AI has the potential to unlock new opportunities for agencies, whether that’s within transportation, public safety, emergency response or facilities management.

Physical AI differs from Generative AI in that it can interact, sense, and reason within the real physical world rather than existing only as software. By merging AI models with technology capabilities around robotics, sensors and autonomous systems, physical AI enables machines to analyze their environment, engage in reasoning and then identify an appropriate action.

Consider infrastructure inspection; instead of relying solely on employees to manually inspect bridges or roads, AI-enabled drones or robotic systems could capture imagery, identify potential damage and help prioritize critical issues requiring human support. 

Early examples are already emerging across several states. Kansas City, Missouri, for example, has an initiative underway that combines drones and AI to automate post-disaster damage assessments and minimize time spent by staff on information collection and processing. California has tested drones for remote, autonomous construction inspection with the goal of improving safety while minimizing the time and costs associated with traditional inspections. Alaska is using drones to support infrastructure inspections and situational awareness in remote areas.

States are also investing in the supporting infrastructure necessary for more advanced autonomous systems and operations. For example, North Dakota’s statewide Vantis network supports drone applications ranging from infrastructure inspection to emergency response.

These early initiatives help preview how AI, autonomous systems, and intelligent infrastructure have the potential to converge across the public sector.

How Physical AI Could Fit Within SLED

There is no doubt that physical AI has significant potential to unlock new opportunities and address an array of challenges across the public sector. Thinking about where emerging technology is being used within the SLED verticals today, a few notable applications stand out:

Transportation and Infrastructure: autonomous systems, computer vision and intelligent sensors can support infrastructure inspections, traffic management and safety, transit operations and maintenance. 

Public safety and emergency response: drones, robots, and other autonomous systems can support situational awareness, search and rescue, disaster assessments, or operations in dangerous environments.

Utilities and public works: physical AI can be used for inspection, monitoring, and maintenance across complex infrastructure environments.

Education: robotics and autonomous systems can enhance research, workforce development, and hands-on training as institutions prepare students for a technology-driven economy.

Naturally, not all applications will be fitting for every agency or vertical, and many use cases are in early stages of development. However, the rapid evolution of AI and innovation, along with the potential capabilities and benefits, are worth continued attention.  

The Opportunity Extends Beyond Robots

For industry, the physical AI story is not limited to the question of robots. The real opportunity lies in the foundational technology ecosystem. To effectively operate in real-world environments, physical AI needs support from cameras and sensors to collect information, edge computing for efficient processing, networks to transmit data, storage for data retention and robust cybersecurity to safeguard devices and information. 

Other advanced technologies like simulation and digital twins can support these environments; for example, virtual environments can help prepare these systems for real-world deployment through scenario testing and training.

For vendors and partners, the growth and evolution of physical AI may create downstream requirements and opportunities across networking, compute, edge infrastructure, data management, cybersecurity, digital twins and professional services.

New Capabilities Bring New Questions

Looking ahead, physical AI could introduce additional challenges beyond those resulting from software-based AI. GenAI has generated a multitude of questions and concerns around safety, reliability, and security, and those considerations will likely be amplified if there’s a shift from these systems operating in the digital to the physical world.

SLED agencies may have considerations around how autonomous systems make decisions, how those decisions are regulated, how these systems handle deviations from the norm, and the role of human oversight. The role of security is paramount given the change in environment and the level of risk that comes with such autonomous systems. For instance, a challenge arising from an implemented chatbot will have different risk and response solutions than autonomous system responsible for emergency management.

In the future, these factors and considerations will likely make governance, testing, and security key components of any public-sector physical AI strategy.

What Industry Should Watch

The reality is physical AI is unlikely to transform SLED overnight. But neither has generative AI.

For industry, the focus today is less about the logistics of widespread adoption and more about understanding where AI may be headed and which existing customer priorities may intersect with it down the line.

When we think about what exactly SLED leaders care about, transportation modernization, public safety and emergency management, constituent healthcare, and how these priorities are already aligning with technologies like edge computing, IoT and digital twins, the conversation begins to flow more holistically. As we move into an increasingly technology-driven future, the next chapter of AI may not be limited to the digital world. The physical environments SLED agencies operate in today may become the next frontier for intelligent technology.

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About the Author:
Yvonne Maffia is the senior analyst covering state, local and education markets. She applies insights and analysis to purchasing trends to help vendors and partners shorten their sales cycles. Prior to joining TD SYNNEX Public Sector, Yvonne spent 8 years working in state and local government, where she oversaw advisory boards across the State of Florida and served as an analyst to a local politician. Yvonne currently lives in Washington, DC.