Interest in AI is showing no signs of slowing down, and businesses are doing everything they can to rapidly deploy AI into their infrastructure for energy efficiency, occupant comfort and data-driven decision-making. Gartner recently found that 29 percent of organizations are already deploying generative AI. There is increasing recognition that AI can be effectively deployed both in the cloud and at the edge, depending on the specific application. While cloud-based AI offers significant benefits, not all AI solutions must rely solely on the cloud. As organizations strive for more energy-efficient and human-centric buildings, one concept is rapidly evolving from a buzzword to a fundamental component: AI at the edge, also known as edge AI.

While traditional AI models depend on centralized cloud computing to transmit data from local devices to remote servers where powerful algorithms analyze it and send back instructions, AI at the edge processes data locally. The localization of AI in on-premises sensors places AI directly within building hardware, like sensors and other connected devices.

The result in the built environment? Autonomous decision-making and optimization without ever needing to send data to the cloud, reducing the burden of continuous internet connectivity and remote data storage. Systems like HVAC, lighting and environmental controls can learn, predict and act in real time. This real-time insight, zero-latency responsiveness and significantly lower energy costs can help facility managers confront rising energy costs, evolving regulations and growing occupant expectations.

AI at the edge is also particularly suited for retrofit projects wherein legacy systems may not support cloud integration, and it can be faster and easier to deploy. This technology can cover the entire commercial building segment, as it can operate independently for buildings without building management systems (BMS) or collaborate with on-premise or cloud-based BMS, which give flexibility for the deployment of this technology.

The 5 phases of AI at the edge

At its core, AI at the edge enables sensors and controllers to process environmental data — like temperature, humidity and occupancy — as it happens. These systems learn behavioral patterns, anticipate needs and adjust conditions to ensure both energy savings and comfort. Because they operate independently of cloud-based processing, responses are immediate.

Here is how a room controller equipped with embedded AI could execute a five-phase algorithm:

  1. Measure – Collect environmental data through onboard sensors (temperature, humidity, occupancy).

  2. Assess – Evaluate this data using internal models of thermal behavior and comfort.

  3. Predict – Anticipate optimal temperature trajectories based on current and forecasted conditions.

  4. Control – Dynamically adjust setpoints and operational timing to maintain comfort.

  5. Learn – Continuously refine the model through feedback loops, improving performance with each cycle.

Consider a room controller equipped with edge-based intelligence: rather than relying on preset schedules or waiting for distant servers to crunch historical data, it responds to what is actually happening in the space and adjusts the temperature to meet ASHRAE 55 comfort standards with immediate, localized precision. That means smarter heating or cooling cycles and faster adaptation to sudden changes in occupancy with no internet connection required.

This level of room controller responsiveness has shown real-world results: up to 15 percent in energy savings and 85 percent of the time in comfort compliance due to the preventive action of the edge AI.

The widespread benefits of keeping AI localized

AI at the edge can lay the groundwork for smarter, more adaptive buildings that continue to improve themselves long after installation. This continuous feedback loop allows the building to function almost like a living organism, adapting to its surroundings and improving autonomously without requiring human intervention for adjustments.

Rather than acting as passive data collectors that funnel information to a remote server, these devices become both the source of data and the decision-making engine. Localized processing dramatically reduces the need for large-scale data transmission, which in turn cuts down on latency, lowers energy use associated with data transfer and removes reliance on persistent internet connectivity. The result is a system that is inherently more robust and resilient, even in the face of network interruptions.

Security is another critical benefit of keeping AI localized. By avoiding the transmission of sensitive building and occupancy data to external cloud servers, AI at the edge sharply reduces the attack surface available to bad actors. With no central data repository and fewer entry points for cyber threats, the entire system becomes more defensible.

Operationally, this model also eases the strain on IT infrastructure. Controllers designed to run AI at the edge typically include a higher number of physical terminals. These additional connection points support a wide range of hardware and software integrations and create an expanded capacity that allows for broader application integration without needing to overhaul existing infrastructure. Buildings in locations with spotty internet access or limited technical resources can still access advanced, self-optimizing capabilities. Facility teams are no longer burdened with managing large volumes of cloud-based data or maintaining constant connectivity to support centralized systems. Instead, because edge AI operates independently of high-bandwidth cloud requirements, it scales more easily, whether across a downtown high-rise, a rural school district or a sprawling corporate campus.

Real-world applications: Smarter comfort, lower costs

AI at the edge is already transforming the way commercial buildings operate. In offices, it helps cut daily HVAC energy use by as much as 15 percent, depending on how busy the space is. Hotels are seeing big benefits as well, with room controllers syncing effortlessly with building systems to create smarter, more responsive guest experiences while keeping energy bills in check. At a time when the built environment is responsible for nearly 40 percent of global CO2 emissions, the ability to deploy smart systems that reduce consumption without compromising performance is vital.

Beyond HVAC, edge-based systems can also tie in lighting and window shade controls, creating a more unified and responsive environment that finds savings in every corner. An example of this is automated demand response, which can predict peak load periods and autonomously adjust usage without human input while staying within compliance frameworks. Automated demand response is critical in easing pressure on the electrical grid, especially during high-demand periods when energy costs and carbon intensity peak. By shifting or shedding loads intelligently, buildings can reduce operational costs while contributing to broader sustainability goals and grid resilience.

Ultimately, AI at the edge represents a fundamental shift in designing, operating and optimizing the buildings around us. By placing AI directly into the devices that shape indoor environments, FMs gain a powerful tool: one that delivers immediate control, measurable savings and long-term resilience without the overhead of complex infrastructure. As organizations face mounting pressure to make their buildings more efficient, adaptive and occupant-friendly, edge AI offers a grounded, scalable path forward.