Imagine being able to talk to your building, almost literally, discussing the condition and operation of its valves, sensors, and controllers. Picture a virtual assistant to help troubleshoot temperature complaints or suddenly high energy bills, without ever having to physically visit office spaces and mechanical rooms or scroll through screens of schematics. Instead, you ask a question of an Alexa-like agent who’d report back with the data you’ve requested.
While this might sound like some sort of Star Trek vision of the future, these are some of the benefits artificial intelligence (AI) could bring to the building management system (BMS) of the future. The building automation industry has made some tremendous strides since the introduction of direct digital control (DDC) systems in the late 1970s and early 1980s. Adding agent-based AI interfaces could just be the next big leap forward.
The DDC foundation
The movement toward advanced building controls dates back to the adoption of DDC designs by leading BMS developers in the 1980s. They replaced old-school pneumatic approaches and enabled BMS companies to begin introducing intelligence into what had been mechanical, time-clock-style control systems.
Over time, these new capabilities included some of the earliest applications of machine learning (ML) in BMS designs—a major subfield of today’s AI. With ML, computer-based systems learn to make predictions or decisions from data rather than acting on explicitly programmed rules. This technology would eventually be used to develop optimal start/stop capabilities that are now standard in today’s smart thermostats.
The AI-advantage
Early-generation devices operated in a simple on/off mode, responding only to the time clocks to which they were wired. Today’s advanced systems, however, can learn and adapt. By analyzing factors such as outdoor temperature, humidity, and room occupancy, they can determine precisely how long it will take to reach the desired room-temperature setpoints under varying conditions. This enables them to optimize operations in real time, maintaining consistent comfort regardless of the weather.
Over the past 40 years, these systems have continued to evolve—particularly in their networking, analytics, and reporting capabilities—but optimal start/stop logic remains core to their effectiveness.
Feeling the difference
Clients who transition from analog pneumatic systems to modern digital controls often notice the impact almost immediately. Those daily hot/cold adjustments fade away as comfort becomes truly automated. This is especially true on multi-building campuses, where temperature management can feel like a full-time job; the time savings can really multiply quickly.
And, of course, the energy savings are equally significant. As control systems learn the relationship between indoor and outdoor conditions and track occupancy patterns, they are able to fine-tune HVAC operations so that heating and cooling are delivered only when and where they’re needed. The resulting reduction in utility costs can often pay for the upgrades far sooner than expected.
Today’s budding AI growth
While ML remains a foundational AI building block, its intelligence is limited to specific tasks, such as managing space-conditioning operations based on a defined set of variables. However, technology has advanced to the development of large language models (LLMs)—a type of ML that draws on exponentially larger amounts of data and has much broader capabilities.
Have you used ChatGPT or Claude? If so, you’ve seen firsthand how these LLMs can assist in tasks ranging from historical research to complex coding questions by utilizing their enormous data caches. These models have the potential to unlock new levels of automation, efficiency, and comfort in building operations.
Toward a future where buildings talk back
While we’re still in the early days of this new AI age, we’re already seeing promise in using LLMs enriched with client data to answer basic operational questions. Instead of scrolling through logs or navigating menus, users can ask, “Were any thermostats out of setpoint last night?” or “Which floors are being used most?” This sort of instant access to building information is invaluable to building operators.
Soon, we expect AI to take another step forward by combining client BMS data with utility rate schedules to automatically calculate cumulative costs and energy savings, eliminating the need to run manual calculations.
Fast-forward a few years, though, and AI might be an even bigger part of a facility manager’s daily life. What this might look like is still undetermined, but the race is on to figure it out, and the potential is enormous.
OpenAI, ChatGPT’s parent company, recently partnered with the Apple iPhone designer in a deal to create AI native devices that could bring the technology as close as your wristwatch, eyeglasses, or lapel pin.
For building personnel, this could feel like entering Star Trek territory, providing conversational voice access to servers’ worth of data on operational history, equipment specifications, and current conditions. Of course, it will still be some time before your AI assistant can actually “beam you up.”
Would you like to continue this conversation? Reach out to learn more about how these new technologies can benefit your team.
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