It’s no small wonder that AI has been quickly absorbed into proptech. Real estate and facilities management have always embraced tools that drive efficiency in the built environment. But it also means that property managers need to accelerate their digital strategy to keep pace.
“We are owners of concrete, steel and earth, but we have to transform into a tech business. That’s because we might be leasing for $30 a square foot, but there’s $1,000 of value associated with it,” said Troy Harvey, CEO and founder of PassiveLogic. “Buildings are no longer just an asset—they are a digital platform with services plugged into it.”
Learn how AI-enhanced proptech is blazing trails in three key areas: design, operations and space planning.
1. Design
AI’s most prevalent influence for architects is during the ideation phase. AI can help capture the original design intent and iterate variations quickly. The process also integrates stakeholders into design conversations at a more interactive level.
“AI-enhanced design tools are helping instill greater confidence in owners,” said Brendan Mullins, architect and design computing discipline lead with Stantec. “We’re going from napkin sketches to renderings in 20 seconds. We can swap design options during a client meeting rather than waiting a week. We can even take a photo of a cardboard layout mockup and swiftly convert it to a rendering.”
AI is also streamlining the design process once it moves into structural and mechanical engineering. Especially when supporting BIM, AI is another layer that adds additional power and value.
“3D modeling is the design heart,” Mullins emphasized. “Everything plugs into it: VR for immersive experiences, sustainability tools for energy forecasting and now AI.”
2. Operations
A fully automated building may seem like science fiction, but this reality is edging closer thanks to AI’s ability to handle complex mathematical calculations. Because mechanical systems are based on industrial process controls, physics-based AI platforms like PassiveLogic are a far different application than the language learning models most people are familiar with. Where they show the most promise is the ability to harness the millions of points on an IoT network and have them work together.
“We have this rich world of IoT devices, but they’re all islands. They do cool things on their own, but they don’t talk to other sensors or participate in a sequence,” stressed Harvey. “We need a brain to unify them so systems can respond dynamically. For buildings to meet their end goals, they need to act on their own. That starts with a building that has an understanding of itself so it knows what to do with real-time information.”
The reason this has remained elusive is that buildings are far more intricate than even a self-driving car. Autonomous vehicles have an average of 12 sensors, whereas a mid-scale commercial building easily has 500-1,000 IoT points; the largest skyscrapers are in the 1 million range, according to Harvey. Without a “brain” coordinating the building’s behaviors, all of these points are disconnected from one another.
“The reality is that buildings are incredibly complex robots,” Harvey emphasized. “Even though they are stationary and bolted to the ground, they have the same set of programming challenges to get them to make decisions. The key difference is that they’re moving through time, not space.”
This is where AI can oversee the multitude of interconnected variables that govern a building and respond in an intelligent manner. Especially when digital twins are paired with AI, building owners can start to account for every factor that influences operations.
“This is a far cry from BMS sequences, which are a static ‘if this, then that’ parameter. Instead, it’s realizing that the air temperature isn’t just one point that represents comfort—it’s actually one of 10 variables that factor in gender, age, and metabolic rates, as well as environmental variables like humidity, air flow, and radiant temperature,” explained Harvey. “The building then has to balance human needs with operational needs like energy conservation and utility price volatility. This demands dynamic decision making that AI can enable.”
3. Space Utilization
How do you measure occupancy when there’s a continual shift in people at the office? This question has sharpened in importance after pandemic conditions, especially for companies that have settled into a hybrid schedule. JLL is using artificial intelligence to capture occupancy trends so space can be better optimized.
“Occupancy fluctuations are causing a greater need for intentionality about the office. As a result, the way a space is finetuned could differ on a weekly, even daily, basis,” said Christina Gratrix, senior director of product management for JLL. “But to achieve real-time occupancy management, we need to look at sources of data that weren’t traditionally used for space planning but are now relevant for hybrid work.”
This means calculating show-up rates using badge swipes, room and desk reservations, and occupancy sensors. This can help shed light on the ideal ratio of private vs. collaborative areas.
“One of the barriers to dynamic occupancy is combining different data sets that have traditionally been siloed. Our AI platform supports data compatibility using aggregation, mapping and standardization,” Gratrix explained. “By marrying those sources, we can generate insights into who planned to come, who actually showed up, and what space they used.”
For example, many companies are finding their pre-2019 layouts no longer match the realities of today’s work. Data could demonstrate this when a six-person conference room is consistently booked by three people. This finding then becomes the rationale for expanding the number of small conference rooms.
“The workplace exists for the workforce,” stressed Gratrix. “AI is helping us understand the story that data is telling about space usage, allowing us to improve the employee experience.”
