The AI Imperative in PropTech: Lessons from Building the Future of Smart Buildings
Why the $65B building automation market is about to be transformed—and what I learned leading product strategy through the shift
Every commercial building in America has a dirty secret: millions of dollars of “smart building” technology that nobody uses.
I’ve seen the dashboard graveyard. As the Head of Product at JLL Technologies, I visited hundreds of buildings equipped with sophisticated sensors, energy management systems, and analytics platforms. In most of them, facility managers had reverted to the same routine they used before the technology arrived: walk the building, check the equipment, respond to complaints.
The technology wasn’t broken. It was irrelevant.
This is about to change. Not because sensors are getting cheaper (they are) or because buildings are getting more complex (they are). It’s changing because AI is finally capable of doing what dashboards never could: making decisions.
Here’s what I learned about building for this future—and why the next three years will determine who wins the $65B building automation market.
The Dashboard Graveyard
Let me describe a typical “smart building” in 2023.
The building has sensors everywhere: temperature sensors, occupancy sensors, air quality monitors, energy meters, equipment monitors. These sensors feed data into a building management system (BMS), which aggregates it into dashboards.
The dashboards are beautiful. Real-time energy consumption. Floor-by-floor occupancy heat maps. Equipment performance trends. A facility manager could spend hours exploring the data.
None of them do.
Here’s why: The data doesn’t tell them what to do. It tells them what’s happening—but translating “what’s happening” into “what I should do about it” requires expertise, time, and mental energy that facility managers don’t have.
A typical commercial building has 15-20 major systems: HVAC, lighting, elevators, security, fire safety, water, waste, and more. Each system has its own data. A facility manager responsible for a 500,000 square foot building might receive 1,000+ alerts per day across these systems.
No human can process this. So they don’t. They develop heuristics: check the important stuff in the morning, respond to complaints, handle emergencies. The dashboards become decoration.
When we surveyed facility managers at JLL, we found that the average “smart building” dashboard was accessed less than twice per week. The median time spent per session was under three minutes.
Millions of dollars in sensors. Minutes of attention.
The Insight That Changed Everything
The breakthrough came from a simple observation: Facility managers don’t want data. They want recommendations.
Not “your HVAC system in Zone 3 is running 15% above baseline.” That’s data.
They want: “Adjust the Zone 3 setpoint by 2 degrees. This will save $340/month with no impact on occupant comfort. Here’s why.”
The difference seems subtle. It’s not. The first requires the facility manager to interpret data, evaluate options, assess tradeoffs, and make a decision. The second requires them to say yes or no.
This is what AI enables. Not better dashboards—fewer dashboards. Not more data—more decisions.
At JLL, we rebuilt our IoT analytics platform around this principle. Instead of presenting data and hoping operators would act on it, we built an AI layer that analyzed the data and generated specific, actionable recommendations.
The results were dramatic. Customer acquisition increased 40%. Not because the underlying sensors were better—they were the same sensors competitors used. The difference was that our platform reduced cognitive load instead of adding to it.
Three AI Applications That Actually Matter
Based on my experience, three AI applications in smart buildings deliver genuine ROI. Everything else is either premature or overhyped.
1. Predictive Maintenance That Pays for Itself
Every building has equipment that’s going to fail. The traditional approach is reactive: wait for failure, then fix it. The “advanced” approach is preventive: replace equipment on a schedule regardless of condition.
Both are expensive. Reactive maintenance leads to emergency repairs, tenant complaints, and secondary damage. Preventive maintenance replaces equipment that still has useful life.
Predictive maintenance uses AI to analyze equipment behavior and predict failures before they happen. But here’s the key: the prediction alone isn’t valuable. The value is in the recommendation.
A useful predictive maintenance system doesn’t just say “this motor will fail in 30 days.” It says: “Schedule maintenance for this motor during the building’s low-occupancy window next Tuesday. Here’s the work order. The estimated savings vs. reactive repair is $4,200.”
That’s a decision, not data.
As we are building Synovai, we are implementing this at scale across our client’s managed portfolio. The buildings that adopted AI-driven predictive maintenance saw 25% reduction in emergency repairs and 15% reduction in overall maintenance costs.
2. Occupancy Optimization Beyond Simple Counting
Most occupancy sensors count people. How many are in the building? How many on each floor? This data feeds into dashboards that facilities teams never look at.
AI-enabled occupancy optimization does something different. It asks: Given current and predicted occupancy, how should the building behave?
If a floor is 20% occupied, should you condition the entire floor? Or can you dynamically adjust HVAC zones to serve only the occupied areas? If occupancy patterns predict heavy use of the east wing in the afternoon, should you pre-condition that space?
These decisions require real-time data, predictive modeling, and integration with building systems. A human can’t make them at the speed and granularity required. AI can.
We saw buildings reduce HVAC energy consumption by 18-22% through AI-driven dynamic zoning—without any reduction in occupant comfort. The savings paid for the technology in under 12 months.
3. Energy Management That Adapts
Energy management used to mean scheduling. Lights on at 7am, off at 8pm. HVAC starts an hour before occupancy.
AI-enabled energy management is adaptive. It responds to weather forecasts, utility rate changes, occupancy patterns, and equipment status in real-time.
Here’s a concrete example: Grid operators increasingly offer demand response programs that pay buildings to reduce consumption during peak periods. But capturing this value requires rapid response—sometimes within 15 minutes of a request.
An AI system can evaluate a demand response request, identify which loads can be curtailed with minimal occupant impact, execute the changes, and document the response for billing—all automatically. A facility manager would need hours to do the same analysis.
We saw buildings generate $50,000-$150,000 annually in demand response revenue that they’d previously been unable to capture, simply because the decision-making was too slow.
Why Most PropTech AI Fails
If AI is so valuable, why aren’t all buildings using it? Three reasons.
The Integration Problem
A typical commercial building has 50+ distinct systems, most of which were installed at different times by different vendors and don’t communicate with each other. The HVAC system doesn’t know what the occupancy sensors see. The lighting system doesn’t know what the energy meters measure.
AI needs data. Fragmented systems produce fragmented data. Before AI can deliver value, someone has to solve the integration problem—which is expensive, time-consuming, and often requires replacing legacy equipment.
At JLL, we spent significant resources on integration middleware. It’s not glamorous work, but without it, the AI layer has nothing to work with.
The Trust Problem
Facility managers have been burned by technology before. They’ve seen the dashboard graveyard. They’ve experienced systems that generated alerts but no value.
When AI starts making recommendations, their first reaction is skepticism. Why should I change my setpoint? How do I know this won’t cause problems?
Building trust requires explainability. Every recommendation needs to come with reasoning that a facility manager can understand and verify. “Adjust setpoint by 2 degrees because current outdoor conditions and predicted occupancy suggest comfort can be maintained at this level, based on similar conditions last month when no complaints were received.”
AI systems that deliver recommendations without explanations don’t get adopted. We learned this the hard way and redesigned our interface to lead with reasoning, not just results.
The ROI Problem
AI requires investment before it delivers returns. Sensors, integration, software, training—all cost money. The returns come later, in reduced energy consumption, lower maintenance costs, better tenant satisfaction.
CFOs want payback calculations. But building performance is noisy—influenced by weather, occupancy, tenant mix, and dozens of other variables. Isolating the impact of AI is genuinely difficult.
We developed measurement frameworks that tracked savings against weather-normalized baselines and comparable buildings. Proving ROI isn’t optional; it’s a prerequisite for scale.
The 2026-2029 Window
I believe the next three years represent a critical window for PropTech companies.
Three trends are converging:
First, AI capabilities have crossed a threshold. Large language models can now interpret building data and generate natural-language recommendations that facility managers actually understand. Computer vision can analyze building conditions from images and video. The technology is ready.
Second, sustainability mandates are creating urgency. The EU’s Energy Performance of Buildings Directive, New York’s Local Law 97, and similar regulations worldwide require buildings to dramatically reduce carbon emissions. Compliance requires technology that most buildings don’t have.
Third, post-pandemic uncertainty about office usage is forcing building owners to rethink their operations. Fixed schedules don’t work when occupancy is unpredictable. Dynamic, AI-driven building management is no longer a luxury; it’s a necessity.
The companies that capture this moment—by solving the integration problem, building operator trust, and demonstrating clear ROI—will define the smart building category for the next decade.
Those that don’t will join the dashboard graveyard.
I spent years building AI-driven products for commercial real estate. I’d love to hear from others in the PropTech space: What’s working? What isn’t? And what are facility managers actually willing to adopt?