Think of a smart building as a self-driving car for energy and operations: sensors constantly watch what’s happening (people, temperature, light, equipment), and AI decides when to heat, cool, light, or ventilate each space so you use the least energy without sacrificing comfort.
Traditional buildings waste large amounts of energy because heating, cooling, lighting, and ventilation are run on fixed schedules or manual rules that don’t reflect real usage. AI for smart buildings optimizes these systems in real time, cutting energy costs and emissions while maintaining or improving occupant comfort and operational reliability.
Integrated, longitudinal building data (BMS/BAS + IoT sensors + occupancy + weather + tariffs), domain-specific control policies, and deep integration into building workflows and infrastructure create switching costs and performance advantages that are hard to replicate quickly.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Integration complexity with heterogeneous building management systems and IoT devices, plus the need for robust, low-latency control loops and reliable, high-quality sensor data at scale.
Early Majority
Positions AI not just as an add-on analytics layer, but as the core brain for continuous, closed-loop optimization of building energy and operational performance across HVAC, lighting, occupancy, and maintenance—moving from static rules to adaptive, learning-based control.