In today’s increasingly connected world, facility managers face a critical question: how do we transition legacy building stock into intelligent, responsive assets that drive operational efficiency, sustainability, and occupant well-being?

In this NFMT Baltimore session, the path to smarter buildings is laid out in a detailed, stepwise framework — focusing on data centralization, analytics integration, system interoperability, and the progression toward autonomous operations.

The industry remains rooted in legacy infrastructure. An estimated 99 percent of buildings today are classified as "legacy" — facilities where standalone systems such as thermostats and basic BMS platforms operate in silos without real-time intelligence. Moving along the maturity curve, buildings first become connected (centralized control and monitoring), then smart (interoperable systems with analytics-driven insights), and ultimately autonomous (systems capable of self-optimization).

At the heart of any smart building initiative is data. Modern facilities generate vast volumes of information — from IoT sensors, utility meters, and HVAC/BAS controllers. However, without a structured, secure approach to capturing and managing this data, the opportunity for insight is lost.

A robust smart building platform must integrate diverse data streams via protocols like BACnet, Modbus, LonWorks, and MQTT, funneled through secure gateways to a centralized independent data layer (IDL). Structuring data using frameworks like Project Haystack or Brick Schema enables advanced analytics and AI-driven decision-making.

Facilities equipped with structured data can deploy scalable applications for energy management, fault detection and diagnostics (FDD), and predictive maintenance. Instead of relying solely on alarms or manual trending, modern platforms apply machine learning models to detect anomalies, predict failures, and optimize asset performance based on real-time occupancy, energy usage, and environmental quality metrics.

The role of AI — including machine learning, deep learning, and emerging generative AI (GenAI) technologies — is rapidly advancing. In smart building applications, AI automates complex tasks such as load forecasting, dynamic HVAC control, and root-cause analysis. GenAI, in particular, enables natural language querying of operational data, empowering non-technical staff to access insights traditionally reserved for specialists.

While full autonomy remains aspirational for most portfolios, staged deployments allow facility teams to iteratively integrate automation — first through data visualization and fault detection, then optimization, and finally closed-loop control strategies.

Watch the video to learn more and get best practices for smart building alignment from Brian Haines, Chief Strategy Officer, FM: Systems and Robbie Davis, Senior Product Manager, Johnson Controls.

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