Introducing OpenBlue Intelligence: Turning building data into intelligent action

AI starts with an AI-ready data foundation

Advanced AI capabilities depend on connected, reliable and accessible data.

OpenBlue Intelligence is built on an open, unified data platform that harmonizes and normalizes building data into a single, AI-ready foundation. Data from building systems across a portfolio can be brought together using standardized data models, real-time access and developer-ready APIs.

That foundation, combined with deep building domain expertise from Johnson Controls, can power advanced analytics, AI agents, automated workflows, autonomous controls and custom applications. It also helps reduce integration complexity and provides a scalable platform for intelligent decision-making across the portfolio.

From insights to outcomes

AI can help facilities and operations teams analyze building data, uncover trends and anomalies, identify faults and recommend actions. Agentic and autonomous capabilities take that intelligence further by making it easier to access, helping automate workflows and enabling building systems to continuously optimize performance.

Across OpenBlue Intelligence, that progression can take several forms:

Together, these capabilities help organizations save time and operational costs, reduce energy consumption and emissions, and ensure precision and uptime in mission-critical environments. OpenBlue Intelligence helps accelerate progress toward those outcomes by incorporating conversational AI that makes relevant building insights easier to access and act on.

One intelligence layer, multiple paths to better performance

OpenBlue Intelligence brings together AI-powered applications, intelligent automation and autonomous building performance across a range of OpenBlue solutions and capabilities.

Harmonized, AI-ready, developer-friendly data: Unify and normalize building data to support analytics, agentic experiences and custom applications across a portfolio.

Airside optimization: Continuously optimize building performance with AI-driven control to reduce energy use, improve comfort and enhance operations.

Waterside optimization: Use automated, real-time operating decisions to improve central plant efficiency, reliability and sustainability.

Fault detection and diagnostics (FDD): Continuously monitor equipment performance, identify current and emerging issues, prioritize them based on impact and receive actionable recommendations.

Energy conservation measures: Analyze energy data across sources including utility bills and submeters to identify savings opportunities, potential regulatory penalties and actions to reduce energy use and carbon emissions.

Conversational AI: Ask natural-language questions and instantly receive energy performance insights, fault diagnostics and conservation recommendations without navigating multiple applications and dashboards.

Ask your building anything

Real estate and facilities teams often have to navigate multiple systems and reports to find the building performance information they’re looking for. OBI, the new OpenBlue conversational AI agent, provides a single, intelligent interface to access actionable insights from energy, fault and utilization data.

Users can ask questions in natural language, such as “What is the monthly electricity consumption for the last year?” or “What are the highest priority faults in the last month?”, follow guided prompts, revisit past conversations and provide feedback to improve the experience over time. The result is faster decision-making, improved productivity and a more intuitive way to interact with buildings.

Moving from reactive operations to predictive control

OpenBlue Energy & Comfort Intelligence (ECI), powered by Nantum AI, extends these capabilities from analyzing and recommending to continuously optimizing building operations.

ECI uses real-time building data and advanced AI to continuously optimize HVAC and airside systems. Inputs including HVAC data, occupancy and zone conditions, and equipment performance metrics inform AI-driven decisions such as optimal start and stop times, ramp-down periods and static pressure resets. These optimizations can improve energy efficiency while maintaining balanced airflow and indoor environmental conditions.

ECI is designed for digitally enabled buildings with existing BMS and connected systems and can scale across sites and portfolios. It extends existing investments through native integration within the OpenBlue ecosystem rather than requiring standalone tools.

Turning intelligence into measurable building outcomes

These technologies may address different operational challenges, but together they ultimately support the three primary outcomes at the heart of OpenBlue Intelligence.

Save time and operational costs

AI-powered FDD can help teams detect and prioritize faults faster, while predictive insights can identify equipment issues before they occur. AI recommendations and automation can also support more efficient load distribution, setpoints and runtime management.

Reduce energy consumption and emissions

Automated setpoint optimization and intelligent runtime management can reduce energy waste, while AI-powered energy conservation measures can identify opportunities based on utilization, equipment performance and peak demand. Airside and central utility plant optimization can further improve efficiency through real-time operating decisions.

Ensure precision and uptime in mission-critical environments

FDD and condition-based monitoring can help predict equipment failures before they occur, while real-time monitoring and autonomous adjustments can help maintain precise environmental conditions and eliminate drift.

From connected buildings to autonomous performance

The next step for building AI is not another dashboard. It is intelligence that helps teams act faster, automate smarter and continuously improve performance. With OpenBlue Intelligence, organizations can advance from simply connected buildings to buildings that can learn, adapt and perform with greater autonomy.