How AI supports building decarbonization goals

While buildings are making progress toward decarbonization, facilities teams still face a practical challenge: finding and acting on energy-saving opportunities hidden across complex, disconnected building systems.

A United Nations Environment Programme (UNEP) report shows that global building energy intensity has fallen 8.5% over the past decade, while green building certifications have nearly tripled. But there is much more work to do. With buildings and construction responsible for around 37% of global CO₂ emissions, UNEP estimates that investment in energy efficiency must more than double by 2030 to keep climate goals within reach.

Much of that investment will come in the form of upgrades to building systems and equipment, as well as deployment of digital solutions that will change the way facilities are operated and maintained. AI can help organizations move from fragmented, reactive improvements to coordinated, continuous optimization, maximizing the impact of those investments while supporting sustainability goals and delivering meaningful business benefits.

Here’s how.

Decarbonization starts with the data

Reducing building emissions starts with understanding where energy is being used, where it is being wasted and where interventions can have the greatest impact. Most building systems already produce the data needed for those insights, but that data is often fragmented across energy meters, utility bills, building management systems and siloed legacy platforms.

AI depends on connected, contextualized data to generate useful insights. Bringing disparate building data together can create a more complete picture of how a facility is performing and give AI the foundation it needs to identify patterns, uncover inefficiencies and recommend actions.

The OpenBlue Data Platform is an open, unified data foundation that connects and harmonizes building data from disparate sources. OpenBlue Intelligence adds the AI layer, turning that contextualized data into actionable insights, intelligent automation and autonomous optimization. Together, they help organizations move from fragmented information to coordinated decisions and action across building operations.

Finding the opportunities that matter

Once building data is connected, AI can help facilities and real estate teams move beyond top-line energy consumption to understand where, when and why energy is being used inefficiently. Combining disparate data streams like building utilization, equipment performance and weather reports makes it easier to pinpoint where, when and why energy is being used inefficiently. These insights can help facilities teams prioritize energy conservation measures based on their potential to reduce both carbon emissions and costs.

OpenBlue Intelligence puts these capabilities into practice by using AI to identify patterns and anomalies in building data. The AI can then translate those findings into actionable recommendations for improving energy and operational performance. Combined with emissions tracking and automated reporting, these insights can help organizations establish baselines, track results and measure progress toward their decarbonization goals.

Bridging recommendations and action

AI’s contributions to energy optimization don’t end with recommending next steps for humans to take. Agentic AI can also put those recommendations into action. Agentic AI surfaces insights, recommends next steps and executes approved workflows, shortening the path from detection to action while keeping people in control of important decisions. This helps facilities teams respond faster without adding another layer of manual work.

OBI, the agentic AI assistant within OpenBlue Intelligence, brings those capabilities to day-to-day facility operations. Teams can use text or voice to access information about energy use, equipment performance, occupancy and other building conditions. OBI can then recommend actions and execute approved workflows, such as prioritizing maintenance or placing work orders.

By helping teams move more efficiently from identifying an issue to addressing it, agentic AI can support the ongoing operational improvements needed to make progress toward decarbonization goals.

Turning insights into continuous optimization

AI can go a step further by continuously optimizing building operations without requiring frequent human intervention. By responding autonomously to changing conditions within defined parameters, AI can reduce unnecessary energy use and associated emissions while allowing facilities teams to focus on higher-level decisions.

For example, AI can analyze factors such as occupancy, weather and system performance to predict building needs and optimize HVAC operations accordingly. Automated adjustments to airflow, temperature, schedules and setpoints enable facilities to use less energy while maintaining occupant comfort, turning energy efficiency from a series of periodic interventions into an ongoing process.

OpenBlue Energy & Comfort Intelligence (ECI) is an AI-powered building optimization solution that puts this approach to work. OpenBlue ECI continuously optimizes HVAC airside systems to balance energy efficiency, occupant comfort and operational performance. It also provides visibility into energy use and emissions, helping organizations measure environmental performance, simplify ESG reporting and track progress toward decarbonization goals across their building portfolios.

Bringing intelligence to the central plant

Central plants present an especially complex optimization challenge. Equipment combinations, loads, weather, utility rates and operating requirements are constantly changing. AI can analyze thousands of variables and continually determine how equipment should operate to meet demand while minimizing energy use and costs.

OpenBlue Central Utility Plant Optimization (CUPO) leverages those capabilities by gathering data from connected equipment and external sources such as weather forecasts and utility rates. The AI layer within OpenBlue CUPO uses that information to automatically generate and implement optimization decisions, continuously adjusting plant operations to improve efficiency, reduce costs and support sustainability goals.

AI-driven optimization can also complement broader electrification and decarbonization strategies. At Children's of Alabama, OpenBlue CUPO supports automated real-time operating decisions for a central plant serving five major campus buildings. Combined with infrastructure upgrades, the hospital has reduced natural gas use by 69% and achieved approximately $870,000 in annual cost savings, including about $170,000 in energy cost savings attributed to OpenBlue.

AI delivers the operational agility decarbonization requires

Decarbonization is not a one-time project. Building use changes. Weather changes. Equipment performance changes. Energy prices and emissions factors change.

AI gives facilities teams a way to respond as those variables evolve. By turning complex building data into insights and automated actions, AI can help reduce energy waste, lower emissions and accelerate measurable progress toward decarbonization goals. OpenBlue’s AI capabilities can surface energy conservation opportunities, detect equipment faults that contribute to energy waste, forecast demand and automate adjustments based on real-time operating conditions.

Conversational AI can also make that intelligence easier to access. Facility and energy managers can ask questions about energy consumption, faults and conservation opportunities in natural language and receive contextual insights and recommendations without searching across multiple dashboards.

AI can help facilities teams move from periodic, limited efforts to continuous management of energy and emissions performance. By connecting building data, identifying the highest-value opportunities and automating approved actions, organizations can make building intelligence an everyday tool for advancing energy, electrification and decarbonization goals.