Facilities management is entering a new phase with intelligence, foresight, and strategic value at the forefront. As buildings generate more data than ever before, facilities leaders now have the opportunity to turn that information into competitive advantage.
Artificial intelligence (AI) is a practical way to transform fragmented building data into proactive operational intelligence. More importantly, it represents a fundamental mindset shift: from buildings being a source of problems, to buildings becoming active participants in solving them—from efficiency and sustainability to resilience and growth.
This opportunity is here today. Nearly 60% of operations leaders report flat or tightening budgets, even as buildings grow more complex while global Internet of Things (IoT) data volumes surpass 300 zettabytes annually. Adoption data suggests leaders in the field are already taking advantage of these capabilities: 92% of organizations have piloted AI tools in commercial real estate use cases, and 28% have embedded AI across facilities operations. The organizations that succeed will be those that harness data they already have to make faster, more confident decisions.
Buildings generate continuous streams of data across HVAC systems, sensors and meters, security cameras, access control, and more. On their own, these data streams remain siloed. AI connects them by contextualizing equipment performance history, energy use, occupancy trends, weather patterns, and external environmental factors. Effective AI tools go a step further by automatically translating both structured or unstructured data to reveal important insights teams can act on, revealing early indicators of equipment health, inefficiencies, or changing usage patterns. These tools are also changing the way teams access the information by putting key insights and recommended actions in front of facilities managers at the outset—gone are the days of combing through dashboards and spreadsheets.
Consider a hypothetical example. AI analyzes historical patterns of actual meeting room usage versus scheduled bookings and finds that certain conference rooms are consistently reserved, but used only about 60% of the time. By cross-referencing badge access data with occupancy sensor readings, the system can adjust HVAC to serve only occupied spaces, avoid wasting energy on empty rooms, and anticipate end-of-day patterns, pre-conditioning spaces based on actual departure times rather than scheduled end times. That’s AI helping you understand all that information and—even more important—taking appropriate action.
The Operational Impact of Proactive Intelligence
This data-driven approach is already delivering measurable results across commercial and industrial facilities. Predictive analytics can cut unplanned downtime by up to 50%, helping teams spend less time reacting and more time optimizing. Meanwhile, smart energy management platforms can drive 20-30% reductions in energy consumption, cutting one of the highest controllable costs.
In one real-world example, a hospital implemented AI analytics and control in its central utility plant to save $170,000 in annual energy costs. One report found that organizations can achieve these kind of results and more with the help of AI-powered technology—including up to 10% reduction in energy costs, 67% reduction in chiller maintenance activity, saving nearly $1.5 million over three years, and a 155% ROI and an eight-month payback period.
It’s no surprise that adoption continues to accelerate: 40% of commercial real estate firms are already using AI for predictive maintenance or tenant engagement, and 84% of commercial building decision-makers plan to increase their use of AI to optimize operations.
Why Historical Data Is the Missing Link
AI is only as effective as the data behind it. While real-time data is critical, predictive maintenance depends on historical context—long-term performance trends, seasonal shifts, and past failure patterns.
Yet data from connected buildings is often underused. BAS trend logs get stored but are rarely analyzed. Work order history sits siloed in CMMS platforms. Energy data is disconnected from occupancy or weather. Failure records aren’t tied back to root causes. And data from different sources varies in format and structure, making it difficult to combine complex datasets.
Without that historical data, reactive work continues to account for over 50% of maintenance activity in facilities environments, despite growing investments in AI. However, when historical datasets are connected to AI solutions, building operators can turn years of operational memory into foresight, unlocking the full context needed for AI to deliver its greatest value and converting maintenance into a predictive, proactive exercise.
How Facilities Teams Can Activate Historical Data
Commercial buildings generate massive volumes of operational data through IoT sensors, automation systems, and maintenance platforms. But volume alone does not create insight.
To operationalize historical data, facilities leaders should focus on finding the right AI tool to support their work. The amount of data involved can be daunting, but effective tools allow teams to automate analysis and seamlessly integrate data. Facilities managers should look for tools that can:
- Consolidate data sources. Bring together BAS logs, CMMS history, energy records, and occupancy data under a unified framework so AI has a complete picture.
- Normalize and contextualize datasets. Leverage modern building intelligence platforms that can automatically ingest both structured and unstructured data to help standardize naming conventions, timestamps, and records across systems.
- Integrate past and present data. Layer historical trends with live sensor data to give AI the context to identify meaningful anomalies—not just outliers in the moment, but deviations from established patterns.
- Establish feedback loops. Feed maintenance outcomes back into models to continuously improve accuracy over time.
These capabilities are key to fully leverage the power of AI, allowing it to identify degradation patterns earlier, refine replacement forecasts, and recommend maintenance timing based on real operating conditions—increasing confidence in automation and human decision-making.
AI Translating and Taking Action
With access to high-quality data, AI can actively optimize building operations, enabling facilities teams to strategize maintenance more effectively while improving performance and compliance outcomes.
A major research university campus demonstrates exactly this. By overhauling its energy infrastructure using an AI-powered building platform, the system continuously models demand and generates performance recommendations—operating in fully automated mode and serving as an advisory tool for human operators. Through this approach, the university generated a 17% reduction in peak energy demand, $500,000 in annual utility cost savings, and a 68% reduction in campus greenhouse gas emissions.
The Buildings Are Already Talking
Data infrastructure is a prerequisite to unlock the power of AI. Without it, AI remains experimental. With it, maintenance becomes predictive, energy use becomes optimized, and buildings become strategic assets—and the right tools make that transition easy and automated.
As facilities management becomes an increasingly data-driven exercise, teams must fine-tune AI-enabled solutions, implement platforms that automatically connect to and operate in existing systems, and contextualize building data to ensure seamless system integration.
Buildings are already talking—and the right tools are out there to help your team listen and act.
David Duncan is Senior Director of OpenBlue Engineering at Johnson Controls.