Building management has hugely evolved over the past decade, driven by innovations in technology and data analytics, including AI. AI has great potential for helping facility managers face challenges such as operational efficiency, sustainability, high costs and fewer staff.

Tools like generative AI can provide quick and useful insights to help solve these problems. A survey from JLL showed that 81 percent of real estate companies want to invest more in new tools. They view AI as a major change for the industry. In addition, the use of Internet of Things (IoT) devices in buildings is expected to grow by 13.7 percent annually until 2028.

The path to using AI has not been simple. Many still feel uncertain because of previous issues with automation. A large number, about 59.1 percent, of facility managers say they do not have a plan for AI. This shows that many feel unsure about how to use AI in real building tasks.

Building and facility managers must navigate uncertainty to maximize AI’s role in managing buildings. To do so, they must recognize the common misunderstandings plaguing the industry and how technology has developed, as well as be aware of helpful tips and current trends so they can confidently understand and handle changes.

The start of uncertainty: Automation that looks like AI

Many people are uncertain about AI. Most early AI products were marketed as easy solutions for saving energy, cutting costs or increasing comfort. However, many of these products failed to fulfill expectations.

These early tools had simple rules for automation that needed to be trained for months to function. Slow learning processes caused delays in experiencing tangible benefits and increased the chance of making mistakes when they were first set up.

As a result, FMs worry about the "automation trap." They fear that the tools which are sold as clever and easy solutions need regular, complicated checks to prevent unforeseen issues.

Past projects have led to buildings replacing expensive equipment even when unnecessary, a direct consequence of misused AI models. HVAC and energy systems can go off track, leading to shutdowns or higher usage and creating more worries and caution for building managers.

In contrast, large language models (LLMs) operate differently from many other AI models today. LLMs learn from large amounts of data and do not need months to be useful, quickly providing help by understanding natural language. They can make summaries, give advice and help people understand difficult data. Instead of simply completing tasks without thinking, LLMs empower people in decision-making and provide clear and smart solutions from the start.

The data challenge: Siloed vs. Unified data

The large amount of data from building management systems, meters, and the IoT has brought in a lot of new data from sensors, which is overwhelming for many managers. Without tools to organize and understand it easily, data can become more of a problem than a solution.

Another significant challenge is that the data is siloed; there are weak or no links between systems. HVAC, lighting and security systems work separately and are not connected in most cases, making it extremely difficult to monitor the whole building’s performance. Siloed data and disparate systems mean that FM teams are at risk of doing the same work twice, incurring expensive hardware upgrades and straining staff and resources.

A complete AI solution requires clear, organized, and easy-to-understand data. It is essential to ensure that data from different areas — like a building, floor, zone or room — is all the same. This method is better than sorting by vendor or device type and is a crucial step. Without it, even the best AI tools will not yield good results.

To make systems work together, FMs need more than just tools. It is also essential to cooperate across different teams and with outside partners. Managers, IT workers, people working in the environment and those who offer help must agree on data rules and plans. They also must understand how the systems connect. This teamwork is essential and often overlooked, but it plays a crucial role in using AI well.

A new way: AI as a tool to enhance, not replace

The new kind of AI is different from older models. It is clearer and works better with people. Generative AI and machine learning tools are here to help facility managers; they are not meant to take their jobs. These systems act like assistants, analyzing data, identifying problems and giving helpful suggestions while allowing humans to remain in control.

AI is not just a simple tool that works well by itself; its real power is in helping people make smarter choices. This allows workers to move faster and plan better, and stronger productivity is directly linked to increased profitability. A report from PwC says AI could add US$15.7 trillion to the world's economy.

Importantly, the role of AI in buildings is more about understanding than just automation. It helps monitor energy use, keep an eye on equipment, identify problems and even predict future trends in a group of buildings. It is vital to remember that AI considers human feedback so experts can make the final choices.

The shift to generative AI introduces new features such as natural language interfaces, predictive questions and chat-based analysis, and these tools help staff use AI more easily. Now, AI is accessible to ordinary users, not just data scientists or engineers.

A benefit of this new method is that it can provide support that understands the situation. Instead of relying on strict programming, AI can learn and change based on the needs of a building and its people. It offers help based on specific settings, use patterns, and how people behave. This creates a better support system for the teams that run it.

Strategic considerations for AI implementation

For successful implementation, managers should first develop a clear plan. This involves a thorough review of the existing infrastructure, including both hardware and software. Selecting AI tools that integrate seamlessly with current systems is crucial to prevent disruptions and reduce the need for additional tools. The AI partner can provide valuable assistance with software and hardware evaluations, helping to create a cost-effective strategy and achieve financial savings

It is important to set clear goals from the start. This can mean lowering energy use by a certain amount or cutting downtime. Having goals helps teams find the best solutions and see how well they work over time.

Additional considerations include:

  • Knowing whether cloud or on-site solutions are better for an organization’s IT setup.

  • Identifying how often the AI model requires updates.

  • Ensuring data privacy, security, and rules are being followed.

  • Understanding who in the organization is responsible for the AI plan, from teams to IT to sustainability.

Organizations should also consider the total cost of ownership. This means looking beyond licensing and implementation, as well as training, support and system upgrades. A full-cost model can help ensure that the AI solution provides value throughout its life.

AI in energy management & sustainability

AI is great at saving energy. Buildings create over 40 percent of carbon emissions globally, so there is a big opportunity for change. AI can monitor energy usage in real time. With this data, managers can spot wasted energy and act immediately.

An AI assistant can significantly improve energy data management in a building by automating the collection, organization and analysis of energy usage data. Instead of manually sifting through spreadsheets or disparate systems, FM teams can rely on AI to identify trends, flag anomalies and suggest energy-saving opportunities.

It can also simplify reporting, generate visualizations and predict future consumption based on historical patterns. This not only saves time but enables more proactive, data-driven decisions that support sustainability goals and cost efficiency. These LLM-based AI solutions can easily and widely automate improvements with humans in control.

Studies show that AI can lower emissions from commercial buildings by as much as 19 percent. To reach climate goals for 2050, worldwide retrofitting rates need to increase significantly. AI is not only transformative to improving operations; it is also a key tool for promoting sustainability, simplifying reporting and helping FMs meet environmental, societal and government goals and certifications.

AI helps FM teams understand how their work affects sustainability. It shows the energy they save, the carbon they cut down and ways to improve.

Building the future: Scalable, human-centered AI

Scalability is vital for companies that have many locations. Modern AI solutions are designed to be changeable and adaptable and can work with various system setups without needing many adjustments.

FMs should incorporate AI platforms that help them adapt. The built environment is always changing, and the tools must match new systems, user needs and rules for sustainability.

The best AI is all about people. It gives clear answers instead of causing confusion, helping managers make better and faster choices. Thanks to the smart use of AI, the buildings of the future will not only be smart, but they will also be strong, adaptable and able to work together.

As AI tools evolve, the CRE industry can expect closer ties with platforms for user experience, security and money management. This connection means that building managers will oversee the buildings and shape how people live and work in them.

Embracing this future means thinking about what can be done. AI will not just monitor and run systems but will play a big role in planning, operations, and strategy. As this development continues, those who have built a strong and adaptable base will be in the best place to use it fully.

Even though past mistakes are still shaping current perceptions, the future of AI in building management seems bright. With the right knowledge, strong data, and a good outlook, organizations can move past their worries and see AI as a helpful partner.

AI is here to help teams manage buildings. It improves their skills, simplifies tasks and helps them access better results for people, profit and the planet. Those who understand this change now will lead the future of the world’s buildings and spaces.