AI in Smart Buildings: What Can It Actually Do?

Artificial intelligence (AI) is becoming an increasingly familiar part of the built environment. From building management systems to energy monitoring and facilities management, new applications are changing how building information is collected, analysed and used.

But what does AI actually do in a smart building? And how does it differ from the technology that buildings have been using for years?

While AI offers a range of applications for building owners and facilities teams, it is important to distinguish between established building technologies, automated processes and the newer capabilities associated with artificial intelligence.

Understanding where AI fits into building operations helps organisations make more informed decisions about the technology they invest in and the benefits they can realistically expect.

What is AI in smart buildings?

AI in smart buildings refers to the use of artificial intelligence to analyse building data, recognise patterns, support operational processes and assist with decision-making.

Buildings already generate considerable amounts of information through Building Management Systems (BMS), Internet of Things (IoT) sensors, energy meters, access control systems and other connected technologies.

AI can help make sense of this information by identifying relationships, highlighting unusual readings and processing information that would otherwise take considerable time to review manually.

However, not every connected building uses AI. Traditional building automation, for example, can control heating, lighting and ventilation according to predefined rules without involving artificial intelligence.

The distinction matters. A building can be highly automated without using AI, while AI applications can work alongside existing building systems to provide additional analysis and support.

How is AI being used in smart buildings?

AI has several practical applications across building management and facilities operations. Some are already being explored and implemented, while others remain at an earlier stage of development.

1. Energy management and building performance

Energy management is one of the most established areas of interest for AI in buildings.

Heating, ventilation and air conditioning (HVAC) systems, lighting and other building services account for a significant proportion of operational energy use. Managing these systems effectively requires an understanding of how buildings are being used and how their equipment is performing.

AI can analyse information from energy meters, environmental sensors and building management systems to identify patterns in consumption and highlight areas of unusual energy use.

For example, analysis might identify:

• Areas where energy consumption is consistently higher than expected.
• Heating or cooling operating outside normal occupancy hours.
• Differences in energy use between similar spaces.
• Changes in consumption that may warrant further investigation.

These insights can give facilities teams a clearer understanding of building performance and help inform decisions about energy management.

The International Energy Agency's 2025 work on data-driven smart buildings explores how data analytics and AI applications can contribute to improved energy performance, alongside the digital infrastructure needed to support them.

2. Occupancy and space management

Understanding how buildings and individual spaces are used is another important part of smart building management.

Occupancy sensors, access control systems and other connected technologies can provide information about how frequently spaces are occupied and how their use changes throughout the day.

AI can help analyse this information, identifying patterns in space utilisation and highlighting differences between how buildings are designed to be used and how they are actually occupied.

For example, this could help organisations understand:

• Which areas of an office are used most frequently.
• Whether meeting rooms are being used efficiently.
• How occupancy varies across different days and times.
• Whether the available space reflects the needs of its occupants.

For organisations managing large estates, this information can support more informed decisions about space allocation, building operation and the use of existing facilities.

3. Fault detection and building systems monitoring

Modern buildings contain a range of interconnected mechanical and electrical systems. Monitoring their performance is an important responsibility for facilities teams, particularly across larger or more complex estates.

AI-based analysis can help identify unusual patterns in system data that may indicate an operational issue.

For example, a change in temperature readings, unexpected energy consumption or irregular equipment operation could be highlighted for further investigation.

This is particularly relevant to heating and ventilation systems, where faults and inefficient operation can affect both energy consumption and the internal environment.

Research published by the US National Institute of Standards and Technology (NIST) in 2025 examines the use of AI-based software to automate fault detection and diagnostics in mechanical systems within large commercial buildings.

These tools do not remove the need for engineering expertise or physical inspections. Instead, they can help teams identify areas that need attention and provide additional information when investigating building performance.

4. Finding and understanding building information

Another developing application of AI is the way people interact with building information.

Facilities teams often need to access a wide range of documents and records, including asset manuals, maintenance instructions, technical drawings, compliance information and equipment specifications.

Finding the correct information can be time-consuming, particularly when records are stored across different systems or document repositories.

AI-assisted search tools and natural language interfaces can make this information easier to explore. Rather than relying entirely on specific search terms or navigating multiple folders, users can ask questions in everyday language and retrieve relevant information from accessible sources.

For example, a facilities manager might want to find the maintenance documentation for a particular asset or locate information relating to a building system.

The usefulness of these tools depends on the information available to them, as well as the accuracy of the results. Appropriate access permissions and human verification remain important, particularly when dealing with technical or safety-related information.

What are the challenges of using AI in smart buildings?

Despite its potential applications, introducing AI into building operations is not without challenges.

Data quality: AI tools depend on the information they receive. Incomplete, inconsistent or outdated records can affect the quality of the results.

System integration: Building information is often distributed across different platforms and technologies. Bringing these sources together can be technically complex, particularly in older buildings with legacy systems.

Cybersecurity and privacy: Connected building systems generate and process information that needs to be managed securely. Organisations must consider data protection, access permissions and the security of their systems.

Cost and implementation: Introducing new technology requires investment, planning and an understanding of the problem it is intended to address. AI should be considered in relation to a clear operational requirement rather than adopted simply because it is available.

Human oversight: AI can assist with analysing information, but it does not replace the experience and judgement of the people responsible for managing buildings. Results need to be understood, checked and used appropriately.

For building owners and facilities managers, these considerations are just as important as the technology itself.

What does AI mean for the future of smart buildings?

AI is adding new ways to analyse building information and support facilities management. However, its value is closely connected to the quality of the underlying building systems and information.

A building does not become smart simply by introducing more technology. Sensors, automation, data platforms and management systems all need to work together in a way that supports the people operating the estate.

This is where connected building information becomes particularly important.

A Lifecycle Digital Twin platform, such as Twinview, brings together information from different building systems, assets and documentation within a single operational environment. This provides facilities teams with greater visibility of their buildings and makes important information easier to access.

While AI and digital twin technology serve different purposes, they are increasingly relevant to the same conversation about how building information is managed and used.

Ultimately, the most important consideration is not how much technology a building contains, but whether that technology helps people understand and manage it more effectively.