AI in the building maintenance industry: pros & cons
Words by Mike Talbot, CTO at SFG20, the industry standard for building maintenance
Artificial Intelligence is no longer a buzzword or a futuristic concept. Across many sectors, AI is being adopted in various ways, with the building maintenance industry closely following behind.
AI in the building maintenance industry leverages machine learning algorithms and data analytics to enhance efficiency, cost-effectiveness, and overall performance of building maintenance processes. Used for predictive maintenance, energy management, and smart building management systems, AI technologies come with a host of benefits.
Recent technologies can summarise maintenance schedules for quick reference, highlighting asset types and locations, easing the process for engineers and facility managers. Similarly, AI is capable of analysing asset register data to automatically identify and map appropriate maintenance schedules to each asset – a task that frequently takes months to do manually.
What are the pros of using AI technologies in a building maintenance strategy?
- Cost reduction
AI can automate the repetitive tasks associated with setting up maintenance plans and ensuring they are optimal and legally compliant. Efficient and effective maintenance relies on a strong understanding of the facilities and assets that require maintenance and an informed plan to address them. AI provides a new capacity to create a deeper understanding that would be unfeasibly costly to implement using human resources.
AI can be used to minimise maintenance costs by decreasing unplanned downtime and optimising energy consumption.
As AI provides real-time data analysis and insights that can help facility managers make more informed operational decisions, this can reduce the likelihood of costly errors in judgment.
2. Time-efficiency
By automating tasks, AI tools can help maintenance professionals work more efficiently by streamlining processes and cutting down on timely repetition. AI software tools can assist with many traditionally manual tasks, speeding up repetitive processes and helping to standardise data. In the long term, this can save those responsible for building maintenance both time and effort, which can be used in more strategic, high-impact areas.
3. Maximises asset lifespan
AI-powered solutions can extend asset lifecycles by predicting potential failures before they occur. By recommending the best timing for maintenance (not applicable for statutory requirements), AI can, in turn, prevent both over-maintenance, which can cause unnecessary wear, and under-maintenance, which can lead to premature failure.
4. Provides more reliable reporting and analytics
AI in facilities management is incredibly helpful for collecting, analysing, and simplifying the array of information involved with building maintenance and its associated responsibilities, including diagnosing problems.
Machine-learning AI can interpret data from various sources, including sensors and real-time user inputs, to identify patterns and make intelligent, personalised predictions about building management needs, for example, previous patterns of equipment failure.
In the past, data had to be sorted manually, with decisions often being made without clear, evidence-backed reasoning. Now, through machine-learning AI, data reporting and analytics can be achieved faster and more reliably than ever before, lowering the risk of human error.
What are the cons of leveraging these technologies?
- High initial costs
Despite its benefits, the implementation of AI in building maintenance is not without challenges – the most obvious being the initial cost of deploying AI systems, such as sensor installation and integrating AI solutions. This upgrade can be particularly costly for older buildings.
Implementing AI systems can require significant upfront investment in hardware, software, and training, so careful planning and budgeting are essential for a successful and cost-effective onboarding.
2. Data Privacy and Security Concerns
As AI systems collect and process large amounts of sensitive building and occupant data, this may cause privacy and cybersecurity concerns for an organisation.
As AI systems require high-quality, consistent data to function effectively, poor or incomplete data can lead to inaccurate insights or decisions. In other words, AI is only as good as the data that goes into it.
Ensuring this data is collected, stored, and used in compliance with privacy regulations is crucial.
3. Overreliance
Industry professionals must always approach AI adoption with caution and not fall into a cycle of overreliance. Artificial intelligence tools are not infallible; they require rigorous data validation and continuous human oversight. Facility management systems are complex and demand nuanced interpretation that AI algorithms may not fully comprehend
