As more government workers return to in-person work, there’s a growing need for efficient, reliable maintenance at government facilities. AI-backed scheduling leverages real-time data and advanced analytics to predict and prevent equipment failures, helping to optimize resource allocation and reduce unexpected downtime. Facility and maintenance managers can deliver a safe, comfortable environment that supports government employee well-being and productivity.  

The growing need for maintenance at government facilities 

According to a recent report from the General Accounting Office, the cost for deferred maintenance has increased from $216 billion in 2022 to $370 billion in 2024, which is more than double the $171 billion it was in 2017. The Canadian government’s real estate property portfolio has an estimated $14 billion in deferred maintenance, with some departments holding a high percentage of facilities in critical condition, according to Canadian Treasury Board report.  

While in the United Kingdom, the National Audit Office, a public spending watchdog, estimates the government’s maintenance backlog at least $64 billion. The number is just a best guess, though, and the Office of Government Property believes it could be substantially higher. Much of the government’s data on the condition of its properties is incomplete and out of date.  

Adding to the challenge is the many federal workers returning to the office five days a week. Recent U.S. mandates, for example, require all departments and agencies in the executive branch to return to in-person work. More people in government facilities means a larger demand on maintenance and janitorial staff. 

These statistics highlight the need for governments to implement new ways of delivering maintenance at their facilities that increase efficiency through better scheduling while also cutting costs. 

What are the challenges of scheduling with traditional maintenance strategies? 

Traditional on-demand maintenance creates several challenges that impact operational efficiency and level of service. A reactive approach to maintenance only addresses issues when they happen, leading to a range of problems, including: 

  • Unexpected downtime: On-demand maintenance is reactive, meaning it only addresses issues when they arise. This can lead to sudden and unpredictable equipment failures, causing unexpected downtime. 
  • Increased operational costs: Reactive maintenance often requires emergency repairs, which can be more expensive than planned maintenance. Emergency parts and labor can be costly, and the urgency of the situation may lead to higher prices. 
  • Reduced equipment lifespan: Running equipment until it fails can lead to more severe damage, reducing the overall lifespan of the machinery.  
  • Resource allocation: On-demand maintenance can strain maintenance resources, as teams must be available to address issues at any time. 

Preventive maintenance, while crucial for maintaining equipment reliability, can lead to over-maintenance. Maintenance departments base the schedules on manufacturer recommendations or historical data, which may not accurately reflect the current condition of the equipment, and this can result in unnecessary repairs and maintenance tasks, wasting valuable resources.  

What is predictive maintenance? 

Predictive maintenance empowers facility managers to leverage advanced data analytics and AI to forecast when maintenance is needed in government facilities. Unlike traditional reactive maintenance, which sees maintenance teams addressing issues after they arise, or preventive maintenance, which has teams follow a fixed schedule regardless of the equipment’s actual condition, predictive maintenance relies on real-time data to identify potential problems before they can lead to failures. A proactive approach ensures maintenance teams can intervene at the optimal time, reducing the chances of unexpected breakdowns and minimizing the need for emergency repairs. 

For facility and maintenance professionals at government facilities, predictive maintenance helps them significantly reduce downtime, which is crucial for maintaining the operational efficiency of government offices and services.  

How does AI make predictive maintenance possible? 

AI is to predictive maintenance what a weather forecast is to storm preparation. Just as a weather forecast uses data to predict and prepare for storms, AI uses real-time data to predict and prepare for equipment failures, ensuring that maintenance is timely and effective.  

Data capture 

Predictive maintenance in government facilities involves collecting data from various sources, including sensor readings, historical maintenance records, and environmental data. Sensors in HVAC systems monitor temperature, pressure, and energy consumption, while historical records detail past maintenance activities and failures. Environmental data, such as temperature and humidity, helps understand how external conditions affect equipment performance. Integrating these data sources provides a comprehensive view of the facility’s operational status. 

Data analysis 

AI algorithms analyze the collected data to identify patterns and predict potential failures. These algorithms detect subtle changes that indicate impending issues. For example, AI can correlate sensor data with environmental conditions to understand how external factors impact equipment. By identifying these patterns, AI predicts when equipment is likely to fail, allowing for proactive intervention. The process helps the maintenance team prevent unexpected breakdowns and optimizes schedules, leading to cost savings and improved efficiency. 

Real-time monitoring 

Tracking conditions in real time is essential for predictive maintenance, enabling quick detection and resolution of issues. AI systems continuously monitor sensor data and other real-time inputs, quickly identifying anomalies. For example, if an AI system detects an unusual increase in server room temperature, it alerts the maintenance team to investigate and take action. A proactive approach prevents equipment failure, reduces downtime, and ensures compliance with safety and regulatory standards, maintaining a safe and reliable work environment. 

How can AI-backed predictive maintenance improve scheduling for maintenance teams? 

By helping maintenance teams to move from a reactive to a proactive stance, predictive maintenance makes scheduling more strategic and less disruptive.  

Optimized maintenance schedules 

Predictive maintenance systems use data from sensors and historical performance to generate highly accurate and optimized maintenance schedules, so the team only performs tasks when they’re required, rather than on a fixed, often more arbitrary schedule. The process helps the team reduce the frequency of maintenance activities, allowing them to focus on other critical tasks.  

For example, instead of replacing a filter every three months, the system might indicate that it can last for four months based on real-time data, thus saving time and resources. 

Fewer emergency repairs 

Teams can leverage predictive maintenance to find potential issues before they lead to equipment failure. A proactive approach significantly reduces the need for emergency repairs, which are often more time-consuming and costly. By addressing issues early, maintenance teams can avoid the uncertainty and stress associated with unexpected breakdowns, leading to a more stable and predictable work environment. 

For example, at a government data center, the predictive maintenance system might detect a slight increase in temperature in a server rack. The team can replace the failing cooling fan before it causes a server outage, saving the facility from potential data loss and downtime. 

Improved resource allocation 

Maintenance teams have a clear, data-driven view of which equipment needs attention and when. With better allocation of personnel, tools, and materials, teams can ensure that the right resources are available at the right time, reducing the likelihood of delays and inefficiencies.  

For example, if the system predicts that a particular piece of equipment will need maintenance next week, the team can ensure that the necessary parts and tools are on hand and that the right personnel are scheduled to perform the task. Another example could be at a government hospital, where the predictive maintenance system forecasted that the MRI machine would require maintenance in the following week. The maintenance team can schedule the necessary repairs and ensure that all required parts are available, preventing any last-minute delays and ensuring quick return to service.  

Enhanced planning and coordination 

Armed with detailed reports and insights, maintenance teams can plan and coordinate their activities more effectively. They can schedule maintenance during off-peak hours or when the facility is less busy, minimizing disruption to daily operations. They’re also able to better coordinate with other departments, ensuring that maintenance activities don’t interfere with critical operations. For example, if a major event is scheduled at a government facility, the maintenance team can plan their tasks around the event to ensure everything runs smoothly. 

Another example might be at a government research laboratory, where the predictive maintenance system identifies that a critical piece of lab equipment requires maintenance in the next two weeks. The maintenance team schedules the repair during a period when the lab is running fewer experiments, ensuring minimal disruption to ongoing research projects. 

AI success starts with solid data 

Reliable data capture is the foundation of a successful AI system. Accurate and comprehensive data allows the AI to make precise predictions, optimize maintenance schedules, and identify potential issues before they become critical. Without solid data, AI insights and recommendations can become unreliable, leading to inefficiencies and potential equipment failures. By focusing on robust data capture from the outset, you set the stage for a successful and effective AI-driven facility management system. 

Learn more about Eptura at government facilities. 

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Gabi is a London-based writer with 8 years of experience in copywriting and strategy across various verticals. She has been immersed in tech for two years, previously writing for a fintech company. Her specialities lie in long- and short-form writing, SEO writing and strategy, and market research. In her current role at Eptura, she covers the latest worktech and workplace experience trends.