Like many industries, facilities management has traditionally relied on labor-intensive tasks, where reactive responses to issues make up the bulk of the work. When it comes to managing maintenance schedules or tracking equipment performance, various inefficiencies and downtime can occur, which is where artificial intelligence (AI) agents can be impactful.
Recently, a survey from Forbes highlighted that 65% of skilled trade workers are confident that their jobs are safe from the threat of AI, with just 11% being concerned about being replaced by AI. This self-conviction makes complete sense—maintenance and repair require a skillset where only humans can intervene successfully. For example, with an old system where integrating AI just isn’t possible or where physical labor is the only solution.
Having said that, there’s no doubt that the continued progression of AI agents is having a transformative effect on the industry, where the ability to help with both reactive and preventive tasks backed up by data-driven insights is crucial in the new age of facilities management. Let’s take a closer look.
Proactive Maintenance Through Analytics
One of the most important applications of agentic AI in facilities management comes from preventive maintenance. This is because it operates on a fixed schedule most of the time, and predictive models analyze historical and real-time data to anticipate equipment failures, enabling proactive interventions. AI agents achieve this by using techniques such as regression analysis and time-series forecasting, which help to identify patterns and trends.
For instance, let’s take a large warehouse equipped with lots of conveyor belts. AI agents integrated with real-time monitoring systems can analyze data from these belts, which are vulnerable to wear and tear due to constant use. They provide insights into vibration patterns, motor torque, belt tension, and other performance metrics. An AI agent can detect any deviations from a good performance, whereby the motor might be particularly strained from increased activity, and predict when the belt is going to tear. Rather than wait for the inevitable breakdown, the maintenance team can be made aware of the need for an intervention, which means there isn’t a big breakdown in operations.
Data Integration and Contextual Insights
In order to conduct all this maintenance, facilities management usually revolves around an array of different systems, from computerized maintenance management systems (CMMS) to building management systems (BMS). It’s not always easy to coordinate maintenance, but AI agents can bridge that gap by integrating data into one unified system. This also gives facilities managers a better overview. For example, if a building’s elevators experience increased downtime, it might have an effect on traffic flow within the facility.
What’s more, integrating AI with programmable logic controllers (PLCs) can provide better monitoring of equipment behavior, while real-time visual feeds can identify physical anomalies like leaks or wear. Centralized data presented in an actionable format helps facilities teams prioritize high-impact issues efficiently.
Training and Decision-Making Improvements
Aside from the clear improvements that AI agents can make through data integration, there is also plenty of scope to improve training programs for maintenance technicians. By collecting and analyzing operational data, training programs can be tailored to address critical areas effectively. This means that technicians are better prepared when it comes to preparing problems they might encounter.
One of the last areas to touch on, but something that is really empowering, is how AI agents can give facilities managers more space to take a more strategic approach to their work. With routine tasks, such as maintenance scheduling, automated in the background, it means that there is more time to stay on top of longer-term issues, such as sustainability initiatives or better infrastructure planning for facility upgrades.
Challenges Moving Forward
We have outlined how AI agents can aid facilities managers, but there are also plenty of challenges. Data quality and integration are not always straightforward, with many systems operating on older platforms with limited interoperability. Despite the evidence that these AI agents are not capable of replacing facility positions, some executives still resist in this area.
Overall, facilities still need to operate within a phased implementation strategy, where AI is implemented to aid rather than replace workers on maintenance tasks. These agents can be part of a positive paradigm shift where reactive problem-solving becomes a thing of the past and data-driven decision-making across areas of predictive maintenance, automated scheduling, and strategic planning becomes commonplace.
Ruban Phukan is co-founder and CEO of GoodGist Inc., a Generative AI Solution Platform.