Today’s facility management industry remains content with the status quo of operating scheduled maintenance programs yet beginning to talk about the possibilities of preventative and proactive maintenance. A property company with a large portfolio of properties performs maintenance mostly by routine checks on buildings and equipment and reacting to complaints from tenants or even large incidents. Such a practice, widely used in today’s market, not only relies heavily on intensive human resource in order to maintain above-average standard of service, but continuously fails to identify faults or problems with individual equipment or a system until it is broken or even collapses. These faults or problems, until amended, continue wasting energy, damaging the equipment ecosystem and often cause discomfort for tenants without Facilities Management teams being aware for weeks or months. Most importantly, the repeated bad experience of complaints that remain unsolved for tenants will create obstacles for owners to increase rents or even retain tenants, not to mention capex and energy waste in an ever more expensive market.

The emerging technologies such as data analytics, machine learning, and artificial intelligence can now offer an unprecedented opportunity to fundamentally change the landscape of facility management – converting the passive method into a proactive one. Based on the conviction that data generated by a building management system provides meaningful insights of the operation, AI-driven software has recently been developed and offered in the market. One of the essential abilities of these software’s is automatic fault detection and diagnostics. It is able to identify faults or incorrect operation that impair equipment health and waste energy in an automatic way. This ability pinpoints problematic units out of hundreds of or even thousands of assets and gets the maintenance team to rectify them before the situation exacerbates or tenants complain. In such a proactive method, a tremendous amount of resource would be saved from significantly reduced manual work and optimized maintenance cycles. It also strengthens owners’ digitalization basis on which costs are reduced but service standard is enhanced in the long term.

The passive method in which today’s Facilities Management is mostly performed causes a negative impact on net cash flow from the property over time, due to increasing energy consumption, maintenance expense and difficulty to increase rents. Ai-based management software, particularly based on machine learning, will offer a new, proactive method in which operations and maintenance are performed in a predictive way to control cost across the operation and enhance service quality in the long term.