Queen's University Belfast and Combined Facilities Management Limited KTP 24_25 R4

A facilities management company is embedding artificial intelligence into its daily operations to predict equipment failures before they happen and automatically schedule repairs. This matters because most commercial buildings still rely on reactive maintenance—fixing things only after they break. That approach wastes energy, disrupts tenants, and costs more in emergency call-outs. The company wants to shift to predictive, data-driven management, but lacks the in-house expertise to build the necessary software tools. If successful, the project will create a system that continuously monitors heating, lighting, ventilation, and security systems across multiple client sites. The AI will spot patterns that human operators miss—a motor drawing slightly more current, a filter getting clogged—and flag them days or weeks before a breakdown occurs. For the company, this means lower operating costs, better compliance with regulations, and the ability to offer smarter services to clients. For tenants and building users, the result is fewer disruptions: no unexpected chiller failures on a hot day, no sudden lift outages. The project is applied commercial research with a direct, practical goal—embedding intelligence into the quiet infrastructure that keeps offices, labs, and warehouses running.