A leading UAE property management operator oversees 4.8 million sq ft across residential towers, office buildings, community malls, and mixed-use compounds. Their mission is to deliver premium living and working spaces while keeping operations lean and sustainable. But behind the scenes, the Facilities Management (FM) workflows were becoming increasingly complex—and costly.
With 24 assets spread across the UAE, the FM team faced mounting challenges: Rising energy costs (+9% YoY) and growing pressure to meet sustainability goals Frequent SLA breaches on tenant complaints, leading to penalties and dissatisfied residents Long work order turnaround times (TAT) averaging 2.9 days, with only a 62% first-time fix rate Unplanned equipment downtime across chillers, pumps, and elevators Vendor invoice cycles averaging 31 days, delaying payments and creating cash flow pressure While the operator had a legacy CMMS, partial Building Management Systems (BMS), and various vendor portals, data was siloed, processes were manual, and opportunities for cost-saving automation were being missed. The leadership’s target was clear: reduce operating expenses by at least 10–15% in the first year without compromising service quality.
Within 6 weeks, Advisoryn launched a pilot across six mixed assets (residential, office, mall, mixed-use) and rolled out: Smart Work-Order Triage & Dispatch Auto-classifies and prioritizes tickets, routes to the right technician, and reserves spare parts—cutting delays. Predictive Maintenance for Critical Assets AI models monitor chiller, AHU, pump, and elevator sensor data to predict failures before they occur, preventing costly breakdowns. Energy Optimization Loop Day-ahead load forecasting and chiller setpoint optimization to reduce energy consumption without impacting comfort. SLA Compliance & Penalty Avoidance Real-time SLA tracking, breach prediction alerts, and root-cause analysis to keep service levels high. Vendor Invoicing Automation Auto-generates and validates service sheets, integrates with finance, and reduces manual invoice processing time by more than half.
AI systems require regular updates to improve their performance as new data becomes available.
33% reduction in TAT (2.7 → 1.8 days) 15-point increase in first-time fix rate (61% → 76%) 58% fewer SLA breaches 41% less unplanned downtime 6.9% reduction in monitored chiller energy use Invoice cycle reduced from 31 to 16 days Annualized savings (pilot assets): AED 4.1M The success of the pilot led to a portfolio-wide rollout in just 3 months.