5 real-life Business Process Automation examples for Facilities

5 real-life Business Process Automation examples for Facilities

Automation in facilities connects physical spaces, assets, vendors, occupants, and maintenance schedules rather than purely digital workflows.

The biggest gains come from automating work orders, inspections, preventive maintenance, contractor coordination, and occupancy communications so teams can run buildings more reliably and cost-effectively without losing visibility into on-the-ground conditions.

Industry

Sector

Siemens X AI-Powered Building Management Platform

Siemens deploys Building X, its AI-enabled digital building platform, to help facility managers make data-driven decisions that improve sustainability, energy efficiency, and building performance, integrating HVAC, lighting, security, and occupancy systems into a unified intelligent management layer.

Use Case
AI-Driven Smart Building Energy and Operations Management
Tools
Siemens Building X
Input
Real-time data from IoT sensors, HVAC systems, lighting controls, security systems and occupancy sensors
Process
AI continuously analyses building system data to predict energy loads, adjust environmental controls based on occupancy patterns and trigger automated maintenance alerts.
Output
Automated HVAC and lighting adjustments, predictive maintenance alerts, energy consumption reports, sustainability dashboards and operational insights for facility managers
Outcome
Commercial buildings using AI-based HVAC systems report energy savings of up to 37%
Facilities Management and Maintenance
Facilities

CBRE's SmartFM Nexus AI Building Operations Platform

CBRE deploys its AI-based Nexus platform across more than 20,000 client sites totalling one billion square feet, centralising building operations and utilisation data to automate facilities management workflows, predictive maintenance, and energy optimisation at global scale.

Use Case
AI-Powered Predictive Facilities Management
Tools
CBRE Nexus
Input
Real-time sensor data, building management system outputs, occupancy data, maintenance records, and energy consumption data from across 20,000+ client sites worldwide
Process
CBRE Nexus aggregates data from 39 billion data points across more than 300 sources into a centralised AI analytics platform.
Output
Automated predictive maintenance work orders, energy optimisation recommendations, occupancy-based space utilisation insights
Outcome
Lower energy costs and streamlined operations through automated workflows replacing manual spreadsheet-based processes
Commercial Real Estate and Property Management
Facilities

JLL's Serve AI-Powered Facilities Management Application

JLL Serve, an AI-powered facilities management application, unifies data from connected and non-connected building assets into a single mobile and web platform, enabling technicians and facility managers to automate workflows and make proactive maintenance decisions.

Use Case
Integrated AI-Driven Facilities Workflow Automation
Tools
JLL Serve
Input
Asset status data from connected IoT sensors and non-connected equipment, work order histories, maintenance records, occupancy data, and service request logs across managed properties
Process
Ingests data from disparate building systems and normalises it into a unified platform.
Output
Automated, prioritised work orders delivered to technicians via mobile, real-time asset health dashboards for facility managers, and proactive maintenance recommendations
Outcome
Reduces administrative coordination time, automates repetitive FM tasks including work order dispatch and invoice approvals, and improves asset uptime and technician productivity.
Facilities Management and Maintenance
Facilities

Cargill's Automated Equipment Failure System

This automation enables Cargill to transition from reactive repairs to proactive asset management by utilising Azima DLI software to monitor 15,000 industrial assets. The system leverages continuous vibration and temperature analysis to identify early signs of mechanical failure, allowing operations teams to schedule interventions before breakdowns occur.

Use Case
Predicting equipment failure
Tools
Azima DLI
Input
Continuous vibration and temperature sensor data from 15,000 industrial assets
Process
Azima DLI’s AI analyses vibration and temperature patterns using machine learning to identify early indicators of mechanical failure. The system generates automated alerts before a failure occurs, enabling scheduled maintenance.
Output
Proactive maintenance replaces reactive repair; reduced unplanned downtime across facilities
Outcome
10% reduction in maintenance needs; decreased downtime and extended machine lifespan
Food Processing and Packing
Facilities

Guy's & St Thomas' NHS Foundation Trust CAFM Automation with MRI Software

Guy's & St Thomas' NHS Foundation Trust deploys MRI Software's AI-powered Computer-Aided Facilities Management (CAFM) system to forecast equipment failures, prioritise maintenance across the Trust's estate, and extract operational insights from large volumes of asset data improving efficiency and decision-making in a critical public sector healthcare environment.

Use Case
Predictive Estates Maintenance
Tools
MRI Software CAFM
Input
Asset condition data, maintenance histories, work orders, and operational estate data
Process
MRI Software's AI analyses asset performance data to forecast equipment failures, auto-prioritise work orders by criticality and automate routine scheduling and reporting.
Output
Predictive maintenance alerts, prioritised work order queues, automated scheduling recommendations, and operational efficiency reports for NHS estates managers
Outcome
Reduced unplanned downtime, improved equipment reliability, and facilities staff freed from manual processing to focus on patient care environments.
Hospitals and Clinics
Facilities
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