The adoption of artificial intelligence (AI) in FM has been slow but it can play a significant role in improving the industry, says Muhammad Asif Khan.
AI – Why it’s lagging in FM
Despite its growing use in other areas, AI is not enjoying the same level of popularity in facilities management. The reasons include:
- Lack of awareness and FM professionals considering AI as a highly complex technology only used to solve scientific and research problems; and
- FM organisations are not ready for AI adoption; AI techniques are mainly data-driven and require accurate historical data, which most FM organisations typically lack.
AI’s role in FM
AI can be applied to a range of problems. I have limited the list to those where AI techniques can be applied to gain operational improvements, enhanced customer experience and decision-making.
- AI-enabled products: AI-enabled equipment such as intelligent temperature sensors can learn the temperature of your comfort and build an automatic schedule accordingly.
- Smart buildings: AI can be integrated into the building management and control systems to automatically adjust system settings and create equipment schedules to improve the building performance index.
- Predictive maintenance: AI algorithms can be applied to learn from historical equipment data to perform real-time prediction about possible equipment failure or need for urgent maintenance.
- Fault identification: AI can be efficiently used to identify faults using system generated data such as alarms and logs. Examples of such identification are power trips, leakages in pipes and ducts.
- Work scheduling: AI can help to forecast future workload in terms of breakdown, service requests given the various parameters such as operational load, weather information, ongoing maintenance activities and building occupancies. Such kind of demand forecasting can be helpful to plan resources and work scheduling.
- People’s safety: AI-based video detection systems can now be deployed to monitor work sites to ensure that people are complying with safety procedures, for example, by detecting people not wearing personal protective equipment. AI can be used to activate an event-specific building evacuation plan that directs people to evacuate the building via a safe route based on the incident location.
- Customer experience: Frequent customer feedback can help to understand customer requirements and expectations. AI can help to analyse automatically this feedback to build automatic recommendation systems to suggest best actions.
- Smart analytics: AI enables FM professionals to gather useful insights from data beyond ‘gut feeling’. Sophisticated AI techniques can analyse data efficiently and build insights that can help to quickly perform accurate decision-making.
Challenges of AI in FM
Several challenges are associated with adopting AI:
- FM professionals need to upskill to understand how AI can transform their functions;
- Organisations need to invest in gathering data with highest possible accuracy. The curse of inaccurate data can produce miserable results;
- In outsourced FM models, equal contributions from client and service provider are required. Client organisations must encourage and support the AI adoption, while service providers need to show full commitment; and
- Collaboration among different organisations to share data can boost the success of AI in FM. However, challenges such as data privacy, confidentiality, trust and sharing of benefits are serious hazards for such collaborations.
Muhammad Asif Khan, Ph.D., MIEEE and senior FM professional