Key takeaways
- Artificial intelligence (AI) is making a transformative impact on facilities management, with a particular focus on healthcare facilities
- Facilities management teams using AI are getting never-before-seen insights into their assets for enhanced preventative maintenance and building management
- With mounting costs and complexity, AI is fast becoming not just a helpful tool, but rather a necessity for healthcare facilities to remain operational
Artificial Intelligence (AI) refers to simulation of human intelligence in machines (such as recognizing patterns, understanding language, and even making decisions). Generative AI, or GenAI, is the latest iteration of this category. You’ve likely heard a lot of hype around GenAI post the launch of ChatGPT in 2022. While this launch was a watershed event, there are many other kinds of AI that are already in place such as machine learning (ML), natural language processing (NLP) and computer vision, among many more.
Software automation via AI will be key for relieving facilities management teams of repetitive, labor-intensive tasks, giving them more time for higher priority work and increased volume of work. Innovative technologies that give visibility into facilities management operations will drive insights for greater efficiency.
Intuitive prompting and dialogue with generative AI chatbot or assistant as well as embedded AI in the software can deliver actionable facilities management insights and faster, more-informed decision-making. Combined with automation, AI accelerates facilities management workflows that were previously challenging, like resource allocation or incident reports for critical environments.
AI in Preventative Maintenance Software
We are implementing several initiatives today. The first one is focused on extracting preventative maintenance schedules and granular task checklists from manufacturer manuals for specific models of diagnostic equipment.
Biomed assets (CT scans, MRI, defibrillators, surgical robots, etc.) are critical and require very specific maintenance based on the Original Equipment Manufacturer (OEM) instructions. By applying Optical Character Recognition (OCR) and combining several open-source AI models, we have managed to extract this information from PDF manuals and parse it in a systematized manner into our software database.
The estimated savings from an initial implementation for one hospital chain with 10 facilities is up to 200 labor hours. Ongoing savings will be realized as new diagnostic assets are introduced or as manuals are revised for existing equipment. This initiative paired with a criticality model to categorize assets into high vs non-high-risk bands enables hospitals to allocate resources and focus preventative maintenance programs on high-risk assets, typically only ~15% of an average portfolio. This scoring framework helps reduce the likelihood of critical failures, risk to occupants and service disruptions.
The second initiative relates to regulatory compliance. Our software helps surveyors prepare mock audits to ensure hospitals are compliant with either DNV or JTC’s standards. We applied GenAI models to auto-classify detected violations, whether those relate to the facilities themselves or documented policies. We have expert surveyors to help our clients, and they have been at the job for several decades and know hundreds of standards by heart. However, the software helps automate the process, resulting in time savings and increased accuracy, but also ensures that new entrants to the field do not have to memorize the standards.
Additionally, AI is there to streamline the client onboarding process by auto-classifying assets and their attributes. This not only allows for tremendous reductions in implementation timelines from months down to days, if not minutes, but also helps avoid human error from manipulating spreadsheets manually.
Most AI will be naturally invisible as it works behind the scenes. It will be embedded in the form of algorithms, agents and workflows. There will be some that are more obvious such as chatbots, assistants, desktop ‘co-pilots.’
The Impact of AI on Building Management Systems (BMS)
AI, including GenAI, is increasingly being integrated into building management systems (BMS) to improve efficiency, enhance automation, and optimize various operations. Here are some examples of how GenAI is applied in this field:
Predictive Maintenance
Analyze data from building systems, such as HVAC units, elevators, and lighting, to predict when maintenance is needed. By simulating different failure scenarios, it can recommend proactive actions before equipment breaks down.
Smart Energy Management
Generate energy consumption models based on real-time and historical usage data, optimizing energy use by adjusting HVAC, lighting, and other systems based on occupancy and weather patterns.
Space Utilization and Design
Produce models that predict how different spaces in a building are used. It helps optimize space allocation and management, suggesting ways to adjust layouts for better efficiency or improved traffic flow.
Sustainability Reporting and Compliance
Automatically create detailed reports on a building’s energy efficiency, suggesting improvements that align with sustainability goals or regulatory requirements. GenAI can also simulate different building management scenarios to assess and reduce a building’s carbon footprint, aiding compliance with environmental standards
Where Are We on the AI Journey
Source: JLLT Report “The State of Facilities Management Technology 2024”
AI has been around since the 1950s, existing in various forms. In recent years, however, advances in computing power, types of processors, the invention of large language models (LLMs), and access to vast volumes of training data have triggered new waves of innovation.
We’re in the early days of very sweeping changes that AI will introduce, and can expect multiple iterations of innovation in the coming years. Specifically, we’re already seeing AI applied to solve problems in existing workflows that were designed for humans. We will soon see examples of AI completely changing those workflows to innovate in ways that humans cannot, freeing up facilities management professionals to do higher value work.
A global survey by JLL Technologies in 2024 confirms very strong interest in AI but lack of adoption. Nearly 60% of facility managers have indicated a desire to use AI but have no strategy. Only 10% reported using AI on a regular basis.
Future Opportunities
The global investments and resources dedicated to AI innovation today are unprecedented in the history of technology, which promises ongoing innovation that will benefit facilities management. Many of the technical limitations facing AI today will be resolved over time. Data availability and data quality as well as proper data layer infrastructure remain on top of the list.
The real limitations to AI in facilities management today are due to a lack of AI knowledge among subject matter experts, a lack of technical knowledge to facilitate implementation, and the general fear and uncertainty that comes with entering new territory. Also, and rather ironically, a growing list of day-to-day responsibilities is taking time away from decisionmakers’ ability to learn and experiment.
At the same time, the demographic deficit already affecting facilities management is only going to get worse. Similarly, budgets are decreasing, and hospitals are finding themselves needing to do a lot more with less. AI is arriving at the scene to rescue facilities management teams from this perfect storm, and those embracing this technology are putting themselves in a strong position for unprecedented financial and operational sustainability.
Interested in being among those leading the pack in the AI movement? Learn more about Vytal Assets
