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AI is the key to greater efficiency
Clients expect transparent, flexible, and ESG-compliant services, while at the same time ever-increasing amounts of data from building technology systems, sensors, and ERP solutions must be processed. Added to this is intense competitive and consolidation pressure, which is forcing companies to make their processes more efficient and scalable.
Artificial intelligence (AI) offers the opportunity not only to overcome these challenges, but also to turn them into tangible competitive advantages.
AI agenda – strategic anchoring of artificial intelligence
In order to identify and develop value-adding AI use cases, the technology must be firmly integrated into the strategic planning process. An AI agenda is essential for this. The following elements are part of such an agenda:
- Holistic approach: AI is not understood as an isolated solution, but as an integral part of the business model. Continuous optimization in all areas, from back office to operational service delivery, must be ensured.
- Governance: Specialist departments are responsible for identifying requirements for AI use cases, while central AI teams ensure methodology, standards, data governance, and rapid testing of new solutions.
- Reusable building blocks: Standardized modules and solutions enable rapid implementation and create synergies across business units. For example, AI-supported utilization control for technicians must also be usable for cleaning staff in its basic features.
- Uniform database: Clear guidelines define which key figures are recorded in which format – from the start-up process to the ongoing supplementation of maintenance logs. Data stewards continuously monitor compliance.
- Data protection and compliance: Customers entrust FM service providers with critical data, such as usage, consumption, and asset data, which is necessary for the use of AI. Transparency in data processing and decision-making by AI algorithms, for example, intervention in the heating curve, are the basis for building customer trust.
Use cases in practice
Apleona has internalized these principles and is already using AI profitably in service delivery and internal process optimization. Here are a few examples that are already in use or currently in development:
Technical plant monitoring
By monitoring plants via sensors or the GLT interface, vibrations in HVAC components such as pumps or motors can be detected at an early stage, indicating bearing damage. Unusually high energy consumption can also indicate friction losses. AI can analyze the measurement curve and provide timely information on when a plant needs to be serviced. This improves service quality, extends the service life of the system, and reduces the probability of failure.
This technology is particularly widely used in production-critical systems (e.g., air extraction systems in clean room production), as the savings from avoided downtime quickly exceed the investment costs. Declining hardware costs make it possible to gradually expand the approach to other systems.
Automated HVAC optimization
AI controls the entire HVAC system—from the room level to the central plant technology—including heating and cooling units, ventilation systems, pumps, boilers, and chillers. This is based on a model created from existing building data, location data, and real-time weather data. Usage patterns are also continuously incorporated. Such a system not only reduces time-consuming manual interventions, but also improves energy efficiency by intelligently adjusting setpoints.
Intelligent contract analysis
In contract management, AI-supported document intelligence can be used to automatically analyze and structure the content of complex facility service framework agreements – such as terms, renewal options, notice periods, or SLA clauses. Contract details are available at any time, risks due to overlooked deadlines are reduced, and service levels can be better tracked. This makes internal processing more efficient and improves the quality of customer service.
AI-supported, automatic asset recording
Automatic asset recognition using imaging, for example with LiDAR 3D scanners and AI functions, makes it possible to capture data quickly and in a standardized manner with reduced effort in the start-up process. This improves efficiency during asset recording and simultaneously increases data quality.
Automated scheduling
AI-supported resource planning ensures that technicians with the right qualifications are available on site more quickly. Customers benefit from more reliable service delivery, shorter waiting times, and a higher first-time resolution rate. This increases utilization and thus also the contribution margin of employees.
A contractual basis is required to divide the resulting efficiency gains between the customer and the FM service provider, thereby creating incentives for both sides to use the technology. Results-oriented contracts offer such a basis. With service levels remaining the same, the FM service provider is incentivized to optimize processes, including through AI, and thus save costs.
Conclusion
Artificial intelligence can have a positive impact on the industry-wide transformation process. The resulting efficiencies not only generate operational advantages, but also a decisive competitive advantage. The path to this goal is clear: away from isolated flagship projects and toward a holistic AI agenda underpinned by an AI-enabled organization.