Around this time last year, my colleague Geert van Offeren wrote about the importance of reviewing use cases and the expected business value before jumping into IoT projects.
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As artificial intelligence (AI) is another frequent buzzword in the facility management industry, I want to re-enforce his advice relating to this technology:
For organisations and facility management service providers, it is important and crucial to research what use cases are relevant and most valuable to their situation. What specific AI solutions is the organisation looking for? In addition, companies have to determine the specific business case from both a quantitative (costs) and/or qualitative (user experience, building performance) perspective for each relevant use. What is the real value of this and the related artificial intelligence? Establishing your use case is where the journey starts to provide real business value.
For example, a compelling AI use-case for a facility management service provider would be one that supports process efficiency in order to increase profitability. As our research has shown, the average profit margin from providing hard services is between 5% and 6%, while the margin on soft services is even lower at just 2% to 3%. Almost any technology investment needs to show an improvement in this key area of business success.
Service Level Agreements (SLAs) can vary per customer, bonus/malus rates can vary, and the services offered can differ. This creates the need to evaluate each incoming request for work against these contractual parameters.
The process of aligning services delivered within individual client contracts is one of the primary challenges service providers face in creating efficient business processes. When this process is automated, you can see a direct impact on profitability. For example, one way to do this is with intelligent capacity planning and smart dispatching that considers both your organisational resources and your clients’ SLAs together. Artificial intelligence can support several steps in this process.
Smart process mining
A process can be seen as a set of activities that are executed in a predefined sequence with predefined options. Processes are conducted in a repetitive fashion. Any changes made to the standard process are usually made by people. In some cases, the outcome of activities themselves are based on decisions made by people too.
In well-defined processes, people make decisions based on data that is available in the context of the process. When the right data is available, and an artificial intelligence can understand the context, systems can start to make some decisions that people already made, thus creating the potential for high-speed operations.
Predictive and prescriptive processes
When machine learning is applied to data analysis, it produces capabilities in these systems to identify correlations not previously identified by people. This is where the true business value comes in.
This type of AI can potentially solve problems or hiccups in processes we were not aware existed. Applying this type of learning capability is commonly referred to as predictive analytics, which envisage developments as they evolve, and prescriptive analytics, which describe the appropriate responses to developments.
This allows a service provider to receive alerts and notifications when the system predicts that an individual contract could be more profitable if specific actions were taken. If replacing this asset now would save money later. Or if staff could be better optimised. Or if there are actions that would reduce throughput time and make processes more efficient.
These are the use-cases that will see artificial intelligence adopted across the world of facility management. Which use-cases are you working on? Do you see the benefits of AI? Let us know in our survey.