With the advent of high-performance buildings, passive design strategies, and new technologies, facilities teams are being confronted with ever-increasing complexity and decreased system robustness. Facilities teams’ knowledge must be simultaneously deeper, to understand complex system interactions, and wider, encompassing programming, complex controls strategies, and new technology.

Add to this the combination of technology moving at a pace faster than a workforce with the average age of 50.9 years can reasonably be trained, plus the next generation of talent with the required hi-tech skills is choosing to pursue other careers, and we find our industry in a talent crisis.

The IFMA recently reported what we all have seen happening for some time – facility management is facing a critical shortage of professionals and urgently needs to attract new talent. In fact, management identified maintaining staff levels is the most difficult to manage compared to other responsibilities in the Facility Executive report Facilities Management Challenges and Opportunities in 2018.

In one approach to help address the talent crisis, industry is working with technical schools to create programs that cover everything from computer programming to traditional mechanical systems. These programs are being paired with apprenticeships at partner organizations to not only learn the significant amount of hands-on knowledge required, but also ensure a continuous feed into partner recruiting efforts.

Our view on this crisis is while technology complexity is contributing to this talent turmoil, it can also be part of the solution. One methodology that addresses building complexity is to combine professional expertise and advanced machine learning tools in such a way as to capture and document this complexity.

Instead of being constrained by the static, out-of-date sequence of operations (which may have been wrong from the beginning), solutions based on a human/cloud methodology could learn the actual interactions through observations, training (by humans) and modeling a system’s response to all other variables in the system. This provides a critical tool for facilities teams to understand the complex interdependencies in their buildings and respond quickly and efficiently.

For example, data tools we are implementing can see which points are driving the total building energy consumption and find unusually high contributions by equipment. We are also training our AI tools to know what is normal and what is not. Until now, this has been the realm of the very experienced facility professionals with intimate knowledge about their buildings. Now it is stored in the cloud.

As good as that sounds, the onsite team is still the final arbiter of deep, unique-to-the-building diagnosis in many cases. Over time, and with continued machine learning, these tools will improve to free the facilities management staff of the tedium of sifting through data to get to a diagnosis and action. These tools can also fill the gap in knowledge for less experienced staff, helping to address the current talent crisis head on.

Chris McClurg is Senior Product Manager of Services at BuildingIQ, and a mechanical engineer focused on energy efficiency in large portfolios and net zero developments. Chris has worked on deep retrofits, integrated design, integrated project delivery, and buildings as a grid asset. She is a PE, CEM and LEED AP certified.