Artificial intelligence: a new technology in TechFM

Overall, facilities management is a conservative market and not always the first to adopt new technologies. Nevertheless, there has been a lot of progress over the last 10 years. Previously, a screwdriver was the most important tool for technicians in the sector; now, it’s their phones!  

To zoom in on what this means in everyday terms at SPIE: not long ago, if a client had a site issue and called our emergency out-of-hours number, our duty engineer would get the alarm and rush to the site. Once there, he or she might discover that another specialist was needed to deal with the problem. 

Today, error notifications are sent automatically to our control centres where they are checked, prioritised, and then referred to the right engineer – who, frequently, can carry out a temporary fix remotely. This is already a completely different way of working. And now, AI is offering further enhancements. 

By and large, technical facilities management is about minor, everyday repairs on small items, but our clients have sizeable portfolios with large numbers of assets generating enormous amounts of data – everything from energy consumption to error reports. AI is already helping us to better manage and harness this data. 

To give you an example: we produce regular maintenance reports for customers, including recommendations for replacements and repairs. Processing them into orders, though, used to take dozens of employees several hours. Now, with our new AI solution, they simply file the reports to an e-mail address and, moments later, receive client-ready proposals for new jobs – with notifications sent straight to our engineers’ phones. And our employees can focus on higher value-added activities. 

This is just one of a dozen AI pilot projects currently ongoing at SPIE. We keep them close to real business cases because, in our experience, the devil is in the detail. Just building and perfecting our maintenance report solution was a complex task. The result, though, more than justifies that investment, as it enables our engineers to concentrate on their core expertise: delivering solutions.

Yes! We talk about ‘hands-on-tool time’. Our employees are now doing less administration and more work on client infrastructure. What is more, AI is already in our systems examining site data for potential component failure, too, which allows us to move further towards predictive maintenance. We can also analyse energy consumption better, using AI to identify potential savings.

Another benefit customers will see in the near future: real-time data from their assets. Currently, clients often need to call us or navigate multiple systems: AI will allow us to combine everything into one place, offering live building dashboards. And, of course, there’s a financial benefit, too: by applying AI, we will be able to work with less time outlay and so in a more cost-effective manner. That means our clients will get more without paying more. 

Of course, processing site data in this way is never without risks, primarily in terms of cybersecurity. It is incumbent on us as providers to be honest and open about dangers – and do our utmost to minimise them. At SPIE, our response has been to introduce robust systems and to focus on training. In systems terms, this means that we now use our own infrastructure to capture and process data, keeping it safe from leakage and pollution. In training, we educate our employees about how to handle and store critical data, enforcing our policies internally with dedicated officer teams. Applying this approach, we have obtained an ISO 27001 certification. 

Beyond cybersecurity concerns, AI-driven predictive maintenance also presents a pricing dilemma. When TechFM providers prevent an issue before the client notices, how should it be billed? So, we expect to see a shift from traditional hourly rates towards performance-based contracts, for example guaranteeing maximum downtime thresholds. That’s not a ‘risk’ in the classic sense, of course – and certainly not bad news for clients in the sector! 

Last, but by no means least: AI also brings organisational challenges. Already, digitisation means that technicians start their working day by checking their phones and heading out to client sites instead of coming into the office first, which can reduce team cohesion and knowledge-sharing. With AI interfaces, there is a risk that the human element will be removed from other interactions, too. So, both within our teams and in contact with our customers, we’re actively developing ways to maintain the personal connection that is so important to us at SPIE. We maintain personal connections by creating more frequent touchpoints—such as monthly operational meetings—and by hosting regional community moments. We also sponsor team‑building initiatives and promote group‑based training sessions to enhance collaboration across our teams.