5 Practical AI Applications in Commercial Real Estate with Real Business Value
No longer a futuristic notion – AI can be used right here and now to maximize profits
The potential benefits of implementing Artificial Intelligence (AI) in almost any industry are well talked about. The idea of having machines to process information like humans do and produce much better outcomes has been considered a futuristic, though tangible prospect up until not so long ago. However, today that notion is closer to reality than ever before, and in some industries, AI is already presenting practical capabilities that deliver real business value – here and now.
One of these industries is Commercial Real Estate, where AI applications are already saving costs, driving better decision-making, boosting asset longevity and improving operational excellence. All of these measurable business benefits can be achieved by applying AI along the commercial real estate ecosystem, in 5 key areas:
#1 Tenant Satisfaction – Boosting Retention, Avoiding Turnover
As in every other sector, here too it is far easier and less costly to keep an existing customer than attract a new one. That’s why tenant satisfaction is so paramount to a property’s prosperity, and it’s also why so many resources and teams are operating to provide the tenants with timely quality service.
However, this becomes an increasingly challenging task when managing multi-campus enterprises with hundreds and sometimes thousands of tenants. This is exactly where AI comes in: by monitoring aggregated data from the building’s existing performance system controls, AI can identify recurring errors, anomalies in response times, or trends in work orders to allow property managers to fix problems faster and gain better insight on the root causes (poor maintenance practices, equipment wear-down, et cetera).
And as opposed to common perception, AI doesn’t only work on smart buildings with sensor-based automated systems, but rather integrates with existing legacy systems, making these capabilities available to practically any property.
#2 Lower Maintenance Costs and Improved Equipment Longevity
Maintenance costs in commercial real estate can skyrocket; from staff to equipment, expenses are high. But the real problem in building maintenance goes deeper than that – the whole value chain is broken: while one department is responsible for preventive upkeep of the equipment, another is responsible for actual repairs in case of equipment break-down, and a different one is responsible for dealing with ongoing fixes – with no one management workflow to link all these together.
This disruptive linkage between the various stages of maintenance is bad for business for the simple reason that the quality of the equipment undeniably affects preventive measures, which in turn have a substantial effect on ongoing troubleshooting. AI aligns all of these into one linked value-chain of predictive analytics: an ongoing equipment monitoring that solicits proactive maintenance activities before a breakdown even occurs, thus substantially reducing costs.
#3 Better Resource Management and Allocation
Traditionally, resource allocation was done based on experience; from the number of electricians needed for one building’s upkeep to how many operation managers are needed to monitor the tenants’ work orders – without analyzing whether it’s too little or too much. Using AI in resource management to analyze the relationship between the workload and the team capacity, provides accurate conclusions on how much staff and resources are needed to handle each project.
#4 Closing the Gap between Service Procurement and Tenant Retention
Providing superior vendor service for the property’s tenants is a top priority, as already established above. However, more often than not, service levels are dropping without proper reporting, since the data on slow response times and pending work orders is lost in the overwhelming data clutter of multiple suppliers across multiple properties. Leveraging AI in these processes enables vendor-specific segmentation in order to identify trends in service levels across different vendors and alert on any anomalies in service.
#5 Predict Cash Flow Deficiencies based on Suspicious Trends in Account Payables
Finding cash flow deficiencies in real-time is already too late. The careful tracking of the company’s cash flow is especially essential in commercial real estate, since any deficiencies can dramatically affect the next acquisition. And while dubbing managers can monitor payment on their ERP systems, they cannot be expected to predict changes in customers’ payment behaviors, track recurring payment delays, or constantly worry about the implications on the company’s cash flow. However, AI algorithms can be used here to identify trends and anomalies in payment patterns, and alert in advance on any behaviors that might affect the company’s cash flow.
Maximizing Property Profits Starts at Leveraging AI Across the Asset’s Ecosystem
Overall, the quality and success of managing the operational and financial aspects of commercial real estate depend on the articulate juggling between tenant satisfaction and retention, timely maintenance, cost control, and quality of service providers. Taking these aspects to the next level requires optimizing the relationship between these links in the value chain, and AI is doing precisely that, thus enabling executives to make better decisions and balance between maximizing profits and keeping maintenance rates low.