AI and Smart Building Technology

Facilities managers must embrace building modernization to overcome aging infrastructure challenges and growing talent shortages, according to a report by Trane. 

The report identifies eight business drivers impacting building technology decisions, including sustainability, operating costs, indoor environment, system reliability and uptime, energy efficiency, system interoperability and cybersecurity. 

Three-quarters of commercial buildings are 25 years old, according to a 2018 U.S. Energy Information Administration survey, with most buildings using outdated controls technology, Trane says. At the time of that survey, 49% of all buildings were unconnected, older buildings, with 40% showing “some element of smartness” or smart technologies. Only 11% of buildings had integrated smart technologies or were reported as “fully integrated,” according to the EIA. 

“The majority of buildings on the planet are not very smart. [They] are not connected or giving you that data you need to be able to make near real-time or real-time decisions,” Brian Haines, chief strategy officer at FM:Systems, said on an IFMA webinar on May 14 for World FM Day. “The reason that a lot of these buildings are lit up at nighttime is because cleaning crews have to go through and flip the lights onto the floor. It’s a manual process of going to turn everything off that just doesn’t get done,” Haines said. 

At the same time, 27% of current workers are within 10 years of retirement, Trane says in the report, citing data from JLL. HVAC technician demand is also growing at 5% annually, creating 40,000 openings yearly through 2031, according to data from the U.S. Bureau of Labor Statistics. 

To address these concerns, operators are working to implement building modernization and smart technology infrastructure to improve their facilities. Fifty-five percent of operators rank improving operational efficiency and reducing cost as their top priority, with 40% prioritizing sustainability practices and technologies, according to a survey by the Association for Smarter Homes & Buildings, in 2024. 

“You’ve got a lot of dumb buildings that aren’t telling us anything. The key right now is that we’re at an evolutionary moment in the way we think about buildings,” Haines said. “The systems going into our buildings are getting smarter. We’ve seen a massive shift in things like lightweight IoT technologies that are really used to connect things like utilization and to understand how buildings are being used.” 

Facility and system resiliency and reliability also remain a top focus, named by 53% of respondents, Trane says. Uptime and efficient operations also impact the bottom line, with 30% of the primary energy use in buildings wasted due to inefficient operations, per the report. 

Downtime also presents a significant financial and operational risk to organizations, Trane says, noting that 20% of all outages could be prevented with equipment controls. 

Due to these factors, every dollar invested in building modernization can return an average of $3 in operational savings over five years, Trane says, citing a 2022 McKinsey report. 

“If you can better control your decision making process around construction and renovation, you can make a big impact in terms of overall carbon emissions,” Jennifer Heath, director of product marketing at FM:Systems, said on the webinar. “If you’re really … able to make changes based on actual utilization, you’re going to be making a much sounder investment and making a much wiser choice.” 

In order to keep critical environments running, smart building technology can help to implement proactive monitoring, remote diagnostics, custom contingency planning and service level agreements that can extend equipment life with tailored maintenance plans, Trane says in the report. 

“We all want to operate high-performance buildings that are beautiful, have a really positive workplace experience and a light carbon footprint,” Haines said. “We’re going to have to work hard to make that happen.”

Warehouse and distribution center operators are using artificial intelligence and automation technologies to improve the effectiveness of their facilities in a number of ways, from managing vendors coming in and out of their buildings to maintaining assets. 

Skeens Warehouse Services, a third-party facility support firm based in Indiana, said it has partnered with CMMS provider Facilio to streamline how it manages work orders and communicates with vendors. 

"What used to take multiple tools, emails and manual steps is now streamlined and fully visible in one place,” said Riley Skeens, vice president of operations at Skeens Warehouse Services. “We've not only eliminated hours of manual coordination but are now better equipped to scale our services for new clients and international expansion."

The implementation of Facilio’s AI-driven Connected CMMS Suite, according to the two companies, automates and integrates how work orders are checked in and out and standardizes the process for submitting quotations and invoices. And it gives facility managers a dedicated portal to submit work orders and track vendor performance.

The partnership replaces disjointed tools and manual coordination, which had made it difficult to maintain efficiency at scale, the companies said Friday. 

AI-driven tools like Facilio’s Connected CMMS suite can also drive maintenance efficiency at warehouses and other industrial facilities, according to Ryan Chan, CEO and founder of UpKeep, a CMMS provider. 

By analyzing data collected from operations, the platforms can help facility managers optimize maintenance rather than wait for assets to break down or rely on repair and replacement schedules.  

“The future of asset operations is, instead of looking at assets at three distinct moments of time, looking at the entire lifecycle,” Chan said during a webinar. “The asset operations vision is shifting the mindset from looking at maintenance as a cost center to a profit center.” 

While AI can’t climb ladders or turn wrenches, it can help with data analysis, training and building a knowledge base, according to Chan. 

By using AI, operators can “run simulations of digital plans that will think, learn, respond and give you a digital blueprint of your entire operations,” he said. “It’s not just triggering alerts, it’s looking at the entire detection diagnosis, helping you dispatch the right person and helping you drive a resolution feedback cycle.” 

The systems can also be useful when facility managers are trying to navigate the line between operations that focus on the building and those that focus on the work that’s being done in the building. Being able to manage assets involved in both types of operation is especially important in warehouse and distribution center settings because these types of operations rely heavily on the physical building, from dock doors to storage areas. That makes strategically planning and managing the layout of facilities to optimize space paramount.

Facility operators that use AI-enabled platforms can realize efficiencies of 20% to 30% in inventory management, a 5% to 20% cut in logistics costs and up to 15% drop in procurement spend, according to a November 2024 analysis by McKinsey.

“Using a highly granular approach, the system evaluates the labor and assets (such as forklifts) required to complete warehouse operations on an hour-by-hour basis and analyzes the impact of variability in demand and resource availability,” McKinsey said in its report. “This helps the company understand the true capacity of its facilities and improves decision making by accurately predicting the impact of changes in labor, assets, or material flows.”

Sponsored content

By JLL

Think about where your data lives. Most organizations are sitting on fragmented data scattered across spreadsheets, siloed systems and manual processes. This leaves your team disorganized, but it also puts you at a strategic disadvantage. If you’re one of the 92% of commercial real estate professionals that has started to pilot AI in your organization, the lack of organized data can feel like trying to build a rocket with spare parts.

The good news? It doesn’t have to be that way. You can get organized and prepared to bring the value of AI to your teams and your properties. Let's break down three ways you can transform your CRE operations from reactive to anticipatory.

1. Consolidate your tech stack (and watch your budget thank you)

To find information, your team may have to bounce between a lease administration system, workplace planning tools, asset management platforms and multiple databases that don't talk to each other. Sound exhausting? That's because it is—and expensive.

Consolidate these point solutions into integrated platforms that create a single source of truth for all your real estate, facilities and workplace data. This isn't just about reducing licensing costs (though you will)—it's about building the structured data foundation that AI actually needs to work effectively.

When your data lives in one unified ecosystem, AI can finally connect the dots between occupancy patterns, lease terms and operational costs. The result? You move from rigid, process-bound decision-making to being able to adapt quickly while maintaining consistency.

2. Automate the routine, elevate your people

Your commercial real estate (CRE) teams probably don’t want to spend their days manually extracting lease data or scheduling maintenance requests. Yet these repetitive tasks consume enormous time and resources while introducing human error.

This is where AI can shine. Deploying AI-powered automation for routine workflows like lease abstraction, compliance reporting and predictive maintenance scheduling can save valuable time and remove headaches. But it isn't about replacing your team; it's about augmenting their abilities.

When AI handles the data-intensive grunt work, your professionals can focus on what they do best: strategic workplace planning, sustainability initiatives, vendor negotiations and relationship management. This shift from tactical operator to strategic leader doesn't just improve job satisfaction; it delivers measurable business impact through faster cycle times and proactive risk management.

3. Turn scattered data into AI-ready intelligence

AI thrives on high-quality, structured, connected data. Unfortunately, CRE data often isn’t organized enough to be fully taken advantage of. It’s often trapped in unstructured documents or scattered across incompatible systems.

We’ve called out four areas to focus on when preparing your data for AI:

• Data standardization to ensure consistency across markets
• Data integration to link data from your financial, HR and operational systems
• Data cleansing to remove duplicates and inaccuracies
• Robust governance to protect sensitive information

AI can actually support this and improve its own data readiness. Machine learning models can detect inconsistencies, reconcile records across systems and provide continuous quality monitoring. It's like AI is improving the very foundation it needs to operate effectively.

When you perform these three strategies together, a multiplier effect transforms your entire CRE function. Reduced system overlaps lower technology costs, automation reduces manual workload and strategic reallocation optimizes your human resources. The result? A leaner, smarter, more agile operation that costs less while delivering higher strategic impact.

The organizations embracing this human-AI collaborative approach today are positioning AI as enhancing your team rather than replacing it. They're not just keeping up with change; they're thriving because of it and building competitive advantages.

JLL’s technology advisory team can help you navigate the AI transformation. Learn more here.

Schneider Electric is releasing what it says is a first-of-its-kind AI-powered platform for energy, power and building systems, the company said at its 2025 Innovation Summit North America.

EcoStruxure Foresight Operation blends machine learning, predictive analytics and fine-tuned controls to reduce engineering workloads up to 40% and boost operational efficiency up to 50%, Schneider says.

Schneider says it will make the platform available to customers in beta mode “in the coming months” ahead of a wider release beginning in the third quarter of 2026.

Schneider’s existing EcoStruxure platform helps facility operators across a variety of industries and geographies improve efficiency and reduce environmental impact, the company says.

Its website features stories from a data center operator in Norway that improved its uninterrupted power supply system’s efficiency by 2% and an office high-rise in India that gained a single-console view into its HVAC, utilities, lighting and building security systems.

Schneider executives say the new AI-powered EcoStruxure platform promises even greater operational improvements. 

Likening the platform to “an intelligent operational assistant that never sleeps,” Manish Kumar, executive vice president of digital energy at Schneider Electric, said in a statement that EcoStruxure Foresight Operation “unifies energy, power and building domains for supervisory and control, which means systems finally speak the same language.”

That’s a step change from a status quo, where building and utility systems operate in separately controlled siloes, Schneider says. Those siloes can lead to problems like voltage imbalances that risk damaging equipment. Given how much energy the typical building wastes, having systems working in siloes isn’t helpful, Schneider says. The U.S. Department of Energy pegs average building energy waste at 30%.

It’s even more important that building systems operate seamlessly and efficiently in high-demand and mission-critical facilities like data centers and hospitals, Schneider says. The company claims facilities with EcoStruxure Foresight Operation can deploy large building systems faster — halving engineering time and reducing large systems’ setup time by 500 hours — and increase uptime during operations with AI diagnostics that can resolve “interrelated electrical and mechanical issues” 90% faster. 

EcoStruxure Foresight Operation hits the market as Schneider deepens its exposure to the fast-growing AI data center industry. Data centers have been the biggest driver of Schneider’s revenue growth for several quarters running, and executives on recent earnings calls have sounded optimistic that will continue.

One Schneider Electric installation partner said as much in an interview with CRN last week.

“AI cannot continue without hundreds of data centers being built in the next five years,” said Brad Bailleaux, vice president of sales for Lockstep Technology Group. “In the next three to five years, we expect our Schneider Electric business to grow tenfold.”

Bailleaux also said EcoStruxure Foresight Operation would more broadly be a “huge game-changer” for end-users, including middle-market companies without the vast facilities management footprint of the Fortune 1000.

“AI and software can now help clients determine the return on investment of EcoStruxure including the wattage, usage and the return on the energy costs. Today it takes three different individuals to create that report,” he said.

Carrier released upgrades to its Abound Insights platform that the company says will bolster the app with interactive maps and updated site view lists to help enterprise-level building operators manage their portfolios, the company said Thursday.

Abound Insights is an AI and IoT platform that uses predictive insights, recommendations and autonomous actions to help operators get better performance out of their equipment and operations, Carrier says. 

With the upgrades, operators will get operations and maintenance recommendations while accessing connected sites with weather and air quality overlays. They’ll also get advanced filters and export capabilities so they can download site lists, equipment details and work order reports for streamlined planning and tracking. 

"With these enhancements, we're giving building operators the power to see and manage their entire portfolio like never before," Yasir Qureshi, general manager of Carrier Abound, said in a statement. 

Getting a context-rich picture of building systems, like the platform is trying to provide, is a first step for building operators trying to meet long-term goals like saving energy, combatting cybersecurity risks and implementing preventive maintenance, studies have shown. 

"By delivering a truly enterprise-wide view, we're helping our customers unlock smarter decisions, drive measurable energy savings, elevate occupant comfort, and streamline operations across their portfolio — all through the intelligence of the Abound platform," Qureshi said. 

The upgrades follow AI-driven maintenance upgrades to the Abound platform in June that added interactive dashboards, alerts, to-do lists and recommendations for more than 25 categories of equipment in commercial buildings, including HVAC, refrigeration and controls. 

Carrier’s technology updates track with progress being made by its competitors. Earlier this week, Trane Technologies released AI tools that enable building operators to analyze occupancy, energy use and weather to adjust HVAC operations and provide recommendations on what actions to take based on conversational prompts. 

In addition to the software, Carrier recently marked a milestone in the energy efficiency capabilities within its hardware, earlier this month becoming the first HVAC manufacturer to complete the U.S. Department of Energy’s Commercial Building HVAC Technology Challenge laboratory validation in the 10-to-14-ton rooftop heat pump category. Lennox, meanwhile, first completed laboratory validation for commercial cold climate heat pump rooftop units in the 15-to-25-tons category earlier this month.

JLL has added AI capabilities to its building operations platform, enabling the software to automate administrative tasks, detect maintenance needs and provide recommendations, the firm announced Wednesday. 

The upgrades to Prism — a software used by building engineers and facility managers to track maintenance tickets, service requests and compliance — will enable faster decisions on resource allocation, tenant satisfaction, risk mitigation, vendor management and capital investments, JLL said. 

Powered by the company’s AI platform Falcon, the platform will pull property documents into an integrated, searchable resource so teams can analyze years’ worth of property task and workflow data and plan more strategically, JLL said. 

For example, teams can analyze service request patterns and get recommendations to improve tenant satisfaction, it said, or engineers can compare service manuals against warranty documentation to detect coverage opportunities and prevent out-of-pocket repairs. 

“AI can unlock a large number of use cases which have been very difficult using traditional technology, such as things that have to do with unstructured data, long documents, search and entity resolution,” Yao Morin, chief technology officer at JLL, told Facilities Dive earlier this year. “AI is not just answering questions you have or processing some data you have, but actually starting to take some actions for you to get to that last mile. AI used to be able to just navigate for you, but now it can drive for you.” 

Users can ask conversational questions like, “What issues from the prior quarter’s property inspection are still outstanding?” and get immediate answers with relevant recommendations, JLL said.

There are security protections built in, too, according to the company. Prism maintains “complete data separation so property data, tenant information and financial details remain private to each organization,” it said. 

“Prism AI supercharges ... professionals to make smarter decisions,” Daniel Russo, president of property management technology at JLL, said in a statement. “This isn't just about efficiency; it's about fundamentally elevating the standard of property management while delivering exceptional value to our clients and tenants."

Smart HVAC room controllers equipped with Schneider Electric’s proprietary “edge AI” model reduced energy consumption relative to room controllers without AI by 5%, on average, the company says in a March 17 report.

In field trials at four Canadian facilities from mid-November through late January, the controllers reduced energy consumption by as much as 15% “under specific operating conditions” while successfully maintaining temperature regulation and comfort compliance more than 85% of the time, Schneider says.

“This is a real application of AI in a use case that actually affects the built environment,” Peter O’Neill, vice president of BMS controls and devices in Schneider’s digital buildings division, told Facilities Dive.

Static control strategies and lack of real-time adaptability mean traditional HVAC systems struggle to optimize building energy efficiency and occupant comfort, Schneider says in the report.

Earlier this year, Schneider debuted a room controller that it says can reduce energy consumption by up to 35% when integrating HVAC, lighting and blinds operation. The new controller offered an opportunity to “enhance features that would have lived in the cloud and bring them down into the room,” O’Neill said.

Each room controller is equipped with AI that processes historical temperature data, occupancy patterns and environmental conditions in real time to predict the ideal temperature path for the room and dynamically adjust climate control settings, Schneider says. The model measures the results and learns over time, refining its thermal models and optimizing temperature predictions.

The extent of the model’s apparent energy savings surprised the Schneider team, O’Neill said. Optimized stop and start times, fewer temperature set point changes and room-level rather than floor- or building-level controls all contributed, he said.

“In that room-by-room granularity, there’s more of an opportunity to save,” he said.

The field trials also revealed a surprising level of user compliance. Very few users chose to turn the AI feature off despite the controller making it easy to do so, O’Neill said.

“That would tend to indicate that people are not noticing an issue with the environment they’re in, which we were most pleased to see,” he said.

Schneider ran the field trials at three of its offices in Montreal and Edmonton, and a fourth non-Schneider, “early adopter” location in Ottawa. One of the Montreal locations deployed 67 controllers across open office spaces, closed offices, conference rooms, a cafeteria and a range of other environments, the company says.

Schneider designed the new room controller to integrate into standard thermostat wiring for ease of installation, O’Neill said. And while the field trial locations were required to use Schneider’s EcoStruxure Building Operation building management system, the AI-enabled HVAC room controllers are “BMS-agnostic,” he noted.

“We don’t want to say that, if you don’t have a Schneider BMS, you can’t use this product,” he said, noting that it’s the “first device of this type with AI on the edge.” 

With field testing complete, Schneider is rolling out its AI-enabled HVAC room controller offering to commercial clients. One early adopter is a major hospital in Saudi Arabia, O’Neill said.

As commercial deployments continue, Schneider aims to roll out additional indoor air quality features and eventually quantify the product’s impact on facilities’ carbon intensity, O’Neill said. 

Smart water management solution provider Symmons Industries has announced a partnership with AWS services provider Caylent to help facilities managers detect water problems before they occur and possibly benefit from lower insurance premiums.    

“By reducing both the number and size of claims, properties [can] maintain a better insurance claims history, which insurers reward with lower premiums,” Tim O’Keefe, CEO of Symmons, said in an email. 

The companies’ new Evolution building management system is intended to help facilities managers head off water problems through its use of automated leak sensing technologies, including non-invasive ultrasonic flow, utility meter flow and point-of-source moisture detection, O’Keefe said. 

The platform also includes real-time anomaly detection, which identifies abnormal water use patterns, leaks and inefficiencies, machine learning-driven predictive maintenance tools that alert teams to potential system failures before they occur and generative AI-powered insights that can help facility managers with prescriptive troubleshooting recommendations, according to Caylent. 

“The challenge isn’t collecting water usage data anymore; it’s turning that data into decisions,” Randall Hunt, chief technology officer at Caylent, said in an email. “That’s where AI delivers real, measurable value. With Symmons, we built an AI-powered system into the Evolution platform that does more than just detect anomalies. Our machine learning models, trained on historical building data like flow rates and temperature fluctuations, can pinpoint abnormal usage with far greater accuracy and far fewer false positives than rules-based systems.”

Early results from Symmons’ Evolution platform have already shown significant benefits and improvements for customers across hospitality, commercial and multifamily properties, Caylent says. It says 80 million gallons of water was saved in 2024 across 74 high-usage sites. Additionally, over 400 leak incidents were detected and resolved in the past year, preventing an estimated $5 million water damage. 

Leaks, undetected failures and inefficient use of water results in millions of gallons of wasted water each year, leading to increased costs and strained infrastructure, Caylent says.  

“With water damage being one of the most common and most expensive causes of claims in commercial buildings, many insurance carriers are mandating leak detection systems,” O’Keefe said. 

The Evolution system can play a critical role in reducing insurance rates for large properties, O’Keefe said. “Many insurers offer significant discounts — sometimes up to 8-20% — to properties that install advanced water leak detection and building management systems,” he said. “Properties with proactive risk mitigation measures like BMS and water sensors are viewed more favorably by underwriters, making it easier to secure comprehensive coverage and avoid premium hikes or policy restrictions.”

While traditional building management systems require costly installations and specialized personnel to analyze data, leaving many facilities reliant on reactive maintenance, Symmons’ systems use AI to provide more streamlined and proactive management, according to the companies’ announcement. 

In the next five years, Symmons’ platform is projected to save 5 billion gallons of water, supporting ESG, sustainability and LEED-aligned environmental goals, per the release. 

“Since we implemented these upgrades, each proactive action taken thanks to the Evolution platform saved our customers an average of $10,000 per incident by preventing full system failures,” O’Keeffe said. 

"Partnering with Symmons Evolution was a strategic move that's already paid off. Beyond immediate financial savings exceeding $300,000 in the first year, our insurance premiums for 2025 improved dramatically thanks to reduced risk from proactive leak and temperature management,” Aimee Fyke, chief operating officer at Tharaldson Hospitality Management, said in a statement.

Facility managers wrestling with how to allocate scarce budget resources for maintenance and upgrades across a portfolio of properties can get an objective assessment of what to do, and when, with a tool that uses AI-assisted aerial imaging analysis, say executives whose company offers that kind of tool. 

“It’s a way to take a data-driven approach to allocating capital,” Miles Kirkpatrick, chief revenue officer at SITE Technologies, told Facilities Dive. “Typically the way it’s done today is, you’ve got your localized facility managers that just scream really loudly when they need work done. It’s a subjective way to allocate money. This is an objective way: you can have all those facilities ranked so you know exactly where money needs to go.”

SITE launched in 2018. The cloud-based platform uses AI to identify from aerial images problems with a facility’s roofing and pavement areas and it uses a team of engineers to recommend a maintenance and repair plan. By early next year the assessments will expand to include building facades. 

“We use Army Corps of Engineers standards to assign a code that’s widely used in the industry – a pavement condition index, a roof condition index,” said Kirkpatrick. “Our team of in-house engineers use the AI, plus their expertise, to provide a repair plan, then we help the clients manage the work by creating bids to send out to contractors.”

The tool can benefit facility managers overseeing one or a few properties, but the more properties involved, the more cost-effective it can be, the company says. 

“There’s definitely more value for the owners who have multiple properties, especially if they’re diverse throughout geographies, because it's more difficult for them to get the data they need,” said Austin Rabine, the company’s co-founder and CEO.

Although the assessments focus on roofing and pavement, the detail of the aerial images and the way the AI is configured makes the tool useful for other purposes, the company says. 

“If you ask someone, what is the cumulative roof square footage across your portfolio, many don’t know the answer to that,” said Kirkpatrick. “We very easily can tell you that because we just measure it out for you. Or you ask, ‘How many HVAC units do you have across your portfolio, or what is your percentage of asphalt versus concrete? How many parking spots do you have?’ Just base level information.”

Today, roof or pavement assessments are usually done by contractors that facility managers hire to come out, take pictures and provide recommendations in a report. 

“We were talking to a customer two days ago who told us they pay approximately $2,000 a facility to have someone do a roof walk,” said Kirkpatrick. “And then they pay a per diem on top of that for travel and food, so you could be, all-in, $2,200 to $2,600 a building.”

SITE doesn’t own or operate the drones that are deployed for the aerial images. It contracts with operators based near the property. It typically takes 1-2 days to get the flyover completed.  

There are almost half a million FAA-certified drone operators, what the FAA calls remote pilots, in the United States, according to the agency. 

“Finding the pilot is not necessarily the issue,” said Rabine. “We can get people out on site generally within a day or two.”

If there’s a delay, it typically has to do with airspace restrictions. If the property is near an airport, for example, there needs to be coordination between the airport and the pilot before the flyover can be scheduled. Or the property might be near sensitive government property — a significant issue in the Washington, D.C., metro area, Kirkpatrick said. 

Drones aren’t necessary for capturing the aerial shots, the company says. The platform is agnostic on how it gets the imagery. It could be by satellite, manned aircraft or even someone’s cell phone. The drone imagery provides the best quality, though. 

“We have customers – the Dallas-Ft. Worth airport and a railway provider – who have their own drone programs,” said Kirkpatrick. “So they’ll capture the data and just send it to us for analysis.”

The company didn’t want to get into the business of owning and operating drones, Rabine said, because there are plenty of companies that do that already. The value-add that SITE brings, he said, is in the AI mapping of the images and in the analysis, which can be done at scale. 

“One of the biggest problems we were solving when we started SITE was the ability to assess many sites across the U.S.,” said Rabine. “Now we’re in 15 countries. So, we wanted to use an outsourced data collection tool. Where we come in is in the platform.”

At the corporate level, companies can use the platform on a one-off basis as a due diligence tool when they look at the property as a possible acquisition. 

 Facility managers can use the platform for one-off projects, too, like inspecting damage to determine the scope of work needed for repairs. But they can get more value by using it for annual inspections so they can track roofing and pavement condition over time, the company says. That way they can rank work priority across their properties and catch degradation before it escalates into a crisis. 

“You’re going to have an all-up view of all of your properties,” said Kirkpatrick. “From worst to best, we’re going to stack-rank your properties – tell you exactly where you need to focus your capital.”

Facilities managers can ensure their vendors have adequate liability insurance to conduct on-site work with newly available technology from Visitt, a building operations platform serving the commercial real estate space. 

The company has launched an AI agent that it says can automate the management of certificates of insurance without any manual input from facility staff. 

Building operators rely on COIs to know their vendors are properly insured for the work they’re doing, but it can be time-consuming for facility staff to ensure all of the COIs of their vendors are sufficient for the work they’re doing and are in compliance with insurance rules. 

“There are many moving parts of compliance, such as expiring and missing certificates of insurance,” says Alicia Frazier, senior manager at Building Engines, a JLL property management software company. “It’s difficult to know everything that’s going on at any given time. Much less real-time updates.” 

For facility managers, the COI renewal process is very manual today, says Jonathan Kroll, co-founder of Visitt. The process requires facility staff to remind vendors or tenants that they need to renew, follow up on responses and then manually verify the accuracy of the files.

“The verification task is very binary, either it meets the requirements or doesn’t,” Kroll said in an interview. 

It’s the binary character of the verification process that makes it “great ground for AI to take over,” he said.  

Visitt’s new AI agent automatically extracts insurance requirements from contracts and checks incoming COIs for compliance, according to the company. That removes the need for manual review, guess work, oversight and time-consuming back-and-forth, it says. 

“Obviously there are certain communication aspects that you would like to delegate to a human or escalate to a human,” Kroll said. “Also, the final verification after the AI will be done by a human. But moving forward, I’m sure once the technology is more improved, people will feel fine with delegating the task to an AI, because they will not be scared for their job anymore and understand that they have a lot of other stuff to do.” 

Maintenance and lifecycle management

The COI agent builds on Visitt’s existing platform for helping building operators streamline work orders and manage preventative maintenance, tenant operations and amenities. 

The company sees the COI application as the start of a phase in which intelligent, self-directed capabilities will become part of broader building operations. 

“This milestone release is the first of a team of fully automated agents,” Kroll said in a statement. “It is a direct response to what property teams have been asking for: real, end-to-end automation that reduces administrative load without sacrificing accuracy or compliance, so teams can focus on what really matters – running their buildings efficiently and serving tenants better.”

Preventative maintenance and equipment lifecycle management are two areas that Visitt plans to release future AI agents for, Kroll said. 

A vast amount of data in the property management system – including inspection and maintenance notes and photos – is being recorded in the company’s platform that can be leveraged, he said.  

“All of this data is very valuable in order to determine the health and condition of a certain asset,” Kroll said. “Everything under preventative maintenance and equipment management, we see a lot of value in. These are very straightforward use cases, like COI management, and can save a lot of time.” 

The commercial real estate industry is rushing to capitalize on the promises of AI technologies, with pilot programs skyrocketing, but many companies lack systematic approaches to implement them, according to a report by JLL.

Commercial real estate professionals have been navigating a flood of bold predictions as well as hype warnings, said JLL, which surveyed more than 1,000 property executives across 16 markets. “Amid all the noise, decision-makers need to separate fact from fiction and have clarity on the true pace and payoffs,” JLL said. 

The number of companies running commercial real estate AI pilots or planning to do so has jumped from 5% in 2023 to 92% this year, the report says. JLL identified about 27 use cases, with organizations pursuing five pilot projects simultaneously on average. 

Ideally, organizations would start with simple, low-risk applications, like abstracting leases, enhancing employee experience or coming up with strategies for optimizing locations, fit-outs and construction projects. But with budget pressures impacting some 65% of organizations, CRE teams are focusing their AI resources on high-impact areas like portfolio optimization, energy management and data-related workflows, the firm said. 

The industry is still in the early experimentation phase, with most organizations still learning what works, JLL says. The firm noted that AI’s promise of “technological leapfrogging,” where organizations can skip intermediate steps to adopt cutting-edge solutions, has captivated business leaders facing tech gaps. 

The survey found “a sobering reality,” however. Rather than enabling laggards to leapfrog ahead, AI adoption is widening the gap between technology leaders and everyone else. Companies that already run successful tech programs are “pulling further ahead in AI outcomes,” JLL said.

“It’s certainly not a silver bullet,” JLL Chief Technology Officer Yao Morin told Facilities Dive last year when discussing the launch of the firm’s AI platform, JLL Falcon. Instead, he said, you should “identify practical use cases to take advantage of AI’s strongest capabilities to drive value.” 

While those companies with foundational capabilities — mature data infrastructure, established change management processes and experienced teams — are achieving more from their AI efforts, most companies – over 60% – are not, the report says. These companies must address fundamental technology issues, like tools with duplicated functionality and systems that are lying dormant, before they can fully leverage AI capabilities. 

JLL recommends organizations try to balance quick wins against longer-term foundational changes that require effort and testing. 

“For example, an occupier might implement AI tools for optimizing energy consumption — which is straightforward to assess — alongside solutions to achieve more complex outcomes such as increasing portfolio agility amid market challenges,” JLL said in the report. 

Organizations must also invest in internal AI talent and resources, strengthen data and cybersecurity systems and align technology rollouts with corporate decision-making cycles, the report says. 

“AI can only be as effective as the humans that are using it,” Morin said. “If you are going to spend the time and resources needed to build the infrastructure around your data to deliver on AI solutions, then you must ensure you effectively train your people on how to take advantage of it.”

Currently, only 33% of the workforce feels adequately trained on AI, while 81% of companies reported having at least three existing systems that aren’t generating the expected results, the report says. 

To boost training and upgrade legacy technologies, companies must get buy-in from everyone and be serious about change management. In its survey, executives say the best time to adopt or change technology systems is during other major changes to the business. But whether or not that kind of timing works out, organizations should move on AI adoption, the firm says.   

“There’s no going back,” JLL said. “AI transformation will only deepen from here…. Occupiers that wait idly for technologies to mature in the hope of a ‘second mover advantage’ risk competitive obsolescence as they miss the chance to experiment and understand how AI can deliver.”