AI and Smart Building Technology

Company leaders’ often vague predictions of how AI will be adopted are giving way to more fully formed strategies in the commercial real estate industry, recent earnings calls and other communications show.  

“This is precisely the moment when a disciplined, evidence-based point of view [about AI] matters the most,” Cushman & Wakefield CEO Michelle MacKay said on a webinar discussing AI’s impact on real estate. 

Here’s a sketch of what these company leaders have said recently about how they’re deploying the technology in their facilities management and other commercial real estate businesses.  

Cushman & Wakefield: Navigating stormy seas

The company recently rolled out its AI Impact Barometer, which it calls a first-of-its-kind research tool that will help occupiers and developers understand AI’s effects across sectors and asset classes. The tool has already given Cushman & Wakefield several negative and positive signals, the company says. 

“While it doesn’t have all the answers, we do feel … that we have enough information to lay out our current thinking and put forward a few scenarios … when it comes to making important real estate decisions,” Kevin Thorpe, Cushman & Wakefield chief economist, said during the AI’s impact on real estate webinar. 

Negative signals include rapidly declining employment in white collar sectors vulnerable to automation, which is rapidly accelerating, and increasing vacancy rates in class B and C offices as automation reduces back-office employment. 

“Make no mistake, AI will create winners and losers,” MacKay said during Cushman & Wakefield’s Q4 earnings call. 

Like many others in the commercial real estate space, Cushman & Wakefield is focusing on implementing the technology to drive efficiency in operations. 

In its assets services business, the company uses AI to generate insights within its proprietary platform and for space planning in its global occupier services business, MacKay said. In leasing, it’s using OneAdvise, which helps automate digital tour books, lease negotiations and benchmarks, she said. 

“What that does is really create a very strong data lake for us to work with as we are cross-selling to our clients,” MacKay said. 

Other use cases include the tracking of cross-selling opportunities, adjusting employees’ compensation, managing customer relations in its capital markets business and tracking contractual obligations in legal. 

“How [is] AI … driving that flow of data and information?” MacKay said. “If you think about de-siloing an organization, it is one thing to do it structurally and organizationally. It is something else to have the data flow freely throughout the organization.” 

CBRE: Digging through its data for treasure

Bob Sulentic, CEO of CBRE, said that his company is using AI in two areas: boosting efficiency and developing a knowledge advantage to differentiate its product offerings. The first use case includes the deployment of AI where its economic value clearly exceeds that of traditional efficiency levers, like offshoring. The company has been disciplined about understanding the trade-offs before pursuing efficiency-related AI investments, Sulentic said on the company’s Q4 earnings call. 

The second use case has to do with CBRE’s real estate data, which Sulentic says is the most comprehensive of any company in the industry. While in the past the company has not been able to turn that data into a comparably large competitive advantage, it’s using AI to change that, he said. 

“We are encouraged, in a balanced way, by both of these AI-related opportunities, with regard to the market-facing risks and opportunities AI introduces to our business,” Sulentic said on the earnings call. 

The company thinks about risk in three broad areas, he said: transactional businesses,  investment businesses where it creates and improves physical assets, and property and facilities management businesses, where it operates assets on behalf of clients.  

The firm’s transactional and investment work is the most protected from AI disruption, he said.  He noted that its brokerage business is “enabled, but not anchored to market data,” meaning AI can help its professionals work smarter through better data insights.  

This same dynamic occurs in its real estate investment business, where clients engage CBRE to plan and execute complex transactions because of its professionals’ creativity, strategic thinking, negotiating skills, deep base of market knowledge and broad relationships. “None of this seems likely to be replaced by AI in the foreseeable future,” Sulentic said. 

The third tranche of CBRE’s business, facilities and property management operations, is more exposed to AI disruption because it removes a lot of the labor that’s required to leverage the large amount of data that’s generated, creating both opportunity and risk, Sulentic said. 

“AI can both enable and disintermediate the data and knowledge side of this,” he said. “We believe the scale and complexity of our client relationships is helpful in mitigating this risk. The labor intensive side will not be easy for AI to disintermediate on the market-facing side.” 

By the end of 2026, Sulentic believes, there will be concrete evidence that CBRE has made real gains in terms of extracting the data it has, assimilating it and delivering it to its professionals in a way that it hasn’t before.

“That is being enabled by AI and that’s one of the areas we’re most encouraged today,” Sulentic said. “It’s going to save us money in terms of accumulating the data, buying the data, and it’s going to make our brokers more efficient in terms of using the data. We’re also using that same set of tools to meaningfully cut the cost of our research efforts.”

JLL: Disciplined data management gives shelter in rain

Efficiency is the main focus for JLL’s AI strategy, CEO Christian Ulbrich said on an earnings call. He noted that the technology has helped drive strong margin performance over the past two years as the company becomes more productive with its use of AI across its business lines. 

“We were able to drive that revenue growth without adding any more head count to the company, and that is a clear outcome from us deploying AI in a successful way,” said Ulbrich, noting he expects that trend to continue. 

The company has been using AI to identify opportunities for its brokers and provide its property management teams with tools to “really be on the spot for what the current pricing [is] for certain work and how to complete that work in an optimal way for clients,” he said. “It goes across the board, it’s very much also in our workplace management business.” 

To leverage the technology, the company for the last decade has been investing in disruptive startups through JLL Spark, the firm’s global venture fund, and it’s been investing in its data platform, Ulbrich said. 

“We have been successfully embedding technology and building proprietary data sets across our core services throughout this time,” he said on JLL’s Q4 earnings call. 

“We spent a tremendous amount of money to get our data in order, which is not trivial in an organization which was built country by country over such a long period of time. But we turned the corner on that one, and that is really helping us in a meaningful way to provide our clients with insights which are not easy to get by by people who don't have that data ,” Ulbrich said. 

He added that the firm will continue to invest in its own platform and develop new internal tools that its teams can use to better service clients; for the time being, JLL has “no intention to increase our investment into third party prop tech startups,” he said. 

“The data is so crucial that we need to do it in-house,” Yao Morin, JLL chief technology officer, said in a 2024 Q&A interview with Facilities Dive. “A lot of the traditional commercial real estate companies develop technologies with the outsource model. We want to build and control the data. We want to protect our data. We want to make sure we build our own secret sauce, so we can stand out from a brokerage perspective and facilities management perspective, and our competitors cannot just copy or license [our approach].”

Ulbrich shared that sentiment on JLL’s earnings call when he highlighted that the company has large amounts of proprietary data that is hard for other companies to gather. “With that proprietary data, we can build tools, which enable our people to drive better outcomes for clients, which then leads to higher revenues per head for our transactional people, but also for other areas of our business,” he said. “The more scaled you are in a business, the better protected you are, because you have data.”

At the same time, the investments in its data platform and in AI are intended to give its professionals tools to offer more value to their clients and not replace what they do.

“The complexity of the commercial real estate asset class, the criticality of real time local market expertise, and fiduciary responsibilities involved create structural barriers [to disintermediating professionals],” he said. “The momentum is very fast on AI, but at the end of the day, there will be human interaction. And that human interaction will be executed by people who have the right data at hand and have the knowledge to deliver that service.”

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 have said. 

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.”

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By JLL

Commercial real estate leaders are investing heavily in artificial intelligence, connected devices and intelligent building systems to improve operational efficiency, reduce costs and enhance tenant experiences. Yet many still struggle to realize meaningful returns from those investments. 

It would be easy to blame technology itself. But it’s not that AI has fallen short – it’s that most organizations are deploying it on a disconnected, incomplete data foundation.

Building management and maintenance systems, IoT sensors, enterprise resource planning software and sustainability tools all generate enormous volumes of information. But too often, that data is siloed in separate, disconnected systems, governed by different standards and managed by different teams. The result is fragmented information, inconsistent reporting, and limited visibility across the portfolio.

Artificial intelligence can process data at unprecedented speed, but it cannot compensate for incomplete data. And in commercial real estate, that means the smartest buildings aren't the ones with the most sensors. They’re the ones with the strongest data foundation.

Building a better data foundation

According to JLL’s Global Technology Survey, organizations are prioritizing investments in AI, digital infrastructure modernization, and strategic technology advisory services. Yet just under 40% report having fully audited their existing technology systems and data, while an equally small percentage say their business intelligence tools are functioning as expected. 

“Despite the high level of interest in AI, and then by default data, we see that the majority don’t really have a strong grasp on it,” says Ibrahim Yate, Research Director for Global Technology Consulting at JLL. “They haven’t audited their data as much as they should. And this reflects the fact that most CRE professionals aren’t experts in data; at least not the technical aspects behind data production and delivery.”

In other words, in the breathless race toward AI, many organizations are struggling to build the very foundation that makes AI possible.

The reason is simple: commercial real estate data rarely lives in one place. Without strategic integration, AI simply exposes and amplifies those disconnects.

“Let’s be honest about what’s often happening with real estate data,” says Andy Targell, Managing Director for Technology Consulting at JLL. “Lease records sit in one system, capital projects in another, energy ops data is elsewhere…. Data’s in lots of different places. So when someone asks, ‘Which assets in my property should I look at for energy efficiency?’, or ‘How do I drive sustainability?’, getting that answer is difficult because data is disconnected across different systems.”

Smart buildings, smarter data

A truly intelligent building doesn't just collect data -- it connects it. And that requires a well-designed data platform, what JLL experts call “the engine” of the modern CRE digital transformation. Such platforms transform raw, unstructured data into trusted business intelligence, enabling organizations to answer questions more accurately while providing the advanced analytics required to train AI models.

Well-designed data platforms establish governance norms, standardize information, and connect disparate systems, creating a trusted source of operational intelligence. Rather than forcing teams to reconcile conflicting reports, they enable AI to generate insights from a common version of the truth.

When information flows seamlessly across building systems, organizations gain a comprehensive view of asset performance, occupancy, energy consumption, maintenance history and operational risk. AI can then identify patterns that would otherwise remain hidden -- predicting equipment failures, optimizing maintenance schedules, improving workplace utilization and uncovering opportunities to reduce operating costs.

The difference isn't the algorithm – it's the quality and accessibility of the data feeding it.

Human expertise remains the competitive advantage

Technology can do a lot: it can identify anomalies, make recommendations, and automate routine tasks. But it can’t establish governance frameworks, prioritize investments, or determine which business problems, build stakeholder consensus or decide which business priorities deserve attention first. Those responsibilities still belong to experienced professionals who understand both commercial real estate operations and technology strategy.

Because at their core, smart buildings aren’t about connected systems. They’re about connected decisions.

Interested in learning more about where to start with your data foundation? Talk to one of JLL’s technology advisors.

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.”

Compliance efforts for New York City’s Local Law 97 have been mixed, according to Ari Shultz, vice president of business development at Entech, a New York-based company that helps retrofit HVAC systems with AI-powered boiler and energy control systems. 

Although many owners have moved quickly to measure and improve their building’s performance, others are only now getting started, which could hurt them down the road when they have to start meeting more stringent performance requirements, he said.  

“The early movers, they're on top of it from the get-go,” Shultz told Facilities Dive. “They're speaking to people. They want to know what different costs benefits [there are] for whichever option they're looking for. And you have … people that [are] not really even sure where they are with their usage, right? We have people coming out now, after the fact, just getting up to speed.”

New York City’s Local Law 97 standard reporting period began May 1, with a June 30 grace period and an additional 120-day extension, making the final reporting deadline August 29. 

The ordinance requires commercial, multifamily and municipal buildings of 25,000 square feet or more to report annual energy, water and emissions use. Going forward, they’ll be required to reduce emissions 40% by 2030 and 80% by 2040. Different emissions limits apply based on property type. Failure to comply can result in costly fines: up to $268 per ton of carbon dioxide equivalent emitted above the annual allowance.

Although roughly 90% of large buildings were compliant at the end of 2024, meeting standards will likely get harder moving forward as emissions limits drop significantly for the 2030-2034 compliance window, according to an analysis by the Urban Green Council. 

Legacy system challenges

For owners of buildings with legacy systems, meeting the requirements can be a daunting task, but technology is available to help even fossil-fuel-powered HVAC systems like boilers operate more efficiently, Shultz said. The key is to get started early.  

“It’s very difficult to set up implementing this at the end,” he said. “If you … slowly put the pieces in place, it’s a lot more manageable.” 

Building owners his company has worked with have saved about $42 million and reduced their carbon emissions by more than 137,000 tons by adopting AI-assisted sensors and other technology that help optimize systems, according to a company spokesperson. The average emissions reduction for each building is about 22%, the spokesperson said. 

“We’re in 9,000 buildings and internal[ly] benchmarking,” Shultz said. “We’re taking data points every 15 seconds. So we know exactly what’s going on inside the boilers [and] we have a good idea of what it’s going to look like before our system and after our system, so we can walk them through what that’s going to look like. That’s usually a great step.” 

Other energy management companies that help building owners in New York City include Runwise, Intech 21 and Parity. 

Shultz said that when his company meets with owners, sometimes their building systems are already working well and Entech’s role is to help them optimize. Other times, early conversations and scans uncover leaks or other issues that are impacting performance and harming energy efficiency, he said. 

The company recently went into a building with two identical boilers and found through scanning that one of the boilers had a leak. “Within 30 minutes of getting our system installed, we were able to see that there [was] a massive stem leak,” Shultz explained. “So instead of waiting three months to review bills and figure out what’s going on, [this was done] within a minute. Because … we don’t just focus on the temperature throughout the building. We’re integrated with the boiler. We have the run times — how much it’s running, the distance, the delay from the time we run until [heat] starts hitting the different units.” 

When it comes to buildings with boilers, steam leaks are a big concern, and they’re often overlooked, he said. One of the benefits of Local Law 97 is it can help owners uncover these hidden costs. 

“There [are] so many systems that are not working well that people are just not aware of,” he said. “Water leaks [are] a big one, because you have the return line that comes all the way through the building and goes underground. There’s an insane amount of water going out. It’s bad for the foundation.” 

The sensor-based analytics and control systems that energy management companies offer their customers are the kind of tools that can catch these problems — including water problems that aren’t the tools’ main focus — that can often go undetected for a long time.    

“It’s built into all our sensors, so we see it,” said Shultz. “And then we call [the customer] up and say, ‘You have a real problem here.’ [In this case] they called people down. They dug up the ground [and] they found it.” 

A potential catastrophe became a source of significant savings in this case, he said. 

“We have a good idea of the diagnostics,” he said. “We know from the actual system itself how much it's overusing and where the savings are going to be. … We know that we saved you some money [and] some energy.”

The large language models that underlie generative AI offer an opportunity to help everyone involved in buildings — architects, engineers, construction professionals and facility managers — access building systems that converse with one another on optimizing development, maintenance and compliance issues.

“It’s time for us to think about not looking at predictive buildings, but more towards conversational buildings,” Nan Ma, assistant professor of architectural engineering at Worcester Polytechnical Institute, said at an ASHRAE conference.  

Ma, founding director of the Laboratory for Healthy, Environmental, and Resilient Buildings, or HERB-Lab, at WPI, led a group of global researchers on a Building and Environment study earlier this year that looks at how LLMs can help make sustainable, intelligent and human-centric buildings a reality.  

“Things like energy use, comfort, occupancy, maintenance logs, control behavior [and] control logics” are the kinds of factors that LLMs could process to enable building systems to better help managers maintain operations better, she said. 

For example, a building manager, maintenance team or facility manager could ask an LLM why the eastern zone of a building is so hot in the afternoon, and receive an answer based on information that has been logged over years. The LLMs will process previously siloed data on temperature, energy consumption and occupancy patterns, among other things, and then share that information across building systems to produce an answer along with its context, Ma said.

The idea of using LLMs to enable building systems to converse with one another solves one of the biggest problems faced by building operators who want to use AI to help them manage their facilities better, what’s known as a “great handoff problem.” The term refers to the separation among sources of data created in a building’s lifecycle and the designs, principles, ideas and equations produced by architects and engineers. The data isn’t easily communicable across disciplines, impeding the ability of stakeholders to use the information in a meaningful way, Ma said. 

But LLMs sharing the information with one another could allow building systems to more easily communicate their information, helping managers to maintain buildings better, she said. 

The process could also enable LLMs to streamline regulatory compliance and building code interpretation for design and construction, according to the research Ma presented. The LLMs could accomplish this compliance help by translating natural language building regulations into a machine-interpretable and computable representation, then extracting information from building information models, or BIMs, and enriching and matching concepts across codes and BIMs. The LLMs then automate rule execution, among other steps, to generate reports that detail non-compliance and suggest potential fixes, according to the research. 

“It doesn’t have to be perfect, but we don’t have to start from scratch,” Ma said.

There remain areas where more effort is needed before LLMs could pull the most value from the data, said Ma, pointing to HVAC control and intelligent building control as two examples. 

“Buildings have become a knowledge system,” Ma said. “There are just so many data points. They’re in very different formats, they’re in very different structures, and it’s very hard to come up with a pipeline or schema to connect all of them.”

To reach these conclusions, the researchers examined a number of questions concerning the role of LLMs in shaping sustainable, intelligent and human-centric buildings, Ma said. Among them: How can LLMs streamline regulatory compliance and enable intelligent digital twins across the building lifecycle? The researchers also looked at the ethical responsibilities posed by LLMs in the built environment. 

AI can help security teams and remote systems technicians react to incidents with speed, intelligence and fewer errors, said Greg Parker, vice president of innovation and portfolio management at Johnson Controls. But legacy technologies like 10-year-old building management systems may limit the technology’s benefits, he said.

“If I … want to take advantage of some of these newer technologies that are out there that give me holistic, proactive protection, they’re not going to work that well on old systems,” Parker said in an interview. 

Parker explained that AI can be used to determine if a camera isn’t pointing the way that it was originally intended, for example, or if it’s blurry or if it’s at a different resolution than is needed. It can also be used for heat mapping, or determining where people are congregating in a building. That can help not only with safety and security, but also with merchandising in the retail space and in other contexts in which traffic flows are important.  

“If you think about sports venues,” he said, “maybe a crowd is forming because I only have three doors open… . That crowd can be managed more effectively to disperse if I have the information that I can readily act upon to open up more egress.” 

AI also can detect threats faster, he said. “AI can tell you in a millisecond if something is out of the ordinary,” he said. “If you look at that same video … with the human eye, it may take anywhere between five seconds and maybe longer. It’s more like a Where’s Waldo situation.” 

That capability saves time, saves money and can save lives, Parker said. “That’s another way that AI is already being used and will continue to develop further as our systems and our processes [do]. Ultimately, it’s about speed and effectiveness,” he said. 

The AI in Johnson Control’s Connected Security platform enables remote technicians to receive not just a series of alarms but insight into what the alarms mean and how to respond, he said. 

“There’s [now] intelligence behind those [alarms] that tells you, ‘This is, with X percent probability, going to cause this reaction,’” he said. “There is a sequence of operations that now has been intelligently woven together through agents.”

He cautions, however, that not all existing building infrastructure works with the AI-assisted security systems Johnson Controls offers. 

With some customers, “we’re going to have to consider a building block scenario to get you there,” he said. “We need to upgrade your security system…. But guess what? You’re probably needing to do that anyway because you have these other risks that are out there that are really hard to debate… . If you have a [building management system] that hasn’t been upgraded … that’s a big red flag,” Parker said.

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.

Johnson Controls has released the latest version of its flagship building automation system, Metasys 16.0. 

The update is designed to help facilities reduce downtime, strengthen cybersecurity and bring systems online faster across complex environments, Johnson Controls said in a release. 

Advances in artificial intelligence, biotechnology and advanced manufacturing require building systems to have stepped-up reliability and cybersecurity safeguards and meet compliance requirements, the release states. 

Metasys integrates a range of building technologies to help facilities managers focus on energy efficiency, systemwide optimization and thermal performance, the company says. Technological advances and demand in data centers and advanced manufacturing are making thermal management a critical pillar for companies, Johnson Controls said in its announcement. 

Operators are also leaning on digitally enabled lifecycle services to maintain uptime, according to the company. A single hour of interruption costs businesses over $100,000, according to Deloitte, with 20% of reported outages costing over $1 million, according to the Uptime Institute. 

The Metasys 16.0 update responds to these needs by providing faster deployment, more autonomous operations and resilience features that enable systems to continue performing through disruptions, the company said. 

“The environments our customers operate in are more complex than ever, and expectations for performance continue to rise,” Faisal Pandit, president of building management systems at Johnson Controls, said in a statement. “Even brief disruptions can impact operations, productivity and outcomes.”

Recent physical strikes affecting hyperscale cloud facilities in the Middle East underscore that data centers are increasingly treated as critical digital infrastructure, according to research by Morningstar. That same strategic importance can also increase vulnerability to cyber attacks from state actors. The concentration of compute and storage within large-scale facilities means that operational impairment can have wider repercussions than a conventional property-level incident, Morningstar says. 

Cyberattack risk is also the largest threat to maintaining equipment performance and uptime, cited by 22% of respondents to a survey released earlier this year by Johnson Controls. Those risks also increasingly impact operators in manufacturing, commercial, retail and hospitality sectors that run processes through legacy building management internet protocols. These legacy systems often operate “under the covers of many proprietary [building management system] implementations,” says cybersecurity firm Claroty. 

Against this increased risk, the Metasys update can help facility managers maintain control, Pandit says. “Metasys 16.0 ... brings [resilience features] together in a single platform, helping customers maintain continuous operations, reduce risk and adapt as their needs evolve,” he said. 

On the cybersecurity front, the updates align with IEC 62443-4-2 Security Level 2 protections to reduce risk and support evolving requirements, the company said. A “fast track” feature can cut upgrade times by up to 40% and accelerate adoption of the latest cybersecurity and operational enhancements, it said. 

Visual integration tools enable real-time data sharing and help operators deploy integrations up to 80% faster, the company said, resulting in up to 70% cost reductions in integrations. 

The update can also help cut engineering and deployment costs by up to 30% and support 30% more IP devices — up to 1,300 — for greater scalability. Furthermore, the system now aligns air and water sequences to ASHRAE Guideline 36, which should deliver more consistent performance and up to 30% energy savings, the company 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 says.

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 company has annouced. 

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."

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.”