At the Real Estate Technology & Transformation Center Technical Summit in February, Amy Barricelli joined a panel of operators discussing artificial intelligence (AI) adoption, where the divide was clear: One was all in; one was taking a measured, pragmatic approach; and one was content not to be a first mover.
For Barricelli, senior vice president at RR Living and a member of the Multifamily Innovation Council, the throughline was clear in how the industry’s AI push is reshaping workflows across the asset lifecycle. The tools are getting sharper. The workflows are getting faster. The appetite to automate is still growing. As technology moves deeper into the operating model, the line between efficiency and experience is becoming more defined.
That tension runs through nearly every corner of multifamily right now. Owners, operators, and vendors are moving past AI as a novelty and into the proving grounds of strategic and tactical execution, from leasing and maintenance to underwriting, portfolio strategy, and resident communication.
In the process, a clearer picture is emerging. The most valuable applications tend to live in speed, analysis, coordination, and administrative relief, while harder questions appear when automation reaches moments that require trust, empathy, judgment, and local knowledge.
Across the industry, those calls are already taking shape in different ways, on the ground, inside portfolios, and in the day-to-day work of running communities. How it plays out depends on where you sit in the business. The five leaders who follow each bring a different perspective on where AI is starting to deliver.
The Operator
In her role at RR Living at RREAF Residential, Barricelli oversees property operations across a growing portfolio of multifamily communities, giving her a direct line of sight into how technology shows up at the property level. From leasing and marketing to maintenance coordination and resident communications, Barricelli’s teams are responsible for the day-to-day workflows that keep apartment communities running. And increasingly, those workflows are where AI is starting to make a measurable impact.
One of the most immediate gains, she says, has come in handling the steady stream of routine resident and prospect communication that once consumed large portions of on-site teams’ time. “Where we’re seeing real traction is in the areas that historically pulled our teams away from the resident-facing work they wanted to be focused on,” she says. “There are a lot of repetitive questions in leasing and resident communication, and AI can handle a surprising amount of that. If the technology can answer routine questions or help guide a prospect through the early part of the leasing process, it frees our teams up to focus on the interactions that actually matter.”
At RREAF, that philosophy has translated into targeted deployments rather than sweeping automation. AI tools are being layered into marketing workflows to help generate property descriptions and campaign copy, assist leasing teams with prospect responses, and help property staff organize and summarize operational information more quickly. The goal, Barricelli says, is not to replace staff but to reduce the friction that slows teams down.
“We’re looking at places where AI technology can remove steps from the process,” she says. “If you can help a leasing professional respond faster, organize information faster, or generate the first pass of something that used to take 20 minutes, that’s meaningful, and those small efficiencies quickly add up across a portfolio.”
For Barricelli, that kind of incremental workflow improvement is where AI is proving its value first. Property operations remain complex and highly local, but when the technology is applied carefully to everyday tasks, from generating marketing content to handling routine prospect and resident inquiries, it can give on-site teams something they rarely have enough of: time.
The Product Builder
Carson Berish approaches AI from the inside of the machine. As senior vice president of product management at Lessen, he helps design the software platforms that connect multifamily operators with the service providers who maintain their communities. That position gives him a unique vantage point on one area where AI is beginning to deliver measurable operational value: within the dense web of maintenance, vendor coordination, and service workflows that underpin property operations.
“AI is incredibly powerful when you apply it to something that already has a defined process behind it,” Berish says. “Where we’re seeing success is in areas where the steps are repeatable, like routing work orders, coordinating vendors, helping teams surface the next step in a workflow. When the process is structured, AI can remove a lot of friction.”
Surprisingly or not, maintenance operations are an early area where AI is gaining traction. A single work order can involve residents, on-site teams, regional managers, and outside vendors, each passing information back and forth as repairs are scheduled, approved, and documented. Berish says AI is increasingly being used to organize those interactions, summarizing service histories, flagging next actions, and helping operators move faster through routine operational steps that previously required manual coordination.
It has also clarified where these tools actually work. In practice, that comes down to the quality of the underlying workflow.
“One of the biggest things we’ve learned is that you can’t just drop AI on top of a broken process,” Berish says. “If a workflow is unclear or inconsistent, the technology will just amplify that problem. But when the process is well defined, AI can dramatically speed things up to help people find information faster, automate routine steps, and keep operations moving.”
For Berish, that combination of clean, defined processes first, automation second is quickly becoming the blueprint for how AI is being successfully deployed inside multifamily operations today.
The Technologist
As chief information officer at Bainbridge, Tony Lopez oversees the digital infrastructure behind a national multifamily operator, spanning property systems, integrations, cybersecurity, and data architecture. That role places him squarely in the middle of the industry’s current AI experimentation, where operators are trying to determine how AI-powered solutions fit into an already complex technology stack.
“Where we’re seeing AI start to deliver real value is in helping people work with information more efficiently,” Lopez says. “There’s an enormous amount of data inside a multifamily organization: Operational data, financial data, leasing information, maintenance records, and AI can help teams organize and interpret that information much, much faster than before.”
Inside Bainbridge, that capability is showing up in practical ways across departments. AI tools are being used to summarize reports, surface operational insights, and help employees find information without digging through multiple systems. In organizations that operate across dozens of communities, simply making internal knowledge easier to access can dramatically change how teams work.
“We’re using it to help people interact with our data in a more natural way,” Lopez says. “Instead of running multiple reports or searching across different platforms, you can ask a question and get a much faster picture of what’s happening.”
One example of where Lopez sees AI being applied against structured data is in helping operators manage the increasingly fragmented software environments that define modern property operations. A typical multi-
family company relies on dozens of platforms, from property management systems and maintenance tools to marketing and accounting software, each producing its own stream of data.
“AI can sit on top of those systems and help connect the dots,” Lopez says. “When you start bringing information together across platforms, people can understand the business much more clearly.”
In that sense, the most important impact of AI may be structural rather than flashy. By making operational data easier to access and interpret, the technology has the potential to simplify the increasingly complicated IT environments that multifamily operators are currently managing every day.
The Strategist
Dan Carr spends much of his time thinking about the intersection of ownership goals and operational execution. As executive vice president of portfolio strategy and expansion at Arqline, Carr is focused on growing a property management portfolio by helping owners and operating teams determine how assets should be positioned, improved, and managed across different markets.
That strategic layer of the business has historically depended on large amounts of research and internal collaboration: areas where Carr says AI is beginning to quietly improve the process.
“AI is really helpful when it comes to organizing information and surfacing insights that would normally take teams a long time to pull together,” Carr says. “If you’re looking across markets, ownership groups, and asset performance, AI can bring a lot of that data together quickly so you’re starting the conversation with better information.”
Within Carr’s team, those capabilities are showing up in the early stages of portfolio analysis and business development. AI tools can scan ownership data, track portfolio performance across markets, and help identify groups that may be evaluating new operating partners in minutes instead of months. Instead of manually researching potential opportunities, teams can start with a clearer map of where activity is happening.
“That kind of front-end screening is incredibly valuable,” Carr says. “You can highlight ownership groups that might be good partners, see how their portfolios are performing, and walk into a conversation with a much stronger understanding of what their goals might be.”
The technology is also helping teams process internal operational information more quickly. Portfolio reviews that once required extensive preparation can now be supported by tools that summarize performance trends and surface key metrics across assets and regions.
“What it really does is accelerate the preparation work,” Carr says. “When the data is organized and insights are easier to see, the team can spend more time actually talking about strategy instead of gathering information.”
For firms operating across multiple markets, Carr says that ability to move faster through the research and analysis phase is already changing how teams approach strategic planning. Even if AI may not be the strategy itself, it’s becoming an increasingly valuable tool for building the foundation that strategy rests on.
The Investor
For Don Oldham, AI is beginning to reshape the earliest stages of multifamily investment analysis.
At Thompson Thrift, where Oldham closely supports development and portfolio strategy as senior vice president of IT, AI-enabled technology is already helping investment teams process the massive reams of market and operational data that go into evaluating deals.
“AI can process an incredible amount of information quickly, and that’s where it becomes useful,” Oldham says. “You can look across market data, operating performance, demographic trends, and all kinds of things that used to take a long time to assemble, and start to see patterns much faster.”
In practical terms, that means speeding up the research and underwriting process that sits behind new investments. AI tools can quickly pull together demographic data, rent trends, analyze submarket supply pipelines, and comparable property performance across multiple markets. Instead of analysts spending days gathering information, AI is allowing teams to move more quickly toward evaluating the opportunity itself.
That ability to synthesize large datasets is particularly valuable for development-focused firms like Thompson Thrift, where understanding market momentum and submarket fundamentals is critical to identifying the right sites and timing new projects.
“The technology is really helpful in surfacing insights that might otherwise take a long time to uncover,” Oldham says. “If you can quickly see how a submarket is evolving, see what rents are doing, see what new supply looks like, and how demographics are shifting, then you’re working from a much stronger foundation of information.”
For Oldham, that kind of analytical acceleration represents one of the clearest ways AI is beginning to influence investment strategy in multifamily, by dramatically compressing the amount of time required to gather and synthesize the information that drives it.
“When you can process data faster and see trends earlier, you’re simply making better-informed decisions,” he says.