From Data to Decisions: How AI Is Reshaping Multifamily Operations

The conversation around artificial intelligence (AI) in multifamily has shifted, and quickly. Just a year ago, operators were experimenting with chatbots and swipe-key analytics. Today, they’re asking entirely different questions: not “can AI work here?” but “where can AI create measurable operational value, and how do we build the infrastructure to support it?”

That’s the short version of what a cross-section of executives say they’re seeing in the multifamily sector, and the future they’re building for.

From Point Solutions to Platform

One of the clearest trends emerging across the industry is the move away from isolated AI tools toward integrated platforms that link data across departments, properties, and leasing touchpoints. “We’re seeing a move from a very point solution view to a platform solution approach,” says Xiyao Yang, senior vice president of digital marketing at Bozzuto Management Co. “Once you have access to the data, the tool becomes a whole platform connecting every single event during the leasing and resident journey.”

That platform thinking is reshaping how operators staff, plan, and respond to operational needs. Christopher Yip, partner at RET Ventures, says the first wave of AI adoption was largely about augmenting leasing teams with conversational tools. The second wave is more targeted. “The agents are getting more specialized,” Yip says, pointing to companies in his portfolio that deploy separate AI agents for leasing and maintenance, each purpose-built for its domain and integrated at scale.

Beyond Automation: Active Workflow Participation

For Carson Berish, senior vice president of product management for a national property maintenance provider, the shift from passive analytics to active workflow participation is where real operational gains lie. For example, his company’s AI platform, Aiden, does more than field maintenance requests. It improves them by prompting users to add important details and by capturing multimodal information like photos and voice descriptions. By helping residents create more complete service requests from the start, Aiden significantly improves downstream outcomes. Berish’s team has noted increases of 15% to 20% in first-time fix rates and 40% in priority accuracy.

“Better maintenance starts with better information,” Berish says. “People who submit work orders aren’t maintenance specialists. AI can do a really good job of asking intelligent follow-up questions.”

Residents aren’t the only ones who benefit from these tools. AI-powered vendor follow-ups, invoice validation, and proposal reviews can also streamline time-consuming manual work for on-site teams.

Reputation Management Gets Smarter

One of the newer frontiers in AI-assisted operations is reputation management. Justin Marshall, president, property services at Fogelman Properties, notes how AI tools allow operators to analyze sentiment across online reviews, forums, and other informal feedback channels. These are the places potential renters turn to get a sense of the “reality” behind a property’s brand. In fact, according to J Turner Research, 83% of consumers say they are more likely to trust reviews with text instead of a star rating.^[1]  This means that monitoring these channels can uncover specific operational issues, the kind that often don’t make it onto a Google review form.

“Instead of focusing on a score, you can ask what people are actually saying,” Marshall explains. “You can pull up the largest complaint about a community and compare it to three others. Suddenly it’s a management tool, not just a marketing tool.” The result is a meaningful evolution in how owners understand and respond to the resident experience, with reputation data becoming an operational input instead of a marketing metric.

What About the Human Element?

No matter how it’s used, one point is clear: AI works best with people, not instead of them. “The opportunity isn’t about replacing people,” Berish says. “It’s about helping operators scale their operational excellence in a very complex space and keeping humans at the center of critical decision-making.”

Marshall frames it as a question of business philosophy. “The companies doing it best are using it as a complementary tool,” he says, “to figure out information that allows them to train their people better, to provide better service, to give better feedback.” In a service-based industry where home is the product, the human element remains a competitive factor.

The Foundation Matters

Before starting to overhaul workflows, a word of caution: AI amplifies what’s already there, for better or worse. “If you don’t have your house in order when it comes to understanding your operations and workflows, AI isn’t going to solve that for you,” Berish warns. “You can actually make things worse by accelerating any fragmentation that sits underneath.”

The operators positioned to move fastest, according to Yip and Yang, are those who invested earlier in clean data infrastructure and cross-functional governance frameworks. AI governance, Yang notes, doesn’t require reinventing the wheel. It builds on the same principles as data privacy frameworks most organizations already have in place—or should.

The takeaway here is that it’s less about chasing the next tool and more about building the foundations that make any tool work. In that sense, the path to smarter AI use in multifamily operations runs straight through the basics: clean data, clear workflows, and people trained and empowered to use both.