Tyler Christiansen, CEO of Funnel and Fenix, on the future of AI in multifamily.
The race to automate everything is on, and real estate is no exception. Anyone looking for an apartment recently has probably noticed the customer experience is changing: more tech, fewer teams.
In multifamily housing, property management companies are rapidly adopting AI tools to streamline operations and accelerate leasing. Some are even asking: Do we still need any humans at all?
Even though I lead an AI company, I’m not betting on an AI-only future. Cutting humans out of the leasing process entirely doesn’t streamline operations. It often shatters renter trust, sidelines talent and delivers a worse experience for everyone.
While others chase full automation, I believe in a different path: one where AI empowers people, not replaces them. One in which AI is embedded into day-to-day workflows and is smart enough to know what to say, when to say it and when to escalate matters to human team members.
Learning from the past
The multifamily industry experienced this playbook before, and so have I. Five years ago, when we embraced centralization (shifting administrative and leasing tasks off-site to specialized teams) and restructured roles around strengths, many called it unrealistic. Others took it too far and wondered if we needed teams on-site if they centralized their operations.
Today, even former skeptics often rely on the combination of specialized roles and on-site team members to support the renter experience and drive efficiencies. Centralization is the standard for any operator serious about efficiency, scalability and employee retention—not because it replaced teams, but because it reimagined how to support them.
The AI-only experiment
AI is at a similar crossroads. AI unlocks efficiencies, but taken too far, it can damage performance. For example, Klarna publicly replaced many of its support agents with an AI assistant. But earlier this year, “due to declining service quality and customer dissatisfaction, the company is now rehiring human agents.” Air Canada’s chatbot promised a policy that didn’t exist, landing the company in court.
We’re seeing the cracks with AI. Operators who were frustrated with their teams and looked to solutions that purport to be perfect virtual agents often found their human teams disengaged more. Not because people are lazy, but the system told them to stand down. Many AI systems won’t hand off when a customer indicates they want to interact with a human.
This isn’t an AI problem. It’s a leadership problem. When AI is misused or over-promised, visibility and accountability vanish. Then, when things go wrong, it’s the teams who face the brunt of direct feedback.
Addressing the challenges of AI
One of the biggest challenges with AI isn’t technical; it’s cognitive. If teams are led to believe the system will “do everything,” they disengage. An MIT Media Lab study showed how heavy reliance on AI tools reduces recall and weakens critical engagement. The same thing happens in the workplace: When employees offload too much to AI, accountability slips and performance suffers.
Leaders need to be honest about what the technology can and can’t do, and set clear boundaries that keep people engaged in the right parts of the workflow.
The second challenge is about human dynamics. Teams need clarity on how their roles evolve once AI enters the workflow. If AI is positioned as a competitor rather than an assistant, employees can feel sidelined instead of supported. Blurred responsibilities, like whether the bot or the associate follows up, quickly create frustration. Without coaching and governance, teams fall back on old habits or work around the system altogether.
The companies that succeed treat AI as a partner: They define explicit handoffs, reinforce how to use AI outputs day-to-day and track whether the tech is actually improving outcomes. Done this way, AI frees people up for higher-value work and elevates both the renter and employee experience.
Why renters aren’t impressed
Nobody likes shouting “representative” at a chatbot. AI works—until it doesn’t. And when it fails, it often fails hard, with the customer caught in the middle.
Multifamily is no exception. Ask any renter who’s submitted a lead and received automated messages. Instead of reducing friction, it creates more of it, pushing people away before they ever set foot on-site.
I think successful operators will put AI and automation to work with their teams, not instead of them.
What protects brand voice?
Many multifamily operators are deeply brand-conscious. They believe their tone, service model and community experience are what set them apart from the competition. They’re not wrong. That sense of personal care is what turns a prospect into a resident, and a resident into a renewal.
By now, we know: AI isn’t magic. You must train the models, or work with those who do, to ensure that how you’ve been differentiating your brand and service is translated into this new form.
Another challenge is scale. When you’re deploying AI across dozens or hundreds of communities, it’s not about uploading a tone guide and calling it a day. It’s about curating examples that reflect the nuance of your communities. Without that structure and training, it starts to lose the intimacy and care that renters associate with your brand.
Not an argument against AI—an argument against misusing it
At its best, AI extends team capacity without undermining trust: handling after-hours inquiries or surfacing red flags in resident conversations. AI handles these tasks well because they’re repetitive, time-sensitive and often overlooked.
For complex moments—renewals, collections, applications—the stakes are higher. That’s where agentic workflows come in, and to be successful, these need systems that go beyond checking boxes. They don’t automate tasks in isolation; they understand context, negotiate within set parameters, assign ownership and coordinate the flow of work between AI and humans.
Designing for complexity
I think the biggest misstep companies are making with AI right now is treating it like a shortcut. Leasing isn’t a low-stakes, linear process; it’s emotional, financial and often urgent.
That complexity is baked into the nature of multifamily operations. The companies that find success in the long term could be the ones that build systems to handle the intricacies. That means using AI not to replace teams, but to make teams sharper.
Not everyone agrees with that approach today. But if the industry’s history with centralization taught us anything, it’s this: Those playing the long game—built for how the real world actually works—are often the ones that win.
