Generative AI has outgrown its “buzzword” status—it’s already a boon for various financial, operational, and administrative tasks. Some estimates suggest it can boost individual productivity by at least 40%, and 91% of business leaders recently surveyed believe it can benefit the entire organization.
This is a phenomenal feat at the basic human level, considering it’s still a nascent technology with few initial “generations.” We’re still in the early innings.
Across real estate—and more specifically, property management—it’s supercharging UX and customer service: improving how users interact and converse with their technology (and yielding incredible time savings—around 12.5 hours per week, according to AppFolio data). With this AI, users are no longer limited to navigating a tricky interface to get work done. Instead, it can learn key workflows, generate correspondence (emails and text messages), and leverage machine learning and wider business data to automate rote tasks. Recent qualitative research from AppFolio also shows that AI users in this space rely heavily on technology to communicate with their residents, including as a translation tool to help break language barriers.
AI emboldens real estate teams to do more—turning users into builders who can fine-tune inputs and gather insights instantly. Ultimately, it acts as a unifier in a way that other technologies simply cannot replicate.
Here, I’ll explore what other vertical markets can learn from the innovation and adoption of embedded GenAI in real estate.
Improving customer service, compliance
Since embedded GenAI platforms rely on natural language inputs, they’re incredibly user-friendly out of the gate. Tech-savvy users and those who are less tech-savvy can all navigate these platforms to reap unique benefits.
Unlike conventional software platform interfaces, GenAI’s conversational features foster engagement and comprehension. With interactions that rival human dialogue, users can quickly prompt their embedded AI to analyze troves of data that might take human teams days, even weeks. While estimates suggest that around 40% of working hours could be augmented or automated by GenAI, I believe that’s a fairly conservative number. Ultimately, for property management teams—or any teams for that matter—these savings unlock more time for strategic thinking or to more directly address customer satisfaction.
For instance, this AI—especially when embedded into industry-specific software like a property management system—can analyze large volumes of text to ensure compliance with local regulations. It can also automate the creation of (and ensure pinpoint accuracy of) legal documents and verify that all communications and operations adhere to relevant laws.
It can even drastically uplevel customer service: providing personalized and instant responses to resident inquiries, automating tasks like maintenance requests, and even absorbing portions of after-hours support (say, for scheduling purposes).
Across other verticals, outputs from embedded GenAI will directly drive innovation in fields such as robotics, while the capital increase spent on everything from research to new chips will have a halo effect for almost every sector in our economy.
Workflow automation: A closer look
While I’ve mentioned that embedded GenAI can uplevel customer service, it’s worth noting how helpful it can be, particularly with incredibly time-consuming, often tedious, tasks. So, let’s explore a communications use case: In property management, it’s up to operators to keep their residents informed about planned maintenance, payment preferences, or upcoming renewal dates.
That means it falls to staff to cue up messages to reach residents individually about, say, utility work happening on the grounds. Teams can prompt their embedded GenAI tool, which taps into business data/functions, to handle the drafting, identify the proper recipients, and automate the notifications moving forward.
Outside of housing, I foresee a space like healthcare experiencing similar benefits. In fact, providers—along with insurers—can use GenAI to synthesize and recommend tailored risk considerations for patients based on their medical history and existing medical literature. The AI provides an opportunity to cut administrative burdens and costs and becomes an incredibly strong resource that teams can use for tailored, impactful data.
No matter its use case, AI’s dynamic nature changes the way teams interact with their software, compared to more walled-off solutions, all without straining staff.
Turning users into builders
Business decisionmakers have increasingly just added to existing technology stacks for quick (but potentially complex or buggy) access to new features. They’ve also relied on existing staff to do more or keep pace. But, neither scenario addresses longer-term needs. This AI innovation, however, has proven that greater efficiency may not be out of reach.
Still, simply layering GenAI atop a fragmented data foundation will also lead to inaccuracies and complexity. To benefit from its efficiencies, teams should consolidate workflows using an integrated platform, which will equip an AI tool with the data access it needs to operate.
AI’s minimal learning curve means that it no longer takes years of IT training to interact with technology, nor do users need to wrangle data across an enormous tech infrastructure to do something as simple as acquiring a vendor quote for a sink repair. Instead, AI users are “builders” with a sturdy data “foundation” and customizable features at their fingertips—all of which help slash burdensome tasks.
Undeniable advantages
The upside here is undeniable: Embedded GenAI portends a future where users can ease and improve their workloads and productivity. In housing, its benefits trickle down to residents and can yield greater satisfaction and potentially more renewals or referrals. GenAI’s impact will be felt across many other industries for similar reasons.
Again, this is just the beginning. We’ll witness ongoing product refinement and improvement, with more deep learning and natural language processing breakthroughs. The immense potential here excites me for the future, though I am equally impressed by what AI has already accomplished. With it, SaaS tools have more agency to assist business users, who can expect unprecedented efficiency, customization, and insight.
Matthew Baird is Senior Vice President, Engineering, at AppFolio, responsible for empowering the company’s engineering team to execute product commitments with exceedingly high-quality, agility, and speed. Prior to his time at AppFolio, Baird was the Founder, Chief Technology Officer, and Vice President of Engineering for AtScale, Inc. He’s an accomplished thought leader who is a regular speaker at major big data events.
