Traditionally, as a field, student housing depends on repeating, partially automatable processes and is full of information that can be hard for humans to form into patterns.
By Adam Dowrie, Associate Director of Organization and Planning, California State University, Sacramento
Housing teams are always working with historical trends from many data streams to make our predictions about occupancy, roommate matches, and risk. At the same time, we communicate manually and perform manual updates about deadlines, checklists, waitlists, billing, contracts and more.
How much will demand spike this year once financial aid packages are released? What did Student ‘X’ think of their room, and how has their academic career unfolded since? Do we have enough staff for Winter Break? Is one of our housing types affecting first-year retention? These are the questions that occupy us. Artificial intelligence (AI), with its genius for pattern matching and streamlining communications, seems an ideal tool to help answer these questions and remove the burden of manual processes from housing professionals.
However, at a recent panel discussion on AI in housing operations, what stood out was that many institutions have not integrated AI tools into housing workflows. Is this a consequence of the fears in higher ed about the effect of AI on academic growth? Is it the force of habit? Perhaps both. And while I cannot speak to the academic side of the equation, the housing team at Sacramento State has seen several distinct benefits already from using the AI features of our housing platform StarRez. If housing professionals find equivalent capabilities for their operations, the value of those tools will likely be immediate and significant.
AI’s impact tends to be most visible in some of the biggest pressure points of our jobs: communication volume, database navigation, and the constant need to make faster decisions with better information. Housing staff must still be there to support students and build community, obviously. But AI does have a clear ability to increase our efficiency and reveal actionable patterns within our data sets. It all adds up to better student service and better work.
Improved communication
One of the simplest but most effective tools leaders should explore is AI writing features. For higher adoption, it may be wise to find a solution that’s embedded within an existing platform, so that there’s no need for staff to move outside it as they’re going through their workflows.
Such features will usually function much like ChatGPT or other LLMs, allowing team members to prompt a chatbot with a few bullet points and generate a multi-paragraph email that’s clear, professional, and easy to understand. Communication is constant and frequently high-stakes in our field. So a single process change that can reduce typos, improve clarity, and save time on revisions would tend to elevate any housing team.
AI student summaries
Another form of AI-assisted time savings lies in database navigation. Housing systems are powerful, and the downside of that power is their complexity. When staff need to confirm a student’s booking details, roommate information, or status, that often requires navigating across multiple fields and modules.
Through a platform’s ‘Ask AI’ features, this information can be summarized immediately when viewing a student profile. So, instead of manually searching for multiple data points, staff can quickly access the information they need, which cuts time on a process they have to go through maybe dozens of times a day.
AI analytics: smarter decisions over time
Since December 2025, we’ve been beta testing AI-powered dashboards in our platform and collaborating closely with the developers to build tools that reflect our operational realities. The process has been rewarding, and the back-and-forth has offered a deeper understanding of what information is important to us, and why. Most of the conversations we have in our weekly meeting with our vendor revolve around which data fields matter, how custom fields connect, and what trends are essential for our long-term decision-making.
I do think housing teams will encounter more of a learning curve with AI analytics tools compared to traditional reporting methods. But the upside is significant. AI-powered dashboards can enable us to analyze historical application and booking patterns, evaluate transaction trends, and examine data sets in ways that were previously more manual or fragmented.
Over time, this intelligence can shape major institutional decisions, such as where to allocate staffing resources, how to adjust financial priorities, whether to expand housing capacity, and what trends are liable to morph into problems. The analytics element is where AI shows promise to move beyond rote efficiency and into strategy.
Starting small and smart
As I said, many introductory AI features, e.g., writing assistance and query tools, are highly accessible, especially for teams already comfortable with platforms like ChatGPT. So housing leaders don’t have to overhaul their operations to benefit from AI. They can begin by reducing drafting time or improving data access. Those early wins quickly build momentum.
We can expect AI’s role in student housing to grow over time, so getting started now would likely make for less jarring transitions later. We may see more student-facing applications within housing portals, offering customized experiences based on past behavior or roommate trends.
On the staff side, AI could identify recurring annual workflows and proactively suggest actions, reducing repetitive administrative tasks. We can imagine a system that looks across applications to recognize patterns – ‘You typically run this process at this time of year’ – and prompts you to execute it with a single click.
AI enables our teams to focus on higher-order thinking. More importantly, it strengthens our ability to serve students. So why wait?
